Profiling microRNA from Brain by Microarray in a Transgenic Mouse Model of Alzheimer's Disease

MicroRNAs (miRNAs) are small noncoding RNAs, which regulate numerous cell functions by targeting mRNA for cleavage or translational repression, and have been found to play an important role in Alzheimer's disease (AD). Our study aimed to identify differentially expressed miRNAs in AD brain as a reference of potential therapeutic miRNAs or biomarkers for this disease. We used amyloid precursor protein (APP) and presenilin 1 (PS1) double transgenic mice and age-matched wild-type (WT) littermates to determine the expression of miRNAs in the brain. MiRNAs were profiled by microarray, and differentially expressed miRNAs underwent target prediction and enrichment analysis. Microarray analysis revealed 56 differentially expressed miRNAs in AD mouse brain, which involved 39 miRNAs that were significantly upregulated and 19 that were downregulated at different ages. Among those miRNAs, a total of 11 miRNAs, including miR-342-3p, miR-342-5p, miR-376c-3p, and miR-301b-3p, were not only conserved in human but also predicted to have targets and signaling pathways closely related to the pathology of AD. In conclusion, in this study, differentially expressed miRNAs were identified in AD brain and proposed as biomarkers, which may have the potential to indicate AD progression. Despite being preliminary, these results may aid in investigating pathological hallmarks and identify effective therapeutic targets.


Introduction
Alzheimer's disease (AD), the main cause of dementia, is a progressive neurodegenerative disease that is characterized by extracellular senile plagues, intracellular neurofibrillary tangles (NFTs), and neuron loss [1,2]. Accumulation of the amyloid beta peptide (A ) within the brain, which is cleaved by -secretase (BACE1) and -secretase from amyloid precursor protein (APP), is considered a major cause of AD [3]. Aggregation of A can disturb neurotransmission and cause synaptic impairment [4]. A oligomers trigger the immune system, leading to immune responses, such as the release of chemokines, proinflammatory cytokines, and complement factors. These will increase neuronal death and neuronal synapse loss [5][6][7]; therefore, the burden of A in AD brain could be an indication of cognitive decline. Several factors have been confirmed to be connected with AD, such as mitochondrial damage, synaptic loss, A accumulation, tau phosphorylation, neuroinflammation, and gene mutations [8][9][10][11][12]. Currently, no effective drugs or treatments exist that can prevent the progress of the disease, and most candidate drugs that targeted A and tau failed during clinical or preclinical research. In addition, there are no noninvasive biomarkers of AD. Thus, microRNAs (miRNAs), which can be easily 2 BioMed Research International detected and are widely distributed, have become interesting in AD biomarker research.
MiRNAs are small single-stranded noncoding RNAs, consisting of about 20 nucleotides in length that are widely distributed in cells, blood, serum, and plasma, and regulate a myriad of cell functions [13][14][15]. Primary miRNA in the nucleus is processed into precursor-miRNA by the RNase III enzyme Drosha and transported to the cytoplasm where it is further processed by an enzyme Dicer to become mature miRNA [12,16]. Binding to the 3 untranslated region (3 -UTR) of target mRNA will result in the formation of a silencing complex, in which miRNA regulates the translation process and inhibits the protein from being generated [17]. About 70% of all reported miRNAs can be found in the brains, and several of these miRNAs have been proven to be tightly connected with AD through binding to the -site amyloid precursor protein cleaving enzyme BACE1 or other mRNAs in the amyloidogenic pathways to decrease overproduction of A [18,19].
MiRNAs are reported to regulate cell functions, including A processing, tau translating, apoptosis, inflammation, and cell cycle phase. Aberrant expression of miRNAs can affect the development and progression of AD [1,19,20]. miR-29a, miR-29c, and miR-124 have been shown to interact with the 3 -UTR of BACE1 mRNA, and downregulation of miR-29a, miR-29c, and miR-124 in AD leads to overproduction of A [21][22][23]. MiR-34a has also been reported to directly bind to tau mRNA and thereby changes the expression of miR-34a in AD, which is important in the expression of tau protein [24].
MiRNA microassay provides an efficient tool for highthroughput and quantitative detection with excellent reproducibility. We detected 3100 capture probes, covering all human, mouse, and rat miRNAs annotated in miRBase 18.0, as well as all viral miRNAs related to these species in APP and presenilin 1 (PS1) double transgenic mouse brain compared with age-matched wild-type (WT) controls, and analyzed any changes to identify aberrant expression of miRNAs.

Animal and Tissue Preparation.
Heterozygous APPswe/ PS1Δ9 transgenic founder mice were purchased from Jackson Laboratory (Bar Harbor, ME, USA). Age-matched WT littermates were used as controls. All animals received care according to the Guide for the Care and Use of Laboratory Animals. Mice were maintained at a constant ambient temperature and a 12 h light/dark cycle, with free access to food and water. Mice were divided into the following groups: 1-month-old, 3-month-old, 6-month-old, and 9-month-old APP/PS1 mice (abbreviated to AD mice) and age-matched WT control mice. Each group contained 6 mice (three males and three females per group). From 3 mice, brains were collected and immediately frozen into liquid nitrogen. The remaining mice were used for additional gene and protein measurements. Both the initial division of mice into different groups and the subsequent selection of mice for gene and protein experiments were done randomly.

RNA Isolation.
Total RNA was isolated using TRIzol (Invitrogen, Carlsbad, CA, USA) and purified using an RNeasy Mini Kit (QIAGEN, Dusseldorf, Germany) according to the manufacturer's instructions. RNA quality and quantity were measured using a Nanodrop spectrophotometer (ND-1000, Nanodrop Technologies, Wilmington, Delaware) and RNA integrity was determined by gel electrophoresis.
2.3. miRNA Labeling and Array Hybridization. RNA labeling and array hybridization were carried out according to Exiqon's manual (Exiqon, Vedbaek, Denmark). After preparation of the quality control, the miRCURY6 Hy36/Hy56 Power labeling kit (Exiqon, Vedbaek, Denmark) was used for miRNA labeling according to the manufacturer's guidelines. A total of 1.0 L total RNA and 2.0 L water were combined with 1.0 L calf-intestinal alkaline phosphatase (CIP) (Exiqon, Vedbaek, Denmark) with the CIP buffer and incubated for 30 min at 37 ∘ C, followed by incubation for 5 min at 95 ∘ C. Then, a mixture, containing 3.0 L labeling buffer, 1.5 L fluorescent labels (Hy3), 2.0 L DMSO, and 2.0 L labeling enzyme, was added and incubated for 1 h at 16 ∘ C, followed by incubation for 15 min at 65 ∘ C to terminate the labeling. After stopping the labeling procedure, the Hy3labeled samples were hybridized onto the miRCURY LNA array (v.18.0) (Exiqon, Vedbaek, Denmark) according to array manual. The Hy3-labeled samples and 25 L hybridization buffer were denatured for 2 min at 95 ∘ C and hybridized to the microarray for 16 h-20 h at 56 ∘ C in a 12-Bay Hybridization System (Hybridization System, Nimblegen Systems, Inc., Madison, WI, USA) following several times of washing using a wash buffer kit (Exiqon, Vedbaek, Denmark). Next, the slides were scanned by the Axon GenePix 4000B microarray scanner (Axon Instruments, Foster City, CA, USA).

Data Analysis.
Scanned images were imported into GenePix Pro 6.0 software (Axon Instruments, Union City, CA, USA) for grid alignment and data analysis. Replicated miRNAs were averaged and miRNAs with an intensity ≥ 30 in all samples were chosen for calculating the normalization factor using median normalization. Significantly differentially expressed miRNAs between two groups were identified by fold change and value, and fold change ≥ 2.0 and value ≤ 0.05 were considered significant. Differentially expressed miRNAs between two samples were filtered through fold change. Finally, hierarchical clustering was performed to identify distinguishable miRNA expression profiling among samples.

miRNA Target Prediction.
For miRNA target prediction, the target prediction programs, involving TargetScan, miRDB, and miRanda, were used. The search was performed on the 3 -UTR regions of target mRNAs with a value of 0.05 defining the probability distribution of random matches set in the software with a minimum miRNA seed length of 7 [25,26].

Functional Classification and Pathway Analysis.
To further investigate the roles of miRNAs in the process of AD, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were carried out using DAVID (https://david.ncifcrf.gov/). Targets were synthesized and intersections of miRDB, TargetScan, and miRanda databases of the 11 most significant changed miRNAs were taken, and then GO and KEGG analysis were performed.

Significant Expression Changes of miRNAs in APP/PS1
Mice. To determine the miRNA levels in the brain, we evaluated the miRNAs in 1-month-old, 3-month-old, 6-monthold, and 9-month-old APP/PS1 mice and age-matched WT controls. APP/PS1 mice were analyzed by computational approaches and compared with age-matched WT controls ( Figure 1). In contrast with the WT control group, 4 miRNAs were significantly upregulated and 4 were downregulated in the 1-month-old APP/PS1 mouse brain group. In addition, 11 miRNAs were upregulated and 3 downregulated in 3-monthold APP/PS1 mouse brain, 24 miRNAs were upregulated and 6 downregulated in 6-month-old APP/PS1 mouse brain, and 5 miRNAs were upregulated and 9 downregulated in 9month-old APP/PS1 mouse brain (Table 1). Among these differentially expressed miRNAs, miR-342-3p was consistently upregulated in 1-month-old, 6-month-old, and 9-month-old APP/PS1 mice, which suggests that miR-342-3p may play a role in the process of AD. Moreover, miR-342-5p was elevated in both 1-month-old and 3-month-old APP/PS1 mouse groups, suggesting that miR-342-5p may play a role in early stages of AD. Furthermore, levels of miR-2136, let-7f-5p, miR-431-5p, and miR-491-3p were higher in both 3-monthold and 6-month-old APP/PS1 mouse brain, indicating their potential involvement in the progression of AD ( Figure 2).
GO and KEGG pathway analyses were performed to elucidate the biological function of the 11 selected miRNAs. GO terms covered 3 domains: molecular function, biological process, and cellular component. Figure 4 shows the top 12 significantly enriched GO terms at each domain. Moreover, in Figure 5, the 10 most significantly enriched pathways are shown that were mapped with KEGG pathway analysis: the Rap1 signaling pathway, MAPK signaling pathway, signaling pathways regulating pluripotency of stem cells, endocytosis, TGF-beta signaling pathway, pathways in cancer, melanogenesis, choline metabolism in cancer, FoxO signaling pathway, and the Wnt signaling pathway.

Prediction Targets of miRNAs with Different Expressions
in AD Brain. To further elucidate the function of miRNAs and the roles of their target genes, we consulted the miR-Base (http://www.mirbase.org/) and TargetScan (http://www .targetscan.org/) and summarized the miRNAs and their targets that tightly correspond to AD ( Table 2).

Discussion
AD is a common form of dementia, with a fast growing morbidity with the speeding aging of the population all over the word. AD patients suffer not only from cognitive dysfunction, such as impairment of memory, visuospatial, language, and executive functions, but also from other psychological symptoms including depression [34]. Therefore, AD patients will gradually lose the ability to take care of  Figure 3: Two-way hierarchical clustering of miRNAs and brain samples. Each row represents a miRNA and each column represents a brain sample. Wide-type control mice are indicated as WT mice. Different age-stage AD mice are shown as 1-month-old (a), 3-month-old (b), 6-month-old (c), and 9-month-old (d) APP/PS1 mice. Clustering tree is shown on the left, and the sample clustering tree appears at the top. The color scale shown at the top illustrates the relative expression level of a miRNA in a certain age group. Red color represents a high relative expression level and a green color represents a low relative expression level.
themselves and suffer from both mental and psychological distress [35]. However, there are neither effective therapeutic strategies nor early diagnostic biomarkers for the treatment of AD. In addition, in most patients, AD is hardly recognized in the early stages of the disease, and therefore the therapeutic opportunity is often missed because of the delay in diagnosis. miRNAs that regulate posttranscription of proteins have been reported to specifically change in the process of AD and can be easily detected by microarray analysis or RT-PCR.
Therefore, miRNAs could potentially serve as noninvasive biomarkers and therapeutic targets of AD [36].
To deepen our understanding of these differentially expressed miRNAs, we performed two types of enrichment analyses: GO and KEGG. In this study, the analyses are focused upon the differential expression characteristics of those miRNAs involved in the pathological pathways of AD, to better understand the underlying mechanisms of miRNA and predicted target transductions in this disease. These bioinformatics analyses enabled us to identify many putative signaling pathways in AD pathology in which the differentially expressed miRNAs are involved. These pathological pathways cover the nerve system development process, synapse component, and MAPK signaling pathways. Longterm potentiation (LTP) with 16 counts of total predicted targets and axon guidance with 22 counts also showed significant enrichment, which is not reflected in Figure 4. Combined with the target prediction analysis, we suggest that the 11 selected miRNAs may play a role in AD by regulating the activity in neuronal molecular events that are related to amyloidogenic processes, tau aggregation, and neuron injury.
Other than performing experiments on blood and cerebrospinal fluid (CSF), we specifically explored the variation of miRNAs in brain, which directly reflected the changes of miRNA in AD brain. Moreover, we explored four age groups, including early stage, medium stage, and late stage of AD, and analyzed the variation of miRNA expression in these four groups. Although it is challenging to compare our results to the results of other studies in which different tissues and species were used, several miRNAs, including let-7f-5p, miR-342-5p, and miR-98-5p, showed a similar trend in human blood and CSF [48,49]. In addition, some differences were found between those results; for example, the level of miR-342-3p was downregulated in AD patient plasma samples, while in our study it was significantly elevated in APP/PS1 mouse brain of 1-month-old, 6-month-old, and 9-month-old mice [46]. miRNA microassay achieved high-throughput screening of miRNAs, and 11 significantly changed miRNAs conserved between human and mouse brain were found in AD brain. Moreover, the alterations of these miRNAs indicate a different development process of AD as indicated by miRNA expression levels at different ages. Among these miRNAs, miR-342-3p was upregulated from 1-month-old to 9-month-old AD mice, suggesting that it may play a critical role in the entire AD process. miR-342-5p was upregulated in brain tissue of 1-month-old and 6-month-old mice, indicating that it is effective at early onset and therefore may act as a specific early biomarker of AD. miR-2136, let-7f-5p, miR-431-5p, and miR-491-3p were upregulated in both 3-month-old and 6-monthold AD mice, implying their role in the development of AD. Combined with subsequent target prediction and enrichment analysis, the 11 miRNAs were predicted to be closely related to the molecular events of the amyloidogenic process, tau phosphorylation, and MAPK and TGF-signaling pathways. Although these miRNAs need to be confirmed due to clearing of false-positive reactions, these findings illustrate that intervention of these miRNAs may affect the signaling transductions from transcriptional to posttranscriptional events and play a role in different processes of AD. In the near future, clinical samples, such as plasma and CSF, are required to detect miRNA levels, targets, and signaling pathways predicted in our study. Thus, our study is a link between the microarray results translating from an animal model to patients who are diagnosed with AD and have developed cognitive deficits with pathological alterations of extracellular A deposition and intracellular NFTs.

Conclusions
In summary, this study was also the first miRNA microarray using different-stage AD brain tissue, and through bioinformatics analysis, eleven miRNAs were shown to have plentiful altered expressions and to be conserved in evolution with possible target correlation to AD and suggested to act as "markers" to distinguish the pathological changes of this disease. Our findings provided a clue for understanding the pathological hallmarks and looking for effective therapeutic targets in the miRNA-mRNA level of AD.

Conflicts of Interest
The authors declare that they have no conflicts of interest regarding the publication of this paper.