MicroRNA and mRNA profiling of cerebral cortex in a transgenic mouse model of Alzheimer’s disease by RNA sequencing

Abstract

In a previous study, we found that long non-coding genes in Alzheimer’s disease (AD) are a result of endogenous gene disorders caused by the recruitment of microRNA (miRNA) and mRNA, and that miR-200a-3p and other representative miRNAs can mediate cognitive impairment and thus serve as new biomarkers for AD. In this study, we investigated the abnormal expression of miRNA and mRNA and the pathogenesis of AD at the epigenetic level. To this aim, we performed RNA sequencing and an integrative analysis of the cerebral cortex of the widely used amyloid precursor protein and presenilin-1 double transgenic mouse model of AD. Overall, 129 mRNAs and 68 miRNAs were aberrantly expressed. Among these, eight down-regulated miRNAs and seven up-regulated miRNAs appeared as promising noninvasive biomarkers and therapeutic targets. The main enriched signaling pathways involved mitogen-activated kinase protein, phosphatidylinositol 3-kinase-protein kinase B, mechanistic target of rapamycin kinase, forkhead box O, and autophagy. An miRNA-mRNA network between dysregulated miRNAs and corresponding target genes connected with AD progression was also constructed. These miRNAs and mRNAs are potential biomarkers and therapeutic targets for new treatment strategies, early diagnosis, and prevention of AD. The present results provide a novel perspective on the role of miRNAs and mRNAs in AD. This study was approved by the Experimental Animal Care and Use Committee of Institute of Medicinal Biotechnology of Beijing, China (approval No. IMB-201909-D6) on September 6, 2019.

Chinese Library Classification No. R446.1; R741.04; Q344+.13

Introduction

Alzheimer’s disease (AD) is the leading cause of dementia, and characterized by cognitive impairment and memory loss (Alexiou et al., 2019; Fiorini et al., 2020; Li et al., 2020). The pathological AD hallmarks are the presence of intracellular neurofibrillary tangles, extracellular senile plaques, and neuronal loss (Xu et al., 2019; Mamun et al., 2020). Patients with familial AD caused by the mutation of specific genes, such as amyloid-beta peptide precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2), and microtubule-associated protein-τ (MAPT), are less than 5% (Cacace et al., 2016). However, the morbidity of sporadic AD, characterized by the involvement of multiple molecular mechanisms, is largely enigmatic and greater than 95% (Dorszewska et al., 2016). The drugs currently available to treat AD do not reduce neuronal deterioration or death. Most of the candidate drugs targeting amyloid-beta peptides (Aβ), tau tangles, neurotransmitters, and against neuroinflammation have proved unsuccessful in clinical trials (Bae et al., 2019; Cummings et al., 2019). Consequently, the identification of reliable biomarkers that may contribute to early diagnosis and timely therapeutic intervention is urgently needed.

MicroRNAs (miRNAs) are single-stranded non-coding RNA of 19–25 nucleotides in length. The biogenesis of miRNAs can be canonical or non-canonical, which involve the endoribonuclease Dicer and the Argonaute protein family, respectively. Both pathways ultimately lead to a functional miRISC complex binding with the target mRNAs to inhibit their translation (Matsuyama and Suzuki, 2019; Xiao and MacRae, 2019). Gene expression is controlled by binding the 3′-untranslated region (UTR) with bases in the mRNAs; this reduces transcription efficiency and/or decreases mRNA expression, which in turn results in a protein production decrease of more than 80% (Guo et al., 2010). Many miRNAs, including miR-346, miR-101, miR-153, miR-15b, miR-339-5p, and miR-200a-3p, contribute to Aβ production and clearance, tau phosphorylation, synaptic dysfunction, and autophagy by suppressing the translation or inducing the degradation of their target mRNAs involved in AD pathogenesis (Martinez and Peplow, 2019; Wang et al., 2019a; Hou et al., 2020; Rodriguez-Ortiz et al., 2020). However, the physiological and pathological function of the aberrantly expressed miRNAs and mRNAs involved in AD is not yet sufficiently clear.

In this study, RNA sequencing was achieved to identify the profile of miRNA and mRNA expression involved in AD progression in APP/PS1 double transgenic mice of different ages compared with age-matched wild type (WT) control mice. Subsequently, the potential target genes of the significantly dysregulated miRNAs, their biological function, and pathway enrichment were assessed. Furthermore, the miRNA-mRNA network was constructed to identify novel diagnostic AD biomarkers and therapeutic targets.

Materials and Methods

Animal treatment and sample preparation

All experiments were designed and reported according to the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines. The APP/PS1 transgenic mice and age-matched WT littermates were purchased from the Zhishan Healthcare Research Institute (Beijing, China; license No. SCXK2019-0008). The Experimental Animal Care and Use Committee of Institute of Medicinal Biotechnology of Beijing, China (approval No. IMB-201909-D6) approved the animal experiments (approval No. IMB-201909-D6) on September 6, 2019. Twelve APP/PS1 mice were grouped by age (1 month, 3 months, 6 months, and 9 months), and the same grouping was applied to the twelve corresponding WT control mice. Each age group included three mice (two female and one male). Before performing the RNA sequencing, APP/PS1 mice were subjected to the Morris water maze test (Liu et al., 2018), in which their learning and memory dysfunction was evaluated using a water navigation task and exploration of the space, and compared with that of the WT control mice (Additional Figure 1 ^{(760KB, tif)} ). Mice were then sacrificed by cervical dislocation and the cerebral cortex was stored in liquid nitrogen.

RNA extraction

We isolated the total RNA from the cerebral cortices using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. The total RNA concentration was assessed using a Spark 20M multimode microplate reader (Tecan Group Ltd., Mannedorf, Switzerland). The integrity of RNA was evaluated using 1% agarose gel electrophoresis. The RNA concentration and electrophoresis are shown in Additional Table 1 and Additional Figure 2 ^{(203.3KB, tif)} .

Additional Table 1.

Concentration of total RNA by Spark 20M multimode microplate reader

Sample	RNAConcentration (ng/μL)	Volume (μL)
1-month-old WT control mice-1	1620	25
1-month-old WT control mice-2	1410	25
1-month-old WT control mice-3	1392	25
3-month-old WT control mice-1	1020	25
3-month-old WT control mice-2	864	25
3-month-old WT control mice-3	834	25
6-month-old WT control mice-1	1098	25
6-month-old WT control mice-2	1164	25
6-month-old WT control mice-3	1194	25
9-month-old WT control mice-1	540	25
9-month-old WT control mice-2	708	25
9-month-old WT control mice-3	894	25
1-month-oldAPP/PS1 mice-1	738	25
1-month-oldAPP/PS1 mice-2	1308	25
1-month-oldAPP/PS1 mice-3	888	25
3-month-oldAPP/PS1 mice-1	660	25
3-month-oldAPP/PS1 mice-2	888	25
3-month-oldAPP/PS1 mice-3	474	25
6-month-oldAPP/PS1 mice-1	1518	25
6-month-oldAPP/PS1 mice-2	1104	25
6-month-oldAPP/PS1 mice-3	1446	25
9-month-oldAPP/PS1 mice-1	1434	25
9-month-oldAPP/PS1 mice-2	1752	25
9-month-oldAPP/PS1 mice-3	519	25

APP: Amyloid-beta peptide precursor protein; PS1: presenilin 1; WT: wild type.

mRNA library construction and sequencing

One microgram of total RNA was used for complementary DNA (cDNA) library preparation. Subsequently, 150–200 bp cDNA fragments were enriched and purified using the AMPure XP system (Beckman Coulter, Beverly, MA, USA), USER Enzyme (NEB, Ipswich, MA, USA), and adaptor-ligated cDNA at 37°C for 15 minutes and subsequently at 95°C for 5 minutes. Then, polymerase chain reaction was performed using the Phusion High-Fidelity DNA polymerase, 10 μM Universal polymerase chain reaction primers, and 10 μM Index (X) Primer. The Agilent Bioanalyzer 2100 system was employed to assess the purification and library quality. The cBot Cluster Generation System using the TruSeq PE Cluster Kit v3-cBot-HS (Illumina, San Diego, CA, USA) was used to yield the cluster, and the Illumina HiSeq2000 platform by Novogene Bioinformatics Technology Co., Ltd. (Beijing, China) was used to evaluate the sequences. Finally, paired-end reads were produced and the clean reads were obtained by removing the adapter (forward: 5′-AGA TCG GAA GAG CAC ACG TCT GAA C-3′; reverse: 5′-AGA TCG GAA GAG CGT CGT GTA GGG A-3′) and the Poly-N and low-quality reads (Q < 20) from the raw reads. The read counts, Q10, Q20, Q30, GC base ratio, and average read length of the clean reads were also calculated.

miRNA library construction and sequencing

One microgram of total RNA was used for the construction of the cDNA library using the TruSeq Small RNA Sample Prep Kits (Illumina) according to the manufacturer’s protocol. Next, the Illumina HiSeq 2500 at the LC-BIO (Hangzhou, China) was employed for the single-end sequencing according to the manufacturer’s recommendations. Finally, the clean reads were obtained by Cutadapt V1.14 to remove the 3′-adapter (5′-TGG AAT TCT CGG GTG CCA AGG AAC TC-3′) that controls the length between 17 and 35 bp. Trimmomatic V0.36 was used to delete the low-quality reads (Q < 20), and Blast V2.6.0 was used to remove the RNA families (ribosomal RNA, transfer RNA, small-nuclear RNA, and small-nucleolar RNA) and repeats using comparison conditions that were defined as a gap-open equal to zero, e-value less than 0.01, and mismatch less than 1. To identify novel miRNAs, miRbase V21.0 was used to screen for known miRNA to make a prediction. The unmapped sequences were further searched using miRDeep2 V2.0.0.8, and the mouse reference genome (http://asia.ensembl.org/index.html) was used to distinguish novel miRNAs and predict their secondary structure (Friedländer et al., 2012).

Data analysis

Raw data were analyzed using the Empirical Analysis of Digital Gene Expression Data (edgeR) in R. The frequency of the miRNA counts was normalized as reads per million to analyze the expression pattern of miRNAs between APP/PS1 mice and WT control mice. The expression difference was evaluated using Student’s t-test. Both a fold change greater than 2.0 and a P-value less than 0.05 were considered as standards to distinguish aberrant miRNAs and mRNAs that were significantly dysregulated between the APP/PS1 mice and WT controls at different age groups. The pheatmap program was also used to visualize the hierarchical clustering of significantly different miRNA expression between APP/PS1 mice and WT mice.

miRNAs target prediction

The known miRNAs were assessed by percent identity between human and mouse species, and the novel miRNAs were evaluated by the miRDeep2 score. In this study, we selected the known miRNAs of a percent identity greater than or equal to 80% and novel miRNAs with a miRDeep2 score greater than or equal to 4 for further analysis. Targetscan, Tarbase, and miRanda were used to search for the potential target genes (Riffo-Campos et al., 2016). The parameters of miRanda were set as single-residue-pair match scores greater than or equal to 150 and ΔG less than or equal to –30 kcal/mol.

Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis

Gene ontology (GO) analysis was performed to explain the molecular mechanism of AD through the molecular function, cellular component, and biological process domains. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to define the signaling pathway that was associated with the target genes of aberrantly expressed miRNA. The mRNAs-GO-network and mRNAs-KEGG-network were constructed according to the information retrieved from these preliminary analyses. Cytoscape was used to visualize the interaction map between miRNAs and mRNAs. An integrated regulatory diagram was also constructed. Additional Table 2 shows the detailed information regarding the software used in this study.

Additional Table 2.

Website information regarding the software used in this study

Software	Version	Site
Trimmomatic	V0.36	http://www.usadellab.org/cms/?page=trimmomatic
Blast	V2.6.0	https://blast.ncbi.nlm.nih.gov/Blast.cgi
miRDeep2	V2.0.0.8	https://www.mdc-berlin.de/8551903/en/
R	V3.4.1	https://www.r-project.org
edgeR	V3.18.1	https://www.bioconductor.org
miRanda	V3.3a	http://www.microrna.org/
miRbase	V21	http://www.mirbase.org/ftp.shtml
Targetscan	V7.2	http://www.targetscan.org/
Tarbase	V8	http://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=tarbasev8%2Findex/
pheatmap	V1.0.8	https://cran.r-project.org
Cutadapt	V1.14	http://code.google.com/p/cutadapt/
Gene Ontology	-	http://www.geneontology.org/
KEGG pathway	-	http://www.genome.jp/kegg/pathway.html
Cytoscape	V3.7.2	http://www.cytoscape.org/

Results

RNA sequencing and analysis for differentially expressed mRNAs in APP/PS1 mice

A total of 24 mRNA relevant cDNA libraries and sequencing were constructed to elucidate the pathogenesis of AD at the transcriptional level, based on a comparative analysis between the WT and APP/PS1 mouse cortex at 1 month, 3 months, 6 months, and 9 months of age. We acquired a mean of 47,675,430 total mRNA clean reads with a percentage of quality value greater than 20 (Q20) and a base ratio higher than 98.59% (Additional Tables 3 and 4). Subsequently, a more in-depth genome analysis showed that over 96.4% of the clean reads were mapped with the reference genome (Additional Table 5). A total of 51,732 genes expressed in the cerebral cortex of the APP/PS1 transgenic mice were discovered, and, among them, 129 were significantly dysregulated. They were divided into 78 up-regulated and 51 down-regulated genes in the APP/PS1 mouse cortex as compared with the WT control mice at different ages (Additional Table 6), which were potentially involved in AD. The top 10 genes with a significant fold-change in their expression were constructed and represented by upregulated laminin receptor 1 (Lamr1-ps1), S100 calcium-binding protein A8 (S100a8), S100 calcium-binding protein A9 (S100a9), cystatin F (Cst7), chemokine (C-C motif) ligand 3 (Ccl3), AC147560.1, and Gm22133, and down-regulated Gm27505, histone cluster 2- H2aa1 (Hist2h2aa1), and mitochondrially encoded transfer RNA serine 2 (mt-Ts2) in APP/PS1 mice compared with the WT control mice (Table 1).

Additional Table 3.

Quality assessment of mRNA sequencing reads including total read counts, total bases counts, average read length, N bases count, N bases ratio, GC bases count, and GC bases ratio

Sample	Total reads count	Total bases count (bp)	Average read length (bp)	N bases count (bp)	N bases ratio (%)	GC bases count (bp)	GC bases ratio (%)
1-month-old WT control mice-1	40433196	5.86E+09	144.85	43096	0.00	2.77E+09	47.32
1-month-old WT control mice-2	43209168	6.13E+09	141.9	45392	0.00	2.9E+09	47.23
1-month-old WT control mice-3	45541384	6.48E+09	142.29	48381	0.00	3.04E+09	46.89
3-month-old WT control mice-1	39512610	5.56E+09	140.83	39412	0.00	2.62E+09	47.08
3-month-old WT control mice-2	41423302	5.94E+09	143.41	44123	0.00	2.8E+09	47.08
3-month-old WT control mice-3	39635208	5.6E+09	141.18	40906	0.00	2.62E+09	46.76
6-month-old WT control mice-1	47357382	6.55E+09	138.34	49728	0.00	3.08E+09	47.02
6-month-old WT control mice-2	53443434	7.49E+09	140.19	55815	0.00	3.49E+09	46.54
6-month-old WT control mice-3	48789418	6.9E+09	141.47	51248	0.00	3.2E+09	46.42
9-month-old WT control mice-1	59615552	8.48E+09	142.19	62858	0.00	3.97E+09	46.85
9-month-old WT control mice-2	39391226	5.61E+09	142.45	41892	0.00	2.62E+09	46.63
9-month-old WT control mice-3	48826638	6.83E+09	139.81	50966	0.00	3.22E+09	47.18
1-month-oldAPP/PS1 mice-1	57970216	8.27E+09	142.63	61252	0.00	3.91E+09	47.27
1-month-oldAPP/PS1 mice-2	50057628	7.12E+09	142.3	53171	0.00	3.32E+09	46.68
1-month-oldAPP/PS1 mice-3	40833668	5.88E+09	144.08	43147	0.00	2.77E+09	47.03
3-month-oldAPP/PS1 mice-1	45900922	6.56E+09	142.86	48482	0.00	3.13E+09	47.72
3-month-oldAPP/PS1 mice-2	56139412	7.97E+09	141.96	60656	0.00	3.81E+09	47.79
3-month-oldAPP/PS1 mice-3	49399086	7.02E+09	142.12	51967	0.00	3.34E+09	47.51
6-month-oldAPP/PS1 mice-1	57033794	8.15E+09	142.88	59119	0.00	3.83E+09	46.99
6-month-oldAPP/PS1 mice-2	50321498	7.08E+09	140.67	52788	0.00	3.34E+09	47.15
6-month-oldAPP/PS1 mice-3	49672972	7.02E+09	141.35	52578	0.00	3.31E+09	47.12
9-month-oldAPP/PS1 mice-1	45057550	6.33E+09	140.59	46942	0.00	2.98E+09	47.08
9-month-oldAPP/PS1 mice-2	46885262	6.6E+09	140.76	50649	0.00	3.14E+09	47.56
9-month-oldAPP/PS1 mice-3	47760514	6.73E+09	141	50270	0.00	3.17E+09	47.01

APP: Amyloid-beta peptide precursor protein; mRNA: messenger RNA; PS1: presenilin 1; WT: wild type.

Additional Table 4.

Quality assessment of mRNA sequencing reads including Q10/Q20/Q30 bases count and Q10/Q20/Q30 bases ratio

Sample	Q10 Bases Count (bp)	Q10 Bases Ratio (%)	Q20 Bases Count (bp)	Q20 Bases Ratio (%)	Q30 Bases Count (bp)	Q30 Bases Ratio (%)
1-month-old WT control mice-1	5.86E+09	100.00	5.78E+09	98.67	5.57E+09	95.11
1-month-old WT control mice-2	6.13E+09	100.00	6.05E+09	98.75	5.85E+09	95.36
1-month-old WT control mice-3	6.48E+09	100.00	6.4E+09	98.81	6.19E+09	95.51
3-month-old WT control mice-1	5.56E+09	100.00	5.49E+09	98.59	5.28E+09	94.9
3-month-old WT control mice-2	5.94E+09	100.00	5.86E+09	98.60	5.64E+09	94.92
3-month-old WT control mice-3	5.6E+09	100.00	5.52E+09	98.69	5.32E+09	95.13
6-month-old WT control mice-1	6.55E+09	100.00	6.47E+09	98.77	6.25E+09	95.45
6-month-old WT control mice-2	7.49E+09	100.00	7.39E+09	98.67	7.13E+09	95.15
6-month-old WT control mice-3	6.9E+09	100.00	6.81E+09	98.71	6.57E+09	95.26
9-month-old WT control mice-1	8.48E+09	100.00	8.37E+09	98.77	8.09E+09	95.40
9-month-old WT control mice-2	5.61E+09	100.00	5.55E+09	98.84	5.37E+09	95.62
9-month-old WT control mice-3	6.83E+09	100.00	6.74E+09	98.79	6.52E+09	95.49
1-month-old APP/PS1 mice-1	8.27E+09	100.00	8.17E+09	98.81	7.9E+09	95.51
1-month-old APP/PS1 mice-2	7.12E+09	100.00	7.03E+09	98.72	6.79E+09	95.26
1-month-old APP/PS1 mice-3	5.88E+09	100.00	5.81E+09	98.78	5.61E+09	95.42
3-month-old APP/PS1 mice-1	6.56E+09	100.00	6.48E+09	98.77	6.26E+09	95.41
3-month-old APP/PS1 mice-2	7.97E+09	100.00	7.88E+09	98.87	7.63E+09	95.71
3-month-old APP/PS1 mice-3	7.02E+09	100.00	6.94E+09	98.84	6.71E+09	95.61
6-month-old APP/PS1 mice-1	8.15E+09	100.00	8.04E+09	98.66	7.75E+09	95.09
6-month-old APP/PS1 mice-2	7.08E+09	100.00	6.99E+09	98.80	6.76E+09	95.50
6-month-old APP/PS1 mice-3	7.02E+09	100.00	6.94E+09	98.82	6.71E+09	95.57
9-month-old APP/PS1 mice-1	6.33E+09	100.00	6.26E+09	98.81	6.05E+09	95.54
9-month-old APP/PS1 mice-2	6.6E+09	100.00	6.52E+09	98.76	6.3E+09	95.39
9-month-old APP/PS1 mice-3	6.73E+09	100.00	6.65E+09	98.72	6.41E+09	95.23

APP: Amyloid-beta peptide precursor protein; mRNA: messenger RNA; PS1: presenilin 1; WT: wild type.

Additional Table 5.

Summary of the genome mapping analysis in the mRNA sequencing, including total reads, total mapped, multiple mapped, and uniquely mapped

1-month-oldAPP/PS1 mice-1	57645074(100.00)	56478112(97.98)	5519333(9.57)	50958779(88.40)
1-month-oldAPP/PS1 mice-2	49838274(100.00)	48690532(97.70)	5341983(10.72)	43348549(86.98)
1-month-oldAPP/PS1 mice-3	40295096(100.00)	39525994(98.09)	4217821(10.47)	35308173(87.62)
3-month-oldAPP/PS1 mice-1	45610434(100.00)	44815837(98.26)	4333420(9.50)	40482417(88.76)
3-month-oldAPP/PS1 mice-2	55801684(100.00)	54941587(98.46)	5729908(10.27)	49211679(88.19)
3-month-oldAPP/PS1 mice-3	49198822(100.00)	48355747(98.29)	5016434(10.20)	43339313(88.09)
6-month-oldAPP/PS1 mice-1	56729808(100.00)	55385310(97.63)	5460583(9.63)	49924727(88.00)
6-month-oldAPP/PS1 mice-2	49936330(100.00)	48688484(97.50)	4828609(9.67)	43859875(87.83)
6-month-oldAPP/PS1 mice-3	49451032(100.00)	48231136(97.53)	4605643(9.31)	43625493(88.22)
9-month-oldAPP/PS1 mice-1	44749838(100.00)	43833491(97.95)	4274531(9.55)	39558960(88.40)
9-month-oldAPP/PS1 mice-2	46686074(100.00)	45838689(98.18)	4553327(9.75)	41285362(88.43)
9-month-oldAPP/PS1 mice-3	47340792(100.00)	46355236(97.92)	4974744(10.51)	41380492(87.41)
1-month-old WT control mice-1	40266824(100.00)	39549660(98.22)	3668373(9.11)	35881287(89.11)
1-month-old WT control mice-2	43046128(100.00)	42128600(97.87)	4130045(9.59)	37998555(88.27)
1-month-old WT control mice-3	45413022(100.00)	44411874(97.80)	4669144(10.28)	39742730(87.51)
3-month-old WT control mice-1	39355612(100.00)	38467260(97.74)	4047409(10.28)	34419851(87.46)
3-month-old WT control mice-2	41078916(100.00)	40224216(97.92)	4001573(9.74)	36222643(88.18)
3-month-old WT control mice-3	39473852(100.00)	38615724(97.83)	4270591(10.82)	34345133(87.01)
6-month-old WT control mice-1	46790578(100.00)	45107760(96.40)	4380712(9.36)	40727048(87.04)
6-month-old WT control mice-2	52953292(100.00)	51121040(96.54)	5162467(9.75)	45958573(86.79)
6-month-old WT control mice-3	48484678(100.00)	47047494(97.04)	5071495(10.46)	41975999(86.58)
9-month-old WT control mice-1	59390490(100.00)	58072403(97.78)	6034802(10.16)	52037601(87.62)
9-month-old WT control mice-2	39148440(100.00)	38268847(97.75)	4164961(10.64)	34103886(87.11)
9-month-old WT control mice-3	48615188(100.00)	47541532(97.79)	4866562(10.01)	42674970(87.78)

Data are expressed as number (percentage). APP: Amyloid-beta peptide precursor protein; mRNA: messenger RNA; PS1: presenilin 1; WT: wild type.

Additional Table 6.

Differentially expressed genes in the APP/PS1 mouse cortices at 1,3,6,9 months in contrast to the same age of control mice

Gene name	Mean TPM (APP/PS1)	Mean TPM (WT)	log2 fold change	P-value	Q-value	Result
1-month-old
Lamr1-ps1	8.331597	0.0001	16.34631	5.35E-15	1.88E-11	Up
Prnp	1468.837	507.0883	1.534365	2.11E-95	6.69E-91	Up
Pianp	63.76651	149.0635	-1.22506	3.88E-45	4.09E-41	Down
Bloc1s6	7.290459	21.08952	-1.53244	1.30E-06	0.001642	Down
Rn7s1	53.21294	17.1677	1.63208	5.37E-05	0.045954	Up
Gm15501	0.623963	7.492632	-3.58594	1.07E-11	3.07E-08	Down
Slc35e2	4.542831	10.98109	-1.27336	7.13E-08	0.000113	Down
AC147560.1	26.13961	0.315753	6.371299	1.62E-05	0.014659	Up
Tanc2	8.460478	36.47014	-2.1079	2.22E-32	1.01E-28	Down
Erbb4	7.498425	1.720321	2.12391	2.57E-10	5.82E-07	Up
AY036118	468.1067	205.2829	1.189224	1.08E-05	0.010017	Up
Dagla	40.63331	11.55615	1.814002	8.98E-36	5.68E-32	Up
Armc10	6.946772	16.88577	-1.28139	1.66E-22	6.57E-19	Down
AC157822.1	21.46617	0.921064	4.542619	1.53E-06	0.001794	Up
3-month-old
Lamr1-ps1	7.63259	0.0001	16.21989	2.60E-14	5.40E-11	Up
Nedd4	109.3076	42.50165	1.362803	1.79E-06	0.001427	Up
Prnp	1620.196	499.8317	1.696654	3.20E-89	6.64E-85	Up
Xiap	7.290938	24.78462	-1.76527	6.42E-12	1.11E-08	Down
Fcho2	16.98941	8.289213	1.035329	6.17E-12	1.11E-08	Up
Zmym5	6.216562	14.43682	-1.21556	1.07E-18	2.48E-15	Down
Fkbp1a	517.338	181.6905	1.509624	3.31E-64	3.43E-60	Up
Commd8	23.31535	11.25659	1.050511	3.98E-05	0.021741	Up
Ahcyl2	15.73925	48.70363	-1.62966	1.05E-38	5.45E-35	Down
Gm27505	0.248576	31.26797	-6.97485	2.16E-11	2.80E-08	Down
Mpv17l	14.64497	39.01993	-1.41381	7.24E-08	7.15E-05	Down
Grb2	38.69309	81.70496	-1.07835	1.62E-28	5.59E-25	Down
Ndn	86.16586	42.15611	1.031375	7.52E-12	1.20E-08	Up
Macrod2	7.089322	17.83625	-1.33109	4.46E-07	0.000375	Down
6-month-old
Gm3375	4.885447	47.44031	-3.27955	2.03E-21	3.93E-18	Down
Gm22133	836.8698	0.001819	18.8115	1.63E-13	1.97E-10	Up
Lamr1-ps1	13.91555	0.0001	17.08634	1.20E-17	1.55E-14	Up
Sik1	3.044825	6.180057	-1.02126	3.00E-05	0.010762	Down
Prnp	2598.264	670.9482	1.953274	1.02E-34	2.81E-31	Up
Lpin1	6.730367	1.05942	2.667411	1.47E-28	3.17E-25	Up
Rasa3	18.69434	6.679233	1.484848	4.18E-05	0.014469	Up
Arhgef9	26.89973	137.7715	-2.35661	1.08E-89	1.05E-85	Down
Egr2	1.947307	6.371476	-1.71015	1.39E-06	0.000655	Down
Gm15501	1.511224	10.68873	-2.8223	5.14E-08	3.56E-05	Down
Tjap1	11.55973	1.682152	2.780728	1.86E-43	6.02E-40	Up
Ptprt	17.44061	4.689551	1.894929	2.58E-05	0.009801	Up
Fkbp1a	662.6176	179.4312	1.884746	4.94E-51	2.40E-47	Up
Cyr61	3.232472	6.781883	-1.06905	4.41E-05	0.014492	Down
Zfx	0.192459	6.660784	-5.11307	9.42E-90	1.05E-85	Down
Gp1bb	19.49666	47.08275	-1.27197	4.29E-10	3.96E-07	Down
Bcas3	43.73818	20.4742	1.095086	0.000116	0.034095	Up
AC147560.1	0.218599	21.13628	-6.59529	4.18E-05	0.014469	Down
Stxbp1	346.1299	107.8913	1.681736	4.33E-07	0.000262	Up
2700081O15Rik	4.00902	12.23863	-1.61012	7.30E-29	1.77E-25	Down
Brd4	26.71231	6.680526	1.999471	1.65E-45	6.40E-42	Up
Sel1l	9.919385	3.658156	1.439134	4.09E-08	3.05E-05	Up
Icmt	13.76329	5.797461	1.247332	0.000106	0.031634	Up
2-Sep	4.048975	14.4711	-1.83755	1.52E-09	1.28E-06	Down
Pdf	12.22894	25.97062	-1.08658	1.06E-18	1.47E-15	Down
Xpo7	3.723344	9.880672	-1.40801	2.74E-21	4.83E-18	Down
Arc	66.9146	170.5302	-1.34963	2.88E-05	0.010526	Down
Fos	11.17202	27.47285	-1.29812	7.75E-09	6.01E-06	Down
Il33	40.51075	19.46344	1.057538	4.48E-08	3.22E-05	Up
Klf2	7.280256	15.40376	-1.08122	2.19E-10	2.24E-07	Down
Nr4a1	40.94647	82.88205	-1.01732	6.23E-11	7.11E-08	Down
Tnpo1	0.423412	14.81462	-5.12882	1.05E-73	6.80E-70	Down
Taf13	9.450447	22.09557	-1.2253	1.28E-19	1.91E-16	Down
Brsk2	38.12381	87.1189	-1.19229	1.03E-20	1.66E-17	Down
9-month-old
Cst7	56.13222	0.410771	7.094351	4.02E-102	7.04E-98	Up
Hnrnpc	48.85224	110.192	-1.17352	1.95E-38	6.82E-35	Down
Mgat1	16.04041	7.332844	1.129266	5.09E-10	1.48E-07	Up
Lamr1-ps1	8.060656	0.026737	8.235934	6.63E-13	3.14E-10	Up
Nedd4	30.13857	106.1997	-1.8171	8.02E-05	0.006562	Down
Gpnmb	6.932263	1.463214	2.244185	3.39E-11	1.16E-08	Up
Itgax	5.909267	0.159953	5.207256	9.49E-37	2.77E-33	Up
Ctsz	73.35858	33.67782	1.123167	8.06E-16	4.86E-13	Up
Prnp	1874.794	523.9732	1.839167	1.09E-34	2.74E-31	Up
Capg	5.834883	1.694853	1.783543	3.67E-09	9.30E-07	Up
Slc11a1	7.507433	3.393156	1.145692	2.11E-12	9.03E-10	Up
Lcn2	12.74023	0.238077	5.741817	0.000423	0.02372	Up
Mpeg1	31.47217	13.46518	1.224843	7.80E-19	6.20E-16	Up
Ly86	54.01884	23.03132	1.229865	3.19E-08	7.06E-06	Up
Olfml3	10.90158	1.88724	2.530188	0.001177	0.047787	Up
Rasa3	26.90664	12.72583	1.080203	4.20E-22	5.25E-19	Up
Ndst4	2.53883	5.793142	-1.19018	1.28E-09	3.43E-07	Down
Hbb-bs	87.68476	317.0072	-1.85412	0.000721	0.03419	Down
Csf3r	10.2598	5.069636	1.017049	8.16E-08	1.68E-05	Up
Fyb	8.614782	3.643961	1.241307	0.000542	0.028191	Up
Ifitm3	58.16765	25.94524	1.164747	9.37E-06	0.001086	Up
Hba-a2	65.71364	256.5913	-1.96521	0.000257	0.016528	Down
Hba-a1	61.25622	233.2518	-1.92896	0.000625	0.030723	Down
Lag3	13.25408	5.91718	1.163455	5.91E-14	3.34E-11	Up
Ifit3	15.28814	6.210571	1.299616	2.84E-12	1.16E-09	Up
Rab26os	20.04657	8.868279	1.176629	0.000132	0.009603	Up
C1qa	176.116	85.48203	1.042833	4.56E-22	5.31E-19	Up
Atp8a1	16.20754	40.22233	-1.31133	2.43E-05	0.002486	Down
Gm15501	11.03089	0.729388	3.91872	2.92E-06	0.000399	Up
mt-Ts2	0.534649	77.19743	-7.17382	1.68E-06	0.000251	Down
Cd300c2	8.388576	4.042001	1.053356	3.54E-06	0.00047	Up
Pde1a	16.58869	56.96195	-1.7798	1.21E-11	4.71E-09	Down
S100a8	11.53033	0.0001	16.81507	2.90E-05	0.002872	Up
Cd9	93.21046	45.89574	1.022132	5.23E-12	2.08E-09	Up
C4b	45.46685	8.71795	2.382754	2.96E-26	4.71E-23	Up
Clec7a	11.98666	0.400086	4.904976	6.92E-59	6.05E-55	Up
AU020206	5.639614	2.392004	1.237377	1.18E-12	5.30E-10	Up
Bst2	11.60964	3.56583	1.703014	1.26E-11	4.71E-09	Up
Serpina3n	41.74544	10.41401	2.003093	1.65E-10	5.26E-08	Up
Mef2c	52.97231	113.2346	-1.09601	2.42E-08	5.44E-06	Down
Gfap	356.9862	83.29594	2.09955	2.02E-16	1.26E-13	Up
Tyrobp	179.4221	50.84766	1.819104	1.15E-46	5.02E-43	Up
Fcer1g	74.58019	35.05207	1.089293	2.39E-13	1.31E-10	Up
Ccl3	13.76641	0.103299	7.058177	1.63E-23	2.38E-20	Up
Ifitm2	44.15068	20.8988	1.079016	0.000706	0.033667	Up
Pcp2	9.978739	0.812252	3.618858	0.00093	0.040775	Up
Ccl6	15.43149	3.330178	2.212206	4.57E-13	2.35E-10	Up
Lsp1	8.248022	3.885915	1.085794	0.000368	0.021535	Up
Tmem181b-ps	103.2725	50.46891	1.03299	9.55E-17	6.43E-14	Up
Pomc	13.90785	0.843073	4.044098	3.24E-10	9.77E-08	Up
S100a9	17.61923	0.248051	6.150368	8.08E-05	0.006578	Up
Hbb-bt	20.79302	84.56809	-2.02401	1.40E-07	2.72E-05	Down
Lyz2	53.46002	15.71911	1.765941	3.57E-31	7.80E-28	Up
Oprk1	2.66741	7.020116	-1.39606	4.56E-05	0.004206	Down
Mid1-ps1	1.777277	7.015588	-1.9809	7.81E-07	0.000133	Down
Bcl2a1b	5.293898	1.208159	2.13152	1.24E-08	2.89E-06	Up
Spp1	37.59672	13.60768	1.466186	1.07E-06	0.000174	Up
Lgals3bp	35.8531	17.20962	1.058883	1.92E-11	6.85E-09	Up
Erbb4	0.498413	8.41588	-4.0777	0.00012	0.008989	Down
Ifit3b	8.604208	4.127797	1.05967	2.95E-10	9.06E-08	Up
Gm694	12.25915	3.51378	1.802764	0.000173	0.011942	Up
Trem2	42.76004	15.90794	1.426516	4.16E-19	3.47E-16	Up
Oasl2	6.292386	2.665781	1.239049	2.85E-07	5.25E-05	Up
Eps8l1	3.553843	8.104072	-1.18927	6.40E-06	0.000789	Down
Hist2h3c1	6.397843	2.655042	1.268851	5.74E-05	0.005027	Up
Cd52	27.41597	6.647257	2.044186	6.68E-21	6.87E-18	Up
Abi3	9.706694	4.683315	1.05145	1.31E-05	0.001458	Up
Ifi27l2a	8.036828	1.690128	2.249494	1.80E-07	3.43E-05	Up
Cd14	7.541097	2.928268	1.364727	7.29E-07	0.000126	Up
Cd68	55.73383	18.69902	1.575591	1.59E-30	3.09E-27	Up
Ttbk2	1.047009	13.96846	-3.73783	0.000204	0.013596	Down
Snx10	31.4692	85.79997	-1.44704	3.78E-50	2.21E-46	Down
Camk2d	25.34241	63.82576	-1.33259	9.63E-18	7.03E-15	Down
Hist2h2aa1	0.0001	22.909	-17.8056	2.38E-18	1.81E-15	Down
Fcgr2b	12.93337	3.258524	1.988808	2.01E-19	1.85E-16	Up
Grp	21.454	9.28983	1.207523	0.000561	0.028589	Up

APP: Amyloid-beta peptide precursor protein; PS1: presenilin 1; TPM: transcripts per million; WT: wild type.

Table 1.

Top 10 of the most significantly dysregulated mRNAs in the AD mouse cortices at 1, 3, 6, and 9 months of age

Gene ID	Gene name	log2 (fold-change)	P-value	Q-value	Result
ENSMUSG00000076036	Gm22133	18.8115	1.63E-13	1.97E-10	Up
ENSMUSG00000056054	S100a8	16.81507	2.90E-05	0.002872	Up
ENSMUSG00000081229	Lamr1-ps1	17.08634	1.20E-17	1.55E-14	Up
ENSMUSG00000104953	AC147560.1	6.371299	1.62E-05	0.014659	Up
ENSMUSG00000068129	Cst7	7.094351	4.02E-102	7.04E-98	Up
ENSMUSG00000000982	Ccl3	7.058177	1.63E-23	2.38E-20	Up
ENSMUSG00000056071	S100a9	6.150368	8.08E-05	0.006578	Up
ENSMUSG00000064220	Hist2h2aa1	–17.8056	2.38E-18	1.81E-15	Down
ENSMUSG00000098974	Gm27505	–6.97485	2.16E-11	2.80E-08	Down
ENSMUSG00000064365	mt-Ts2	–7.17382	1.68E-06	0.000251	Down

AD: Alzheimer’s disease.

Identification of differentially expressed miRNA in APP/PS1 mice

A total of 24 miRNA relevant cDNA libraries were also constructed, and a mean of 681,080 clean reads after deduplication with a Q20 higher than 97.57% were obtained (Additional Table 7). The clean reads from the 24 miRNA-specific cDNA libraries were aligned using miRBase V21.0 and miRDeep2 V2.0.0.8, which allowed us to identify 1915 known miRNAs and 371 novel miRNAs in the APP/PS1 mouse cortex in contrast to the correspondent control ones (Additional Tables 8 ^{(245.9KB, pdf)} and 9). Among these known and novel miRNAs, a further evaluation revealed that 68 miRNAs were significantly dysregulated in the APP/PS1 mouse cortex compared with that of the correspondent control mice (Figure 1). Among these significantly dysregulated miRNAs, 6 were increased and 5 were decreased in the APP/PS1 mouse cortex of the 1 month old group. Five miRNAs were significantly increased and eight were significantly decreased in the APP/PS1 mouse cortex of the 3 months old group. Finally, four miRNAs were increased and five miRNAs were decreased in the APP/PS1 mouse cortex of the 6 months old group, and 24 miRNAs were increased and eight miRNAs were decreased in the APP/PS1 mouse cortex of the 9 months old group. Twenty-five significantly expressed miRNAs were novel, and this was the first time they have been identified in the cortex of APP/PS1 mice; these results thus reveal new potential biomarkers that are involved in the pathological process of AD (Table 2). The miRNA-10a-5p was down-regulated in the APP/PS1 mice both at 1 month and 3 months of age, and miRNA-706 was decreased in the APP/PS1 mice at 3 and 6 months of age. Furthermore, novel mature miR-80 and novel mature miR-7 were decreased in the APP/PS1 mice at 6 and 9 months old. Interestingly, novel mature miR-3 was reduced in the 3-month-old APP/PS1 mice, but increased in the 9-month-old APP/PS1 mice (Figure 2). The heat map revealed the expression pattern of the significantly changed miRNAs from 1 to 9 months old, and the hierarchical cluster analysis revealed the clustering of differentially expressed miRNAs in the cerebral samples, as shown in Figure 3.

Additional Table 7.

Summary of the miRNA sequencing reads

Sample	Reads_count	Uniq_reads_count	Bases_count	Average_length	Q10	Q20	Q30	GC_percentage
1-month-oldAPP/PS1 mice-1	12729834	686531	2.79E+08	21.92	100.00	97.98	96.87	49.35
1-month-oldAPP/PS1 mice-2	7821972	459888	1.72E+08	21.94	100.00	97.98	96.88	50.00
1-month-oldAPP/PS1 mice-3	10749727	563013	2.37E+08	22.09	100.00	97.96	96.86	50.25
3-month-oldAPP/PS1 mice-1	10587370	486548	2.32E+08	21.96	100.00	97.94	96.80	49.53
3-month-oldAPP/PS1 mice-2	12123223	524714	2.63E+08	21.73	100.00	97.97	96.85	49.39
3-month-oldAPP/PS1 mice-3	9252098	412254	2E+08	21.66	100.00	97.94	96.80	49.40
6-month-oldAPP/PS1 mice-1	12268755	1011818	2.74E+08	22.34	100.00	97.90	96.71	48.63
6-month-oldAPP/PS1 mice-2	9662514	854507	2.16E+08	22.39	100.00	97.86	96.64	49.55
6-month-oldAPP/PS1 mice-3	9271968	909827	2.08E+08	22.44	100.00	97.85	96.63	49.20
9-month-oldAPP/PS1 mice-1	9203905	678262	2.05E+08	22.23	100.00	97.99	96.89	49.35
9-month-oldAPP/PS1 mice-2	9904974	695072	2.2E+08	22.17	100.00	97.94	96.80	49.14
9-month-oldAPP/PS1 mice-3	8785112	629609	1.95E+08	22.17	100.00	97.91	96.75	49.42
1-month-old WT control mice-1	6768197	397031	1.49E+08	21.96	100.00	97.92	96.79	50.78
1-month-old WT control mice-2	6991031	437870	1.52E+08	21.73	100.00	97.94	96.81	49.78
1-month-old WT control mice-3	9287056	417817	2.04E+08	21.95	100.00	97.89	96.74	49.80
3-month-old WT control mice-1	7761039	468075	1.7E+08	21.96	100.00	97.95	96.83	50.39
3-month-old WT control mice-2	10693517	545212	2.34E+08	21.86	100.00	97.84	96.61	49.17
3-month-old WT control mice-3	8825697	461374	1.93E+08	21.91	100.00	97.89	96.72	49.89
6-month-old WT control mice-1	7129577	1106789	1.63E+08	22.92	100.00	97.57	96.08	48.71
6-month-old WT control mice-2	9138168	1069494	2.05E+08	22.41	100.00	97.88	96.70	49.24
6-month-old WT control mice-3	11712124	1637686	2.64E+08	22.53	100.00	97.83	96.59	48.17
9-month-old WT control mice-1	8726091	591285	1.92E+08	21.99	100.00	97.91	96.75	50.08
9-month-old WT control mice-2	11923652	681961	2.61E+08	21.86	100.00	97.92	96.77	50.08
9-month-old WT control mice-3	12766418	619289	2.78E+08	21.77	100.00	97.99	96.90	48.91

APP: Amyloid-beta peptide precursor protein; miRNA: microRNA; PS1: presenilin 1; Q10-30: percentage of quality value greater than 10-30; WT: wild type.

Additional Table 9.

Summary of the novel miRNA prediction using miRBase and miRDeep2 in the APP/PS1 mouse cortices at the four tested ages

Provisional ID	miRDeep2 score	Total read count	Mature read count	Consensus mature sequence (5’-3’)	Consensus star sequence (5’-3’)
novel mmu-miR-1	22676.7	44489	42119	CUCGACACAAGGGUUUG	GCUUCUGGGUCGGGGUU
novel mmu-miR-2	21174.7	41531	41530	AUCUCGCUGGGGCCUCCA	GGGCCCAAGUGUUGAGAAC
novel mmu-miR-3	6489	12724	12720	UGACUUCCAAUUAGUAGAU	UAUUGAUGAGGAUCUUA
novel mmu-miR-4	2860.4	5645	5585	GUUUCCGUAGUGUAGUG	CUAACACGCGAAAGGUCCCC
novel mmu-miR-5	1933.9	3819	3818	CAUUUGUUUUGAUGAUGGA	UAAAUACACUAGAAAUG
novel mmu-miR-6	1204.1	2370	2369	AACGAGGUUCCCACUGU	CUUGAGAGCGCCUUGUU
novel mmu-miR-7	1045	2044	2040	AGGCUAGGCUCACAACC	UCUGAGGCCAGCCUGGGC
novel mmu-miR-8	552.8	1098	1091	AUUUGAUGGCCCUGAAG	UUAAGGGGAACGUAUGGG
novel mmu-miR-9	329.6	724	719	AAAAGAAUUACUUUGAU	AACAAAGGAUUCUCAAC
novel mmu-miR-10	289.5	592	591	UCUUUGGUUAUCUUGCUGUG	UAAAACUAUAACACCAAGCUUGGAA
novel mmu-miR-11	145.3	291	290	UGCACCCUUCUGACCCACUUCUCCU	AAGGAGGUGGGGGGCUGCUGU
novel mmu-miR-12	136.2	268	241	UGUGGAAUCCCUUCAACCUUGUGG	CUUGGUUGUACUUGGGUUCUUGC
novel mmu-miR-13	127.4	258	243	UUGGGAAGGUGGAUAAUUUGG	CAAGUUUCCAACUCCCCGCAGU
novel mmu-miR-14	104.3	212	211	CUGGCGCUUUCACACACU	UGCUGAAGGCCGUUUCCCGUG
novel mmu-miR-15	81.8	159	100	CUUGCAUGUGGGCCUGUGUGCU	ACAGACGGGCUCUCAUGCUGACA
novel mmu-miR-16	49.9	90	70	CCAGCACUGAGUUGUUCUGUCA	UCAGAACAACCUGACCUGCCU
novel mmu-miR-17	45.2	80	66	AAACAAACCAGAGGCUCACACU	UGUGAAUCUCCGGCGCGUUU
novel mmu-miR-18	37.2	64	24	ACUGGGCUGCUCUGGGCGAGCCGG	UGCACACCUGGAGCCCAGAU
novel mmu-miR-19	35.5	63	54	UUCCUCUUCCUUUCUUCAUUUAUU	AUAAAUGAAGGAAGAAGGCAG
novel mmu-miR-20	34.4	59	58	CUGGCGGCGGUUGCUCUCUGC	AGAGACUAAUCUGUCGCCACCC
novel mmu-miR-21	30.5	64	62	CGUUCCCGCGGCUGUCACCGCG	GCGGCAGCGGCGGGAGCG
novel mmu-miR-22	30.1	51	49	CCCUGCGCUGAGUGCUGUGACU	ACUCAGCCCCAGUGCAGCCUGGCC
novel mmu-miR-23	29.9	57	52	CUUUCUCCUGCUGCCCUGCAGA	CCUGUAGGCCAACGGGGGAAG
novel mmu-miR-24	28.1	47	32	CACUGACAGCAGCAUCUCCAUGA	UGGAGACUUCCCUGUCAGAGC
novel mmu-miR-25	28	47	41	UGAAGCUCUCUGCUUGCUCACCU	UCAGGUGAGGAGAGAGCUCUGAGUU
novel mmu-miR-26	24.9	48	27	UGUCCAUGGCUUAGCCUCCUCACU	AGAGAAGGCCGGCCUUGCAGA
novel mmu-miR-27	23.6	53	52	AUCUCCCCUACCUUUUCCAGG	UGGGAGUAGGUAUGGGAGUUCA
novel mmu-miR-28	23.3	44	25	AGGCCUGGGCCCACACUAACU	UUGGUGUGCUGAGCUCAGGCCAAGG
novel mmu-miR-29	23.1	45	44	UCUCUUCUGCUCUGUGUCACAGC	UGCACUGCUGAGGAACA
novel mmu-miR-30	22.1	41	38	CCCAGAGUGGACGGAACACCGA	CGUUGUUCCCUUCCACGCUGGGC
novel mmu-miR-31	19.7	30	27	ACAGUCUGUCACCUGAGCCAAACU	UUUGCUGCAUUUGACAGGCACA
novel mmu-miR-32	17.9	35	32	UUGAGUCGGGUAGAAUCUGUGG	AGAGGCUUUACCCGAACACUGU
novel mmu-miR-33	16.8	29	23	CACAUCCAGUCACUAAGGCUC	AGCCUUAGUGACUGGAUGUGU
novel mmu-miR-34	16.8	33	29	CACAUGGCUCUGGACAACAUG	AUGAUGUCACGAGCACAGGUGG
novel mmu-miR-35	16.3	43	42	UAGAAUUAGCUUCUGCC	GGGGAUGGCUAAGUGGU
novel mmu-miR-36	16.3	30	29	UGUCCUGCCUUAUCACAAAGC	UUGUGAUAAGGCAGGACAUAU
novel mmu-miR-37	16.2	32	27	AUUCCACAUUCCUGUCCUUUG	AAGCAGAGGGCACGUGGAUCUGA
novel mmu-miR-38	15.8	27	23	CACAUCCAGUCACUAAGGCUC	AGCCUUAGUGACUGGAUGUGU
novel mmu-miR-39	15.6	22	20	AGGGGACCAUUCUUGUGAAGGA	CUUCAAAAGGAUGGGCCCAC
novel mmu-miR-40	15.5	23	22	ACAGCUCCUCUCUCUCUCUGAAG	UGCAGAGAGAUGCGGGGAGGGUUAA
novel mmu-miR-41	14.5	28	24	AACACCAGGACUGAAAACAGCC	UUGUUUCAUCUCCUGGGUUUGU
novel mmu-miR-42	14	26	8	AAGCAUCUGUCCAAGACCGGGG	UCCAUGUCUUGCUCAGCUUGCUU
novel mmu-miR-43	13.9	50	48	GAGGAAGAAGUUUGUACAGA	UGUGACUGCUGUUUGCC
novel mmu-miR-44	13.5	25	24	UCUGGCUCUUGGGAUCUUUCUGU	AGAUAGGUCCCUGAGCCCUGAGGG
novel mmu-miR-45	13.2	25	10	CAUCGUUACCAGACAGUGUUAG	CAGCACUGUCCGGUAAGAUGCC
novel mmu-miR-46	13.2	24	14	CUACUGGGCAGACUCUAAGAAA	UCUUGGAGACUUCCAGCUUGUGA
novel mmu-miR-47	11.3	20	12	UGCUUGGCACCUGGUAAGCACUC	UGCCUACUGUGUGCCAAGACAUU
novel mmu-miR-48	11	21	16	UAGGGAGGAGUGGCCUGAGUGCUCU	AGAGGGGACACUUCUCUCUCC
novel mmu-miR-49	11	21	16	UAGGGAGGAGUGGCCUGAGUGCUCU	AGAGGGGACACUUCUCUCUCC
novel mmu-miR-50	10.6	9	8	AGGCACCAAGGAGGAACUAGG	CUAGUUCCUCCUUGGUGCCUGC
novel mmu-miR-51	10.6	20	19	AAGCUGUUAUCUCUCCAAGCCU	GUGCUGGAGGCUCGCAGCUUUC
novel mmu-miR-52	10.6	9	8	AGGCACCAAGGAGGAACUAGG	CUAGUUCCUCCUUGGUGCCUGC
novel mmu-miR-53	10.5	28	27	UUCCUAGCGGGUGAACCU	CUCAGUAGCUGGAGCAUC
novel mmu-miR-54	10.4	20	18	UUCUAAGCAGAGGUGUUAGUUCC	AACUGCACCUCUACUUCCAGA
novel mmu-miR-55	10.1	10	7	UGAAACUGUUUUCCAGACACACA	UGUGUGUCUGGAAAACAGUUUU
novel mmu-miR-56	9.4	10	9	UUUGGUUCCUCUGACCUUUUGCU	GCCAGGUCUCUGGAGCCUUUGC
novel mmu-miR-57	9.2	17	12	UCUCUAUCCCUGGCCUGCUCUCC	GCCUAGGUUUGGGGAUACAGAGC
novel mmu-miR-58	8.7	15	9	CCUGCUUGCCUCUCACUGACAGC	CUGCAGUGAGAGGAAGAAAGCU
novel mmu-miR-59	8.4	13	5	UGGGUAUCCAAAGGCCUCCUCU	GAGGAGGCCUUUGGAUACCCA
novel mmu-miR-60	8.4	13	5	UGGGUAUCCAAAGGCCUCCUCU	GAGGAGGCCUUUGGAUACCCA
novel mmu-miR-61	8	15	13	AAUGCAGCCUAGAACAGUGC	CUCUGUUCUCAGCUGCAGC
novel mmu-miR-62	7.8	25	21	UGUUGCAAGCACCUGAAUCG	AGGGCUGCGGAUUACCUC
novel mmu-miR-63	7.8	7	6	UAAGCUCUGCUCACUCUGAAGC	UGUCCAGAGCGAGCAGAGCUCA
novel mmu-miR-64	7.5	6	3	CAGGCAGUGACUGUUCAGAUGUC	CACUUCUCCAGCGCUGCCUUCC
novel mmu-miR-65	6.6	4	3	CAGGAAGGAGCUGGUUGCAUCUC	CUGCAUGCUGCUCCUUCCUACA
novel mmu-miR-66	6	10	9	CCACCAGCGCUGUCACACAGAGC	CAGUGUGUGGGAAGCGCUUCUGGGAGGCGGCCC
novel mmu-miR-67	5.9	10	9	UAUGUUCCAACAUGUCAGCAUGC	AUGCUGACAUGUGCGAACAUGU
novel mmu-miR-68	5.8	17	15	GUUGCUGGGCUAGAAGC	GGACAGUCUAGGAACAGC
novel mmu-miR-69	5.8	12	9	UCCCUCCCUCCAUCUUCCCAGA	AGAGAGGGAGGGAGGGA
novel mmu-miR-70	5.7	43	43	UCCUGAGAUUCUGCCCCGCAGC	UGCGGGCAGGGCGGUCAGGGCC
novel mmu-miR-71	5.7	9	7	CACAUGGGCCAAAGCUUGGGUC	GCCAGCUUUGGGCCUAAGUGCU
novel mmu-miR-72	5.6	36	35	UGAUGGAUCUGUCUGAGCCAU	GGCUCAGACAGAUCCAUCACG
novel mmu-miR-73	5.5	17	15	UCCCCUCCCUUCUCUGGUUGCAGG	AGUGCUGGGAGGGACGGCU
novel mmu-miR-74	5.5	8	7	UCUUUUGCUAGAUGCUGUGCC	CACGGGUGAUCUAGCAGAAGAUGA
novel mmu-miR-75	5.5	17	17	UGGCCCCACUUGGCUUUUGAGA	CUAGAAGCCAUGUGGGGCCAUG
novel mmu-miR-76	5.4	9	8	AGAGGCCGCGUCGGGCCGCAGC	UCGCGGCCUCUAGCGUGACGUCU
novel mmu-miR-77	5.4	217	216	UCCCGCCCUUUCUCCAUCUUAG	GGGGUGGGGUGGGGGUGGGCUC
novel mmu-miR-78	5.3	12	12	GAGGGACAUACUCAAUGAGA	UUAUUGUCCAUGUCCCUGCC
novel mmu-miR-79	5.3	13	9	CCCUUCUUGGCCCUGGC	UGGGGUAGGUGUU
novel mmu-miR-80	5.2	160	160	GGGGAAUGUGGCUCUUGCC	CAAGGCGCAUCUCCUCUU
novel mmu-miR-81	5.2	19	19	UUGGCCAAGCUCAGAGAAAG	UUCUCUGGAUGUGGCCAGA
novel mmu-miR-82	5.2	13	12	UGUGUGUGUACAUGUGCAUGUG	UGUGCAUAUGUGCAUGUGGGC
novel mmu-miR-83	5.2	17	17	UACUUGGAUCCCACAGAUAGCUG	GUUUUCUUGUGGGACCCAGGUAUA
novel mmu-miR-84	5.1	12	12	ACGCCUCCCUUUUCUGCCAG	GGUUGAAAAGGUUGGGGGUGG
novel mmu-miR-85	5.1	11	11	UAGUGCCUCUUCCACCUUCAGG	UGCAGAGGUCGGAAUAUGGGCAGAAGU
novel mmu-miR-86	5.1	27	26	UGGCUCAGAUCAGCAGG	CCUGGCAUGCUGUGGGC
novel mmu-miR-87	5.1	19	19	UGGCAGUGGAGUUAGUGAUUGU	AAUCAGCUAAUUACACUGCCUAC
novel mmu-miR-88	5	27	27	UUGGUCUGAGCAUCUUCCAGG	GGGAGGAUGUCAGGAUGCAGACU
novel mmu-miR-89	5	10	10	UUUCUCUCUCCCCCGCCCCUGC	GGGGGUGGGGAAGAGGGAGAGA
novel mmu-miR-90	5	12	12	AGGCUGUGACUCUGGCAC	GCAAGUCCUGGGCCGCG
novel mmu-miR-91	5	35	35	UCUUCUCUUUCCAGUCAUCAGC	UGGUGCCUGGAUUGGAGGAUG
novel mmu-miR-92	5	11	11	AGCUGUCUGGGCUGUCAGGCCUG	GUUUCUGGCUCCCUGGCCAGCUGC
novel mmu-miR-93	4.9	13	7	CAAAGAGGGGACCUGAGCU	UUCUUGGUUUCCUCAGUG
novel mmu-miR-94	4.9	8	7	CGGCGGGGCCGGUACUUGUAGU	CAGCGAGUACUUGUCCU
novel mmu-miR-95	4.9	16	5	AUUUCCGGGCUGUGGCGCC	GGGCUUUCCACUGGAACG
novel mmu-miR-96	4.9	31	31	CAAAGCCAGCUGACAUUU	AUGCAUGGGUGUGAUGCU
novel mmu-miR-97	4.8	468	462	GUGAGGACUGGGGAGGUGG	GCGCUUCCAGAGGUUCUGGCUU
novel mmu-miR-98	4.7	26	22	UAAAGGGUUUUGUCUGCUCACC	AGGACAGACAGACCCUAUCU
novel mmu-miR-99	4.7	31	31	UAGUGCCCCUGUGUUCUCUACU	GAGAGAAGCGUGGGGCAGUUAGA
novel mmu-miR-100	4.6	40	40	UAGCACAAUGUGAAAAGAGCUCC	UGCCCUUUUAACAUUGCACUGCU
novel mmu-miR-101	4.6	12	7	UUCUCUCUGGCCCCUUCC	AAGGGUUAAACAUGGAGAAGG
novel mmu-miR-102	4.6	10	10	UCCUGUAGCCAGCAUAGUGC	ACUCUGCUGGCUACAGUGUCA
novel mmu-miR-103	4.5	11	11	AGUUCCUCUGGGCUCAGA	UGGGCUCAUGAGGAAGCAG
novel mmu-miR-104	4.4	41	41	UGUGUGUGUGUGUGUGU	UCAGCACACAUAGACAGC
novel mmu-miR-105	4.4	21	21	UGGCUCAGUUCCAGGAAC	UCCUCUCUCCCUGGGCAGACU
novel mmu-miR-106	4.3	9	8	UGCCCCUCCUUCUCCACCACCA	GAGGUGGAGAAGGGUGGGACUUCAGG
novel mmu-miR-107	4.2	25	25	CCAGCCACCCGCCACUGCA	CAAUCCAGUGGUGAGCUGACA
novel mmu-miR-108	4.2	6	4	UGCGAGUCCACACUGGGGUGC	CACCCGGCGUGGGCUCGCUCGG
novel mmu-miR-109	4.1	26	26	GUGGCCAGAGACUGGGAA	AGGGGUUUUUAGGGUAGGG
novel mmu-miR-110	4.1	22	22	UUGUGCUUGAUCUAGCCAC	GGCUCGGGAGUGUAAGCACGGGU
novel mmu-miR-111	4.1	11	8	CUGACUCUGGGAUUCCCAUUA	AUGGGAAGAAACUCAGACUCUA
novel mmu-miR-112	4	14	13	CCCGUCAGGCAGGAAGGC	CUUUCUCUGUUUUCUUC
novel mmu-miR-113	4	128	124	ACACAGCAUGGAGACCUG	GGCCGUACCCUGUUUGC
novel mmu-miR-114	4	23	23	UCUCUGGGCCUGUGACUUUU	GAGUCAAAGCCCACAGGGG
novel mmu-miR-115	3.9	12	11	CUGUGACCACUGUGGAUC	ACACGUACUCCAGUCCUA
novel mmu-miR-116	3.9	10	10	UUCACUGGGAGCCAUCCAA	GGGUAGCAUCCAGGGAGC
novel mmu-miR-117	3.7	6	5	UUAUGAUCCCGUUUUAUAGAUG	UCUAUAAAACGGGAUCAUAAC
novel mmu-miR-118	3.7	9	9	AGCACCUGCCAGCUCUGAC	CAGAGAGAAGCAGGCCCUGCCUC
novel mmu-miR-119	3.6	11	10	UGGAUUCUGAGGAUCUCC	AUUGCCAGUCCUAUGAG
novel mmu-miR-120	3.6	10	10	GGGACUGUAAGGAAGGA	CUUCAUUGAUGCGUUCCUUG
novel mmu-miR-121	3.6	8	7	AUGUGGAUGGCAGCUUCU	AGGACUUGGUCUCACACAUC
novel mmu-miR-122	3.6	5	3	GAUCCUGGAGGCAGAGACUAA	UGUUUUUAUGCACUCCAG
novel mmu-miR-123	3.6	10	8	AGGAAACAGAGACUUCUC	GAAGUUUCUGCGCUUCUGA
novel mmu-miR-124	3.6	8	8	UGUGUGGAAGCCUCUAGCCUGC	AGCUGGCGCCUUCGCACAGA
novel mmu-miR-125	3.6	34	34	UGUGUGUGUGUGUGUAUGUGU	GCGUACAUGUACACACCUUUG
novel mmu-miR-126	3.6	7	6	ACCUGGGUCCUACCUGAGAGC	AAUCCCAAGGGAAGGAACCCAGCACAGCU
novel mmu-miR-127	3.5	8	7	GUGAAAGGUACUAGAGCC	UUCAGCUCCUGAGGCAG
novel mmu-miR-128	3.4	13	12	CUUCCUCCUUGACUGGGUCAUC	GCACCCGGAGAAGGAGGGCGGAC
novel mmu-miR-129	3.4	8	8	GUGCUUGAGAAUGCAGAAUUC	AUGGGGCGUGGGCAGAG
novel mmu-miR-130	3.3	13	12	UCCGUUGUGUGAGGAGGC	CGCCUUAGAAGGAAGGAA
novel mmu-miR-131	3.3	28	28	UUGCUUCAGGACCCAGUCUCC	GGACUAGGCUCUGAGGAUAAAG
novel mmu-miR-132	3.2	10	10	UCCGGACAAUCUGUAACUCAU	CAGUUACAGACUGUCCGGAGG
novel mmu-miR-133	3.1	12	6	UAGGAGGUGGAGCGGCUGCCUGA	UCUCUGCUGCUUUCCUCCUAGA
novel mmu-miR-134	3.1	11	11	UAUGUGCCUGCAUGUAUAU	GUGCACCUGCAUGUAUAUUCA
novel mmu-miR-135	3.1	12	5	CACCUGGCUGGUGAACAGUG	UUUUGUAAACAGCCUGUGC
novel mmu-miR-136	3.1	117	116	AGACACUAUGUCAGCUCCUUUCU	UUCAAAGUGUUCUUAAAGC
novel mmu-miR-137	3	5	3	CAGCACACUGGUGAUCCC	CUCACUAGUCUGCUUAG
novel mmu-miR-138	2.9	5	4	UUUUCUCCUUGUCCUCCUCAG	UGAGAGGCAAGGAGAGAUAGGGA
novel mmu-miR-139	2.9	7	1	AACAGAUAGGAACCAAAAUAUU	AUUUUGAUUCCUAUCUGUUCU
novel mmu-miR-140	2.8	11	11	AUAUCAGAAGGUGACUG	GUGUACGCUUUUGGUAUCU
novel mmu-miR-141	2.8	153	153	UGGCCCCCCAAGAACUAUGUU	CAUAGUUCUUGGGGGGCCAGA
novel mmu-miR-142	2.8	153	153	UGGCCCCCCAAGAACUAUGUU	CAUAGUUCUUGGGGGGCCAGA
novel mmu-miR-143	2.8	32	32	CCCAGCCGUCGCCUCGCCUCGUC	CGAGGCGAGGCGGCCGGCGGCGCGGCG
novel mmu-miR-144	2.8	21	8	AUGCACAGUGUUUUCCUGA	UGGAAGAACAGCUGUGAGC
novel mmu-miR-145	2.7	16	15	AGGCCCAGAUCAGCAGGA	UUCUAGCUCUGCCUGUG
novel mmu-miR-146	2.7	12	12	UCAGUGCAUCACAGAGCUU	GUAUUGUGAGUAAAUGAGC
novel mmu-miR-147	2.6	339	338	CCUCGUACGGGCCACCA	GUGGCCCAUAUGGGGAC
novel mmu-miR-148	2.5	7	7	UAAUCUCUGGAAAGGUCACC	UGGGCUUUUCCAGUGGACAUACC
novel mmu-miR-149	2.5	78	78	UGGAGGACUUGUGAUUUUCUU	GAAAGAUCUCAGCCAUUUGGA
novel mmu-miR-150	2.4	4	3	CUGACCUCCUGCAGCAAGCC	GAUGUGCAGGAGUCUUGAGUA
novel mmu-miR-151	2.4	6	6	UGAGCAGAAAGGGACAGAGAG	CUGUCUCCCUCUGUUUUGCUCCAGG
novel mmu-miR-152	2.3	16	16	UGUUUUCCAGUUUUUCUGUAC	ACAGAAAAACUGGAAAACAAA
novel mmu-miR-153	2.2	12	2	GUUGCCAGGGAGAAAUCUACU	AGUAGAUUUCUCCCUGGCAACU
novel mmu-miR-154	2.2	52	17	ACACUGGGGUUACAGAUCCUG	CAGGAUCUGUAACUCCAGUGUC
novel mmu-miR-155	2.2	6	6	AGGGGCUGAGAAAGUGGU	CAAUCUCCCCUCA
novel mmu-miR-156	2.2	21	20	GUUCGCGGAGCUCACGUGCUC	GCGCUGAGGUUCGGGUGACCG
novel mmu-miR-157	2.2	16	16	UGUUUUCCAGUUUUUCUGUAC	ACAGAAAAACUGGAAAACAAA
novel mmu-miR-158	2.1	13	13	CACCCGCCUCCUCGGUGACCGG	GCCACCGAGGAGCCGUGGGCACG
novel mmu-miR-159	2.1	6	6	CGUCUGCCUUCCUCUGCUCCUGG	AGGACAAGGGGAAGUGCUGACACG
novel mmu-miR-160	2.1	148	148	GCCCAUGGAGCUGUAGGA	UUAAGGGCACCGUGGGGCG
novel mmu-miR-161	2.1	22	22	UUGUUUUAACUUUAUUUUACUCU	AGUAAAAUAAAGUUAAAACAAAA
novel mmu-miR-162	2	47	47	UGGGCCCUGACUCAUGCUCCACA	UGGGGGCGUGGUCUUGGGCCUCGA
novel mmu-miR-163	2	22	22	UUGUUUUAACUUUAUUUUACUCU	AGUAAAAUAAAGUUAAAACAAAA
novel mmu-miR-164	2	4	3	AGGACGUCUCUCAAAAGG	UAUUUGAAACUGAUGUC
novel mmu-miR-165	2	9	8	AAUGUAGAGUCUAUUGCU	CUAUAGACUCUUUGGUCA
novel mmu-miR-166	2	4	3	UGAUCCACAGUUGUCUUAUGACC	UCAUGGCAAGUGUGGACCC
novel mmu-miR-167	1.9	20	17	UUCCUUUCCCCACCUCCCCAGA	CCUGGGGACCUGGGGGUGAGGAAC
novel mmu-miR-168	1.9	16	16	CAUGCCGGAAGUUGUAGUUCCU	GGACUACAACUCCCAGCGGGCC
novel mmu-miR-169	1.9	16	13	CCCGGAGGCUUUGCUUCUAGCU	UAAGAGCUAGGUGCUCCAGGACU
novel mmu-miR-170	1.9	13	13	CUCCCUGCUCCCUGCCCCUAGC	UGGGGAGGAGGUAGUGGGUG
novel mmu-miR-171	1.9	26	26	ACUUUGUCCUGGCUAAUGUCACU	UGACAAUUGGGAAGGAUAGAGACU
novel mmu-miR-172	1.9	26	26	GGGCACAGCUGUGAGAGC	UGUGAGGGCUGUUUGCUCAG
novel mmu-miR-173	1.9	15	15	CUCGGCCGCCUGCCCCUUCUGC	AGGGGGGGGGGGCGCCCGCAGC
novel mmu-miR-174	1.9	13	13	UGGACCAGUGUGCAUGCAUGCA	CACGCAUGGUGCUUCUGCGUGCAAA
novel mmu-miR-175	1.9	118	118	CGGGGCCGGGCGCGCGC	GACUGGCCGGCUCCCGC
novel mmu-miR-176	1.9	10	10	GCCUGCUGGUGUGGAACCC	GAUCCCAACGGCAGGACGUCCCAG
novel mmu-miR-177	1.9	57	57	CACGAGUUGUAGGUUCUCCCC	GGAGAGUCCACUGCUCCUUGGUGGA
novel mmu-miR-178	1.8	4	4	UGCCUGGGCUAUGAUGUAGAAU	UCUACAUCAUAGCCCAGGCAGA
novel mmu-miR-179	1.8	16	16	AUGUAGACUUUCUCACAUCU	AUGUGAGAGAGUCUACACUG
novel mmu-miR-180	1.8	4	4	AUGGCAGGUAGGAUGGUC	CCGUUCACUCGGAGG
novel mmu-miR-181	1.8	33	33	CGUGACCUCUGUCUCCCUCAGG	AUGGGGAGGCUGGGUGUUAUUUG
novel mmu-miR-182	1.8	11	11	UAUCCGGGUGUCUGCAGCUGCU	CAGCUGCUGGUAUCUGGGUGUC
novel mmu-miR-183	1.8	15	15	UGGUGCCCAUGCCUCCUAGUCA	GGUAGUGGGGAUGGUGCCCAUG
novel mmu-miR-184	1.8	25	25	AUCUAGGCACCGCGCUCCCACAGG	UGUGUGGGCUGGGCUUUUGGGUGU
novel mmu-miR-185	1.8	19	18	UGGCCUCUGAGACCGGCUCCU	UACAGGUCUUGCGGGCCGGGC
novel mmu-miR-186	1.8	11	4	CUCAGCCUGAGCCGGGGU	GAGCCGGCGAGAGGUGAGC
novel mmu-miR-187	1.7	65	65	GGGCGGCGACUCUGGAC	CCGGGUGGCGACCGUG
novel mmu-miR-188	1.7	17	17	UGUGCCUUUCUCAACCACCCAGA	GGGGUGGAUGGAAGGCUGCAGG
novel mmu-miR-189	1.7	13	13	UGGCAUGUUGGUUAGGGAGGUGU	GCUUCCCUUCCCAAUGCCAGG
novel mmu-miR-190	1.7	7	1	AGGGUGGGGCAUGGUCAGGAAGG	UCACCAACCCCUUCCCACAGC
novel mmu-miR-191	1.7	3	3	CCCCUCCCGGCGCCCCCGCGC	GCGGCGGGCGCAGCGGGAGGAGGCA
novel mmu-miR-192	1.7	497	496	GAGCUUGACUCAAGUCU	AUGUCACCUCAUAGAGC
novel mmu-miR-193	1.7	587	587	UAAGUGCCUGCAUGUAUGUG	CAUGUGUGUGUGCACAUAUG
novel mmu-miR-194	1.7	22	22	AGGACUCGAGGAAUGUGUGACU	UCCACAUUCCUCCAGCCUCC
novel mmu-miR-195	1.7	115	115	CCGCGCUCUCUCUCUCU	GGAGGGGGAAUUCAGUC
novel mmu-miR-196	1.7	20	19	UCACGGAUACAGCCUCCUUUGGGA	GUAUCUGCCUGUGUCCA
novel mmu-miR-197	1.7	76	76	UGGGGCUCUGCAGACUCACC	AGAUCCUGCAGAGACCCAAG
novel mmu-miR-198	1.7	27	26	CAGAGGCCCUUGGUCUGGAGA	CCUCAGAGCAGGGUGGCCUCUUCU
novel mmu-miR-199	1.6	5	5	GCACUGUCAGCUCUGGGGC	CCUGGGUUGAUUUAUUUU
novel mmu-miR-200	1.6	10	10	UAAGUCUAGGGCUCCGCCAGC	UGGGGAGCUGGGGGCGCGGC
novel mmu-miR-201	1.6	18	17	GCAAAUAUUGCGUGGGCU	GUUACGCUUGCAG
novel mmu-miR-202	1.6	1975	1973	AAUCCGGGACGAGCCCCCA	GAGUCCUGGGAUGAGCU
novel mmu-miR-203	1.6	36	36	GUUAGUGGCAGAGCCAGGA	AAGGCUCAGGUGACUGACUG
novel mmu-miR-204	1.6	12	10	CCUCCGGGGAUAUGCUGUUUUUA	AAGCAGCAUAGCCUGGAUCAGA
novel mmu-miR-205	1.6	15	15	UGGGCACUCCUCUUUCCAGAGA	UCUGUAAUGGGAGAGGAGAGCCUGGU
novel mmu-miR-206	1.6	122	122	UUCCCAGUGCUCUGAAU	CCAGACACUGGGAGUU
novel mmu-miR-207	1.6	44	37	AGCAGAACGUGCUCGUGAGCGGCA	CGCCGGCAUGGGUACGGGUGCAUGACU
novel mmu-miR-208	1.6	12	11	UUCAUUGGAAAUCUGUCUCAGG	GACAGGAUUCCUGGAGAGGCU
novel mmu-miR-209	1.6	11	11	CUCAGACCCUCUCCUCCACAGU	UGGGGAGGCAGAGGGCUGGUG
novel mmu-miR-210	1.6	47	47	UACCCAGGGUUGUGGGCAGUGU	ACUGCUUCCUACCCAGGGUUGU
novel mmu-miR-211	1.6	43	43	UAAGAUUGUGACUUCCUCCAUG	AGGAGGGCAGCCACAGUCCUAGC
novel mmu-miR-212	1.5	14	14	UACUAUGCCUGGAAGGCACC	UGGCUUCUGUUUGCAUAGUUAUGGC
novel mmu-miR-213	1.5	17	17	CUGAAGGAGCUGGUUCU	ACACCACCUAUUGCGCAGUC
novel mmu-miR-214	1.5	11	9	ACAGAUGCCCUGUAAUUCUAAC	UGGAAUUACAAGGGUAUUUAUGA
novel mmu-miR-215	1.5	67	53	UCCUUCACUAGCUGAGACCUGA	CAACUCUGCUAGUGGAGAGACC
novel mmu-miR-216	1.5	61	36	UCCCACUUGGGCCUGUCUCCACA	CAUGGAGUAACAGGUGCUUGGUG
novel mmu-miR-217	1.5	13	12	UGACACUCAUGGCCUUUCCCCA	UUGGGAGGGUCAUGGGCAAGCU
novel mmu-miR-218	1.5	41	41	CUGGUGUUGUGAAUCAGCA	CUUGGUUCCCUGCCAGAG
novel mmu-miR-219	1.5	20	20	UAAGGGAGAAACUGACCUGUGG	ACGGUCGGCUUUUCUCUAAAC
novel mmu-miR-220	1.5	4	4	UCUCUUCCAGGCCUGUGUCC	ACACAGGACUGGGCUGGGC
novel mmu-miR-221	1.4	18	18	GCCCGAGACUGGAAGGUG	CCCCCGCUCGGGUAG
novel mmu-miR-222	1.4	28	19	UCGGCACCGACACAAGGAUCCUG	UCCCUGAUAUCGAUGCUGUGC
novel mmu-miR-223	1.4	15	15	CAUGGAGCUUUCCCAGAGACU	UCCCUAGGCAAGCUGCUGCC
novel mmu-miR-224	1.4	17	17	CCGGGCGCGCGCCGUUCCAGC	GUGAGCCGCCGCCCGCCCCCGGGC
novel mmu-miR-225	1.4	77747	77747	UCCCUGUUCGGGCGCCA	GUGAACGAGGACUGGGAAA
novel mmu-miR-226	1.4	16	16	UUCACAUCUGUGAGCUUGCACU	UGCAGCAGCUCAGACAGAUGGCAAACC
novel mmu-miR-227	1.4	545	545	CUCCACGUUGGGCGCCA	GUUCUCAACUGGAAGCA
novel mmu-miR-228	1.4	16	16	CUGAGGGCGGCAGGGAGC	UCCCUCUACUGCCCAGUC
novel mmu-miR-229	1.4	18	18	UGCUCCCGUCUCUCUCCACAGC	GGUGGGGAGGACCCGGGCAGC
novel mmu-miR-230	1.3	90	90	CUCCUGGCUGGCUUACC	AGAGCCUGUCAGGAGAC
novel mmu-miR-231	1.3	11	9	UUUUCGGACGCUGUCACU	GUGGCUGAGGCCUGGAG
novel mmu-miR-232	1.3	11	11	CUGGAGUCGGAGCCCGAG	UGGAUUCGGCUGUCAGCG
novel mmu-miR-233	1.3	4	4	AUCUGGGAGAAAAAUUCAUC	UAAAUUUUCCUCCCAGAUGU
novel mmu-miR-234	1.3	218	218	ACACUCUCUGCUGAGCUCACU	UAGAGCUUUCCCUGAGGGUAGGG
novel mmu-miR-235	1.3	105	105	UAGAGCAAUGUAGCUGGCAGUC	CUGAAUCAUCUACAUUGAUUCUUGG
novel mmu-miR-236	1.3	11	7	UUGACGGAUCCGGAACCU	GCUGGGUCCCUCUGCCCC
novel mmu-miR-237	1.3	1969	1969	AAUCCCGAACGAGCCCCCA	CUCACUCAAGUCUCGGGAACUUUG
novel mmu-miR-238	1.3	51	51	AUGGCCUUACCCUUCCUGAAGC	GCAAGGAGGGUGGGUCAUGC
novel mmu-miR-239	1.3	139	139	UAUAGACCUGUAUAGCUAUCU	AUCUAUAUAGGUCUGUAUA
novel mmu-miR-240	1.3	11	1	UCGUGUACUUCACUGCU	CAGGAGGCAUCUUA
novel mmu-miR-241	1.2	27	27	AUCCUUUCAAAGGCUAGACCU	GUCCAGCCCUUGUAAGGGAAGC
novel mmu-miR-242	1.2	61	59	UGGCUCAGACCAGCAGGAAC	UCAGACUGGCUGUGGAGUUAGU
novel mmu-miR-243	1.2	25	25	CCUGAGGUGGUGGAACCU	GUGCUGUUUUAGGGG
novel mmu-miR-244	1.2	11	9	UCCACACUGUGCCUGACCUGUU	UCUGGCAUUACUGUAGAGCAUG
novel mmu-miR-245	1.2	501373	501367	UCCCGGGUUUCGGCACCA	UCCGGAAUGAGGGAUCUUCCU
novel mmu-miR-246	1.2	434	434	CGGCGGCGGCGGCGACC	CUGCCUGCUGACUUCCGUU
novel mmu-miR-247	1.2	11	11	UUGCUCAAUCUCGUUGUCACU	UGACAACGAGAUUGAGCAAAA
novel mmu-miR-248	1.2	748	748	CUCGGGUUUCGGCACCA	GUAACCGUCCCGGGUU
novel mmu-miR-249	1.2	5	5	UGAGAACUCUGGACAGUGAGUU	AUCACUGUCUUUGGAACUGCAGA
novel mmu-miR-250	1.2	96	96	GCUACAUUGUCUGCUGG	AGCACAAGCCGCCU
novel mmu-miR-251	1.2	748	748	CUCGGGUUUCGGCACCA	GUAACCGUCCCGGGUU
novel mmu-miR-252	1.2	748	748	CUCGGGUUUCGGCACCA	GUAACCGUCCCGGGUU
novel mmu-miR-253	1.2	21	21	CAUCAUUGGUGAGGAGAA	CUCUUCCCAAGCAGGUG
novel mmu-miR-254	1.1	35	35	CUCCUGGAGCUGAGAGGU	GGUUUAGCUAAAGGCCAG
novel mmu-miR-255	1.1	249	244	UGCCAGACAGGUACACAGUCUCU	UGCCCCUGUGUCCUGUCUGUAG
novel mmu-miR-256	1.1	119	119	UCGGAUCCGUCAGCUUGG	UAGUUGAUUGCCUCAGAGC
novel mmu-miR-257	1.1	11	11	UCCAGGACUCUCCAACUGCC	CAGAAGGGAAGGGUGCUGGAGU
novel mmu-miR-258	1.1	17	15	ACCGAGAAGACUAGGGGA	CCAUAGCCUACCGGAUU
novel mmu-miR-259	1.1	13	13	ACCUGUGGUGGCUGCAAG	UGUGGAGGCUGCAAGGGAG
novel mmu-miR-260	1.1	4	4	AGGGGCAGUAGGAAGGCU	CCUCCUGCAUGUCACACC
novel mmu-miR-261	1.1	17	17	UCUCCAGCUCUGCACUGCAAGA	CGGCUUUGCAGAGCUGCGAUUCA
novel mmu-miR-262	1.1	5	5	CACACAAGAGCCUUGAU	CAGGUGAUUGUGGGA
novel mmu-miR-263	1.1	17	17	UCCUUGGACAAAGAAGAAC	UCAUGUUUGUCCCCUAGCC
novel mmu-miR-264	1.1	24	24	UGGCAAGAUGCCCUGAUU	UGAGGUCAUUGUGCCACA
novel mmu-miR-265	1.1	12	12	UCCAUCGGUCUGACAGACUAGC	CUGUCUCAGACUGCUGUGAAU
novel mmu-miR-266	1.1	51	51	UAGUCCAUCUUUGCACCCUCAGG	UGGGGGUUCAAGGAUGGGGGAAU
novel mmu-miR-267	1.1	49	49	UCCUUCCCAUCUGCUCUGCAGG	CGCGGCGCGGAGACCUGGGGGUGGCA
novel mmu-miR-268	1.1	140	140	UGAGAUGAAACACUGUAGC	UGAGUUUUAUACUUGGU
novel mmu-miR-269	1.1	5	5	AGGGGGUGGGGGGUUUGGA	CAGCAACCUCAUCAACGGG
novel mmu-miR-270	1	424	422	GUAGAUGUUCCUUCUAUGGU	CAUGGUGGAUGCUUCUCCU
novel mmu-miR-271	1	38	28	UUCCUCAUUCUACCUCCCAGG	AGGGGGAGAGAAAAUGAGGAAGA
novel mmu-miR-272	1	35	35	AGGCUGCAGGCCCACUUC	GGUCAGGCCAUGGGAGGCUUU
novel mmu-miR-273	1	11	11	ACUACCCACUUCCAUCUCCACAGC	UGGGGAGGUGGGAGGGAUAGCUGA
novel mmu-miR-274	1	5	5	ACUUCACCCUCCUGAAA	UCAGGAAGCUGAGGUGC
novel mmu-miR-275	1	205	205	UUCCCAGCCAACGCACCA	GUGUGAGGGGUGGUCGAG
novel mmu-miR-276	1	6	5	UACGGUCCGGCGCCGCGCGG	GUCGCGGUCGUCGCCGGG
novel mmu-miR-277	1	11	11	CCUACACAGGACCUCUUGGCU	CCAGGAGUUGUCUGUGGGGAC
novel mmu-miR-278	1	33	33	UUGCUCUGUGCUGUGGAUCAGG	UGAGCCUCUGGAGAGCAAGG
novel mmu-miR-279	1	50	50	AAGGCUGGGGAGAGGUUGGG	UAGCAGAACUCAGCAUCU
novel mmu-miR-280	0.9	12	11	UUACUCCUGCCCCUCUACUCCAGU	UGGUUUGGAGGGAGGGAAAAGA
novel mmu-miR-281	0.9	187	187	UAAGGUUUGGCUCUAAG	CUGAGCCACCUCACC
novel mmu-miR-282	0.9	33	33	UCCUGAGGUUGUUGAGCU	CUCAGAAUGCAGUAGG
novel mmu-miR-283	0.9	25	25	CCAGCCACCCGCCACUGCA	CAAUCCAGUGGUGAGCUGACA
novel mmu-miR-284	0.9	7	7	UCCCUGGGCCUGUGUCUU	GACAAAUGCCCAUGGAGA
novel mmu-miR-285	0.9	2349	2349	CCUAGUCCUAGCCCUAGCCC	ACUAGCACUAGGACUAACAC
novel mmu-miR-286	0.9	77	38	CAGAGUCCAGUCCCUUU	GCAGGCAGAUCUCUGAGU
novel mmu-miR-287	0.9	112	112	UCUCUGAGACCCUUUAACCC	GACCAGGGGUCUGCAGGUAAUA
novel mmu-miR-288	0.9	16	16	UCCAGGGAGGCACAUGAGCAG	GUCUCAAGCGUGAUAGGAAU
novel mmu-miR-289	0.9	101	100	GCCCAUGGAGCUGUAGGA	CAACAGCCUUCUCAAGUGA
novel mmu-miR-290	0.9	28	11	UGGGAGAGCCGGUACCUUUCUGU	UGAGAGCUAGUGGUUUUCCCU
novel mmu-miR-291	0.9	3	3	CUGGACGGCGCUUGCACC	AGCAAGGUAGCUGCAGUG
novel mmu-miR-292	0.8	3	3	UCGGAGAGACUCUGGGGU	CGCCGGAGCCACCUUUGACC
novel mmu-miR-293	0.8	2768	2763	UCCCUGAGACCCUUUAAC	CAGAGGUGAGGGAGA
novel mmu-miR-294	0.8	13	13	UGACCCCCUCCCCCACUCCAGA	UGGGCUGAGGGUGGGGAGUCCCU
novel mmu-miR-295	0.8	11	9	UGGUUUUGCAUCUCUCUAC	UUUGAGAGGGUCUAAGCCAAU
novel mmu-miR-296	0.8	932	932	CCUCAGAGAAGGCACCA	GGCAUGGCGACGGGGCA
novel mmu-miR-297	0.8	481	481	AUUAGAGUAGCAGAGCC	CUGAGUUCACAAAGUAG
novel mmu-miR-298	0.8	3	3	UCUGACACUGUUGUUCCCGUCU	GAGGGAGCACUGGGGUGUCAGGUG
novel mmu-miR-299	0.8	19	16	CAGGAGGCGCACACAGAA	UUGACGGCUGUCUCCAGCC
novel mmu-miR-300	0.8	89	89	AAUCCGUCCUCCCUAUCCCCAGG	UGGGGGCCUGGGAAUGGCUUUGG
novel mmu-miR-301	0.8	10	10	CACAGAUCCAUGGGACCUCCAAGG	GUGGGAGUCCCUGGGUCUGUUUC
novel mmu-miR-302	0.8	3	3	ACACCCUCUGGAGGUGACUUUCU	GGAGUUCCCCACAGAGCUGGUCC
novel mmu-miR-303	0.8	38	38	UGACUGCCUUCCCUCUGCCCAGC	UGAGCCCUAGACUCCCAGGCACUCCCU
novel mmu-miR-304	0.7	448	448	UCUCUCCAGCCACCUUU	AGGGAGUCUGGAGGAAGU
novel mmu-miR-305	0.7	18	18	AUGCCCAUUUUCUUCCACUGCUG	GCAGUGGGCAUUUGGGUGCCA
novel mmu-miR-306	0.7	9	9	UCCCCCAACACCCACCUUGCC	CGAGGUAGGAGUGGGUGGUGC
novel mmu-miR-307	0.7	857	853	AUCUGAAGGUCCUGAGU	AGGGGCUGGAAAGAUGGC
novel mmu-miR-308	0.7	39	39	UGAAGGACCAUGUAGGCUUU	GGCCUCUGUGGUUAUACUGU
novel mmu-miR-309	0.7	9	2	GUUGCCAGGGAGAAAUCUACU	AGUAGAUUUCUCCCUGGCAACU
novel mmu-miR-310	0.7	10	10	UCUGGGCGGAAUUCAGUUUUU	AAGCUGAAUCUGAUGCCCAGAGC
novel mmu-miR-311	0.7	339	339	GAUCUCCGUGGGACCUCCA	GGGGUUCCUGGGUGUCAC
novel mmu-miR-312	0.7	18	13	ACCAUGUUCUGUCAGGUCU	UGGCAUAUAGGUGACAA
novel mmu-miR-313	0.7	8	6	UUAUUCAUCCUGUAUCUGGUAGG	UUCUGGAACAGGUGGAAGC
novel mmu-miR-314	0.6	36	36	UCCCUCAGACCCUAACUU	GUUUGGGGUGAGGUGGGACC
novel mmu-miR-315	0.6	27	25	AAUCACCCUGUCCUCUCUCAGA	CCUGAGAGGCAGGUGUGGCAUU
novel mmu-miR-316	0.6	12	12	GGUGCCUGUGAAUCCUUCC	AAGGGGACUGUCCUG
novel mmu-miR-317	0.6	3	3	UUUGCCAUCCCCAUCCCAACU	UUGAGGUGUGGGAUGGCAACC
novel mmu-miR-318	0.6	15	14	AUCUCUGGAGCCUGAAUU	UGAAGCUCGUGAGGUGA
novel mmu-miR-319	0.6	15	15	UUCCUUGACAACUACUGUAGA	UUCCAAAGGGGAUGUCGGGAAAA
novel mmu-miR-320	0.6	41	41	UUCUGAGAAUUCUGUGUAACUGG	UUCCUCACACCGUUUCUCAGGUUGGU
novel mmu-miR-321	0.6	9	9	AGGGCCGUCCACUCUGCUGACC	AGGGCAGAGUGGACAGUGUUCC
novel mmu-miR-322	0.5	16	16	UGUCUCUCCAGUCACCUU	GGAAGGCGAGAUACC
novel mmu-miR-323	0.5	9	9	UUCAUCCACCAGCCCUGCCACU	AGGCAGGGUCUCGUGGGUGUUGU
novel mmu-miR-324	0.5	12	12	CAAGCACCAGAUGUUCUCUUGC	CAGGGAGCCUCUGGUGAACUCGGG
novel mmu-miR-325	0.5	3	3	UUGUGGCUCUGUUUGACU	UCACCUACAGGGUUCGUAAG
novel mmu-miR-326	0.5	27	27	AUCCCAGCGGGGCUUCCA	GACUUGGCCCUUUUGACAAAC
novel mmu-miR-327	0.5	13	12	UCCAUUGGCUGUUUGAAGA	AUGGCCAGUGAUCCUCAAA
novel mmu-miR-328	0.5	35	35	GCUUUCCCGGGCUUGCU	CUAAGCCCUAGCAC
novel mmu-miR-329	0.4	140	140	AUGCAUGGAUUUGGAUU	UCCCAUGCUAGAGCAAAC
novel mmu-miR-330	0.4	122	29	AGGCAUUGCCAUAGAACU	UUCCCAGUCCUGG
novel mmu-miR-331	0.4	11	3	UGGCUGUUGGAGUGAAGCU	CUCCCAACGUGUUGGC
novel mmu-miR-332	0.4	17	17	GUUCCACCUGGGGUACCA	GUAUUCCCUCCAGGAAGCC
novel mmu-miR-333	0.4	57	56	GCAGCGCAGAGCAGAAAGCAA	CCCUGCGCUCUUUCCUG
novel mmu-miR-334	0.4	62	62	UUGCAAGCAACACUCUGUGG	ACAAUUUGAGCUUGCUAUA
novel mmu-miR-335	0.4	3	3	GAAAUGAACCUGUCCCUG	GGGUAGGUGGCUCUUUCAG
novel mmu-miR-336	0.4	11	11	CAUAGAUCUUGGCAUGAAG	UAGUGCAGAUCUCCAGG
novel mmu-miR-337	0.4	542	542	UUCCCAGCCAAUGCACCA	AUGCAGUGUCUGGGUCCU
novel mmu-miR-338	0.4	3	3	CAGAGGGACAGGAAGGGC	ACUUCCUGGCUGCUCUGUU
novel mmu-miR-339	0.3	9	9	AGCAUGGCUGCUUGUGACACU	UGUCUCCAAGGCCAGGCUGC
novel mmu-miR-340	0.3	25	15	CUACUAGACUGUGAGCUUU	AGUGUGGCCUCCAGAGC
novel mmu-miR-341	0.3	30	30	UGGCUCAGUUCAGUAGGGAG	CCCUGGGGAGGUGGCCAUG
novel mmu-miR-342	0.2	9	9	UUGGCCACGGCUGUCCCCGAGG	UGGGGGGUGGCUGGAGAGCGGAGG
novel mmu-miR-343	0.2	10	10	CUCCUUGGCUGAGUUUACC	GAAUCAUAGUUUUAAGGGGCU
novel mmu-miR-344	0.2	17	17	AGAGGCUUAUAGCUCUAA	AUGGCUCCUAGCCAUCAGA
novel mmu-miR-345	0.2	12	12	AGGCUGUGACUCUGGCAC	ACCCGAGUCCAGGUCAGA
novel mmu-miR-346	0.2	40	40	CCAGACUGAGGCUCCUUGG	AUCUUGCCUCGGUAACAAGUGGAG
novel mmu-miR-347	0.2	14	13	GCAAAGCACAGGGCCAGCAGC	UGGCCUGAGUGGUGUACU
novel mmu-miR-348	0.2	4	4	UGCCUGGGCUAUGAUGUAGAAU	AGUAUAAUGUAGUCCCUUAGGCAGC
novel mmu-miR-349	0.2	13	13	CAGGAGCUGUAUGCCACC	UGAGCAGUACAGCAAGCA
novel mmu-miR-350	0.2	3	3	GAGCCCCCUGUGGAUCCU	GUUCCAACAAGGUGAGG
novel mmu-miR-351	0.2	4	4	AGAGAUGCAGUCAGCAGA	UGCCCUGCUGCUUCUUUUC
novel mmu-miR-352	0.1	3	3	ACAGGACAUGGUGAGUCACACCA	GUAGCUCACCCUGUCCUUCUU
novel mmu-miR-353	0.1	10	10	AAUCUUGUUUGGCAGAAUGGU	CAGUUCUGUCACUAAGGACUUCC
novel mmu-miR-354	0.1	6	5	UGCGGACCCUCAGCCUGAGC	CUGGCUGGGGCUCCGCC
novel mmu-miR-355	0.1	5	4	ACAGUCAGCCUGAUUCCU	UGAGUCUUUUGUUGACA
novel mmu-miR-356	0.1	6	6	AAGGAGUCUGCUUGCUUAC	GAGUGUUUUCCCCUUUC
novel mmu-miR-357	0.1	13	13	CCGACUGUGGACAGCUCU	UCCUUCUGCAGCUCAGGAG
novel mmu-miR-358	0.1	76	76	UGGGGCUCUGCAGACUCACC	CUUGUCCGCGACUGAGACCCCGAU
novel mmu-miR-359	0.1	10	5	UGGGUAACACAGCUGGAUGCAG	UGCAUUUGUCUCUGUUCCU
novel mmu-miR-360	0.1	244	243	ACACAGUGAACCUGUCUCAU	GUAUUGUGUUUGUGUGUAU
novel mmu-miR-361	0.1	9	9	GUUCGGAGACUCCACGGAGAGG	UCCCUGGAGGCCCCGAGCCC
novel mmu-miR-362	0.1	9	6	GAGCAAGCUGCAGGAGCCGUAGAAU	UUCUACCUCCUGUAUUUUCU
novel mmu-miR-363	0	11	11	GAAGGCAUCCUAGAAUCUCUC	GGGAUUGUCUAGGUUGCCUACAU
novel mmu-miR-364	0	131	131	UGUAGGAACCCUAAACC	CCAAGGGUUUAUUCCUACUCC
novel mmu-miR-365	0	9	1	CAGGAGCUUGUGGCGUC	GUCCCCACGCUCCAGCC
novel mmu-miR-366	0	9	6	ACUGAGCUUCACAGAUUGAAC	CUCCAAUCUGAGUGGCUCAUGG
novel mmu-miR-367	0	794	793	UCCCGGGUUUCGACACCA	GUAACCGUCCCGGGUUU
novel mmu-miR-368	0	10	10	AGGACCAGAAAGUUUACAUUUCU	AGAUGUAAACGUCUGGCCCUGC
novel mmu-miR-369	0	20	20	CAGCCCAUCGACUGCUGUUGCC	CAACAUCAGUCUGAUAAGCUAUC
novel mmu-miR-370	0	167	167	UGGCUCAGUUCAGAAGGAA	CCUGACAAGUCCACC
novel mmu-miR-371	0	1	1	CUGAGGAGCCACGGAAGC	UUCCGUGGGGUAGAC

APP: Amyloid-beta peptide precursor protein; miRNA: microRNA; PS1: presenilin 1; WT: wild type.

Figure 1.

Volcano plots of the differentially expressed miRNAs in the APP/PS1 mouse cortices at 1 (A), 3 (B), 6 (C), and 9 months (D) of age, in contrast to the corresponding control ones.

Red triangles indicate up-regulated miRNAs, green triangles indicate down-regulated miRNAs, and purple dots indicate non-significant miRNAs. APP: Amyloid-beta peptide precursor protein; miRNA: microRNA; PS1: presenilin 1; WT: wild type.

Table 2.

Dysregulated miRNAs in the AD mouse cortices (vs. WT control mice) at 1, 3, 6, and 9 months of age

miRNAs name	Fold change	P-value	Regulated	miRDeep2 score (percent identity %)
1-mon-old
mmu-miR-10a-5p	0.39856	0.025366	Down	99.08
mmu-miR-3093-3p	0.459885	0.01669	Down	–
mmu-miR-361-5p	0.302294	0.005419	Down	91.43
mmu-miR-6966-5p	0.180847	0.006352	Down	–
mmu-miR-7661-3p	0.131742	0.021494	Down	–
mmu-miR-296-5p	6.062553	0.022866	Up	93.65
mmu-miR-351-5p	6.089575	0.024571	Up	–
mmu-miR-384-3p	2.014569	0.007307	Up	–
novel mature mmu-miR-100	7.256074	0.048437	Up	4.6
novel mature mmu-miR-211	7.235458	0.00804	Up	1.6
novel mature mmu-miR-333	6.877201	0.034028	Up	0.4
3-mon-old
mmu-miR-10a-5p	0.405847	0.000407	Down	99.08
mmu-miR-190b-3p	0.206511	0.038593	Down	92.11
mmu-miR-466q	0.050676	0.029731	Down	–
mmu-miR-706	0.13551	0.021947	Down	–
novel mature mmu-miR-196	0.206507	0.011726	Down	1.7
novel mature mmu-miR-281	0.364672	0.003254	Down	0.9
novel mature mmu-miR-29	0.240416	0.012949	Down	23.1
novel mature mmu-miR-3	0.39958	0.014653	Down	6489
mmu-miR-1912-5p	15.47854	0.036518	Up	88.75
mmu-miR-3572-3p	17.92218	0.041076	Up	–
mmu-miR-6932-5p	22.41296	0.010802	Up	–
novel mature mmu-miR-15	5.590053	0.000187	Up	3.9
novel mature mmu-miR-271	5.633821	0.039076	Up	1
6-mon-old
mmu-miR-706	0.208995	0.00329	Down	–
mmu-miR-96-5p	0.094653	0.002382	Down	97.44
novel mature mmu-miR-102	0.044095	0.028993	Down	4.6
novel mature mmu-miR-125	0.1863	0.022475	Down	3.6
novel mature mmu-miR-304	0.414379	0.007412	Down	0.7
novel mature mmu-miR-308	0.042078	0.007816	Down	0.7
novel mature mmu-miR-7	0.490183	0.000748	Down	1045
novel mature mmu-miR-80	0.063557	0.017132	Down	5.2
mmu-miR-3110-3p	16.1709	0.037245	Up	–
mmu-miR-466g	35.0722	0.002431	Up	–
mmu-miR-7093-3p	18.12043	0.046436	Up	–
novel mature mmu-miR-293	2.306658	0.008196	Up	0.8
9-mon-old
mmu-miR-144-3p	0.328517	1.72E-05	Down	98.48
mmu-miR-144-5p	0.267881	2.64E-09	Down	98.48
mmu-miR-181c-3p	0.056882	0.041915	Down	97.75
mmu-miR-1960	0.151435	0.041338	Down	–
mmu-miR-451a	0.380303	4.38E-05	Down	96.83
mmu-miR-7651-5p	0.189649	0.01575	Down	–
novel mature mmu-miR-143	0.034577	0.009456	Down	2.8
novel mature mmu-miR-80	0.014909	0.004011	Down	5.2
mmu-miR-10b-3p	3.781309	0.006919	Up	95.59
mmu-miR-192-3p	7.382986	0.036387	Up	95.29
mmu-miR-1957a	3.65399	4.95E-06	Up	–
mmu-miR-211-5p	3.332832	2.58E-05	Up	86.96
mmu-miR-214-3p	2.298404	0.049632	Up	100
mmu-miR-215-5p	68.16251	4.10E-06	Up	84
mmu-miR-223-3p	2.556458	0.010859	Up	91.74
mmu-miR-297a-5p	7.555543	0.037031	Up	–
mmu-miR-3470a	2.79206	0.000446	Up	–
mmu-miR-3470b	2.816997	0.001186	Up	–
mmu-miR-3473e	28.49542	0.009351	Up	–
mmu-miR-378d	2.618788	0.025701	Up	–
mmu-miR-6481	153.2064	4.66E-11	Up	–
mmu-miR-690	4.710149	1.54E-08	Up	–
novel mature mmu-miR-207	28.3406	0.002838	Up	1.6
novel mature mmu-miR-254	6.047508	0.007423	Up	1.1
novel mature mmu-miR-297	7.48741	3.48E-06	Up	0.8
novel mature mmu-miR-3	61.68188	7.66E-61	Up	6489
novel mature mmu-miR-329	12.57373	0.009557	Up	0.4
novel mature mmu-miR-332	2.490143	0.041352	Up	0.4
novel mature mmu-miR-35	11.96082	0.002814	Up	16.3
novel mature mmu-miR-364	28.20909	4.49E-09	Up	0
novel mature mmu-miR-7	4.172186	1.41E-07	Up	1045
novel mature mmu-miR-9	14.27648	1.01E-12	Up	329.6

AD: Alzheimer’s disease; APP: amyloid-beta peptide precursor protein; PS1: presenilin 1; WT: wild type.

Figure 2.

Venn diagram of the aberrantly changed miRNAs in the APP/PS1 mice at 1, 3, 6, and 9 months of age.

The number of overlapping and expression-changed miRNAs at each stage of Alzheimer’s disease is shown. Purple, yellow, green, and red colors represent the differentially expressed miRNAs in the APP/PS1 mouse cortex at 1, 3, 6, and 9 months of age. APP: Amyloid-beta peptide precursor protein; miRNA: microRNA; PS1: presenilin 1; WT: wild type.

Figure 3.

Hierarchical cluster analysis of differentially expressed miRNAs in APP/PS1 mice at 1 (A), 3 (B), 6 (C), and 9 months (D) of age.

The expression cluster tree is shown on the left, while the sample cluster tree is shown on top. The relative level of miRNA is shown by the color change, whereby red indicates high expression and blue indicates low expression. APP: Amyloid-beta peptide precursor protein; miRNA: microRNA; PS1: presenilin 1; WT: wild type.

Prediction of the targets of the significantly expressed miRNAs

The sequence conservation of the differentially expressed miRNA from the cerebral cortex of the APP/PS1 mice was analyzed. Additionally, according to previous reports, a higher miRDeep2 score was taken to indicate a more reliable identification of novel miRNAs (Friedländer et al., 2008; Sand et al., 2016). Accordingly, these dysregulated miRNAs were filtered using a percent identity ≥ 80% of known miRNAs and an miRDeep2 score ≥ 4 of novel miRNAs as the cut-off values (Table 2). A total of 8 novel miRNAs and 15 conserved and known miRNAs were obtained, which were further selected to predict potential mRNA targets. A total of 21,322 putative target genes were intersected with these selected miRNAs using the above algorithms. Notably, 12 highly conserved known miRNAs and 3 novel miRNAs with the corresponding target genes were tightly connected with AD pathogenesis, including miR-10a-5p, miR-10b-5p, miR-96-5p, miR-144-3p, miR-192-3p, miR-211-5p, miR-214-3p, miR-215-5p, miR-223-3p, miR-361-5p, miR-296-5p, miR-451a, novel mature miR-29, novel mature miR-80, and novel mature miR-102. Their target genes were found to be specific and closely related to AD, and included APP, beta-secretase 1 (BACE1), ADAM metallopeptidase domain 10 (ADAM10), insulin-like growth factor 1 (IGF1), autophagy related 12 (ATG12), brain-derived neurotrophic factor (BDNF), B-cell lymphoma 2 (BCL2) apoptosis regulator, and the limb-bud and heart (LBH) regulator of the wingless-type (WNT) signaling pathway (Table 3). Subsequently, the miRNA-mRNA interaction was merged to construct a network. mRNAs and miRNAs were defined as the network nodes, and an edge was added between nodes that interacted with each other. This intersected network contained 25 mRNAs and 15 miRNAs. Thus, based on the interaction network (Figure 4), we constructed the cerebral miRNA-mRNA network associated with the pathological AD process.

Table 3.

Significantly changed miRNAs and their predicted target genes, including miRNAs name, sequence, and target genes

miRNA name	Sequence (5′–3′)	Target genes
miR-10a-5p	CAA AUU CGU AUC UAG GGG AAU A	BDNF, PTEN, LBH, ABI3
miR-361-5p	UUA UCA GAA UCU CCA GGG GUA C	ADAM10
miR-296-5p	AGG GCC CCC CCU CAA UCC UGU	ADAM12, PKM, PIN1, RAB4A, BSN
miR-96-5p	UUU GGC ACU AGC ACA UUU UUG CU	QKI, BCL2
miR-144-3p	UAC AGU AUA GAU GAU GUA CU	QKI, BCL2
miR-451a	AAA CCG UUA CCA UUA CUG AGU U	BCL2, ADAM10
miR-10b-5p	UAC CCU GUA GAA CCG AAU UUG UG	ATG12, PTEN
miR-192-3p	CUG CCA AUU CCA UAG GUC ACA G	IGF2, ADAM10
miR-211-5p	UUC CCU UUG UCA UCC UUU GCC U	BDNF
miR-214-3p	ACA GCA GGC ACA GAC AGG CAG U	ATG12, QKI, ABCA1
miR-215-5p	AUG ACC UAU GAU UUG ACA GAC	IGF1
miR-223-3p	UGU CAG UUU GUC AAA UAC CCC A	ATG7, BACE1, IGF1
miR-190b-3p	ACU GAA UGU CAA GCA UAC UCU CA	–
miR-181c-3p	ACC AUC GAC CGU UGA GUG GAC C	–
miR-1912-5p	UGC UCA UUG CAU GGG UGU GUA	–
novel mature miR-100	UAG CAC AAU GUG AAA AGA GCU CC	–
novel mature miR-29	UCU CUU CUG CUC UGU GUC ACA GC	DUSP1, CASK, ANK2
novel mature miR-3	UGA CUU CCA AUU AGU AGA U	–
novel mature miR-102	UCC UG UAG CCA GCA UAG UGC	NDUFA9, IRF5
novel mature miR-7	AGG CUA GGC UCA CAA CC	–
novel mature miR-80	GGG GAA UGU GGC UCU UGC C	HTR6, SLC14A1
novel mature miR-35	UAG AAU UAG CUU CUG CC	–
novel mature miR-9	AAA AGA AUU ACU UUG AU	–

miRNA: microRNA.

Figure 4.

The miRNA-mRNA network of the significantly expressed miRNAs and their corresponding targets genes in the APP/PS1 mouse cortices at 1, 3, 6, and 9 months of age in contrast to the corresponding control ones.

APP: Amyloid-beta peptide precursor protein; miRNA: microRNA; PS1: presenilin 1.

GO and KEGG pathway analysis of the dysregulated miRNAs

The dysregulated miRNAs led to a change of gene expression, which resulted in activation of the pathological signaling pathway that causes AD. Figures 5 and 6 display the top 10 GO enrichment domains of the target genes of the significantly dysregulated miRNAs; the biological process terms of GO enrichment were involved in gene silencing by miRNAs development, transcription DNA-template development, nervous system development, and phosphorylation development. Moreover, identification of the top ten cellular components indicated that the differentially expressed miRNAs were located at the synapse, in the nucleus, and on the cell membrane. Furthermore, the molecular function terms revealed a role of the top ten molecular functions in protein phosphatase binding, actin binding, and activating transcription factor binding. The KEGG pathway analysis also revealed an involvement of mitogen-activated kinase protein (MAPK), phosphatidylinositol 3-kinase-protein kinase B (PI3K-AKT), mechanistic target of rapamycin kinase (mTOR), forkhead box O (FOXO), axon guidance, olfactory transduction, pancreatic secretion, neuroactive ligand-receptor interaction, and autophagy in AD, as shown in Figures 7 and 8. The GO top enrichment network (Additional Figure 3 ^{(3MB, tif)} ) and pathway top enrichment network (Additional Figure 4 ^{(2.9MB, tif)} ) were established to clarify the potential regulatory mechanism in the pathogenesis of AD, as well as the interaction between miRNAs and mRNAs.

Figure 5.

Top 10 GO enrichment analysis of the target genes of the down-regulated miRNAs in the APP/PS1 mouse cortices at 1 (A), 3 (B), 6 (C), and 9 months (D) of age.

The blue, green, and yellow bars represent the molecular function, cellular component, and biological processes. APP: Amyloid-beta peptide precursor protein; GO: gene ontology; miRNA: microRNA; PS1: presenilin 1; WT: wild type.

Figure 6.

Top 10 GO enrichment analysis of the target genes of the significantly increased miRNAs in the APP/PS1 mouse cortices at 1 (A), 3 (B), 6 (C), and 9 months (D) of age.

The blue, green, and yellow bars represent the molecular function, cellular component, and biological processes. APP: Amyloid-beta peptide precursor protein; GO: gene ontology; miRNA: microRNA; PS1: presenilin 1; WT: wild type.

Figure 7.

Top 10 KEGG pathway analysis of the target genes of the significantly decreased miRNAs in the APP/PS1 mouse cortices at 1 (A), 3 (B), 6 (C), and 9 months (D) of age.

The gene count represents how many target genes were present in each pathway, while the gene ratio was equal to the ratio of the number of target genes divided by the quantity of all genes in each pathway. APP: Amyloid-beta peptide precursor protein; KEGG: Kyoto Encyclopedia of Genes and Genomes; miRNA: microRNA; PS1: presenilin 1; WT: wild type.

Figure 8.

Top 10 KEGG pathway analysis of the target genes of the significantly increased miRNAs in the APP/PS1 mouse cortices at 1 (A), 3 (B), 6 (C), and 9 months (D) of age.

The gene count represents how many target genes were present in each pathway, while the gene ratio was equal to the ratio of the number of target genes divided by the quantity of all genes in each pathway. APP: Amyloid-beta peptide precursor protein; KEGG: Kyoto Encyclopedia of Genes and Genomes; miRNA: microRNA; PS1: presenilin 1; WT: wild type.

Discussion

No cure for AD is available, and there is not yet an effective treatment to inhibit or slow its progression; however, some options are available to treat the symptoms of AD. Growing evidence has indicated that the best way to avoid AD or delay symptom onset effectively is prevention and early diagnosis, rather than treatment (Dolgin, 2018). To this aim, increasingly efficient and accurate sequencing technology has been used to help identify biomarkers and differentially expressed genes that are related to the pathogenesis of AD. miRNAs are negative regulators of genes, widely distribution throughout body tissues, and easy to detect, and regulate genes at a transcriptional and post-transcriptional level to reduce gene silencing. Increasing evidence has revealed that the dysregulation and alteration of miRNAs can lead to the occurrence of AD (Silvestro et al., 2019). The identification of molecular biomarkers represented by aberrantly expressed miRNAs and mRNAs could facilitate the diagnosis and prognosis of AD before the onset of the symptoms; thus, they could be potential drug targets.

APP/PS1 transgenic mice simulate human AD-like senile plaques symptoms by overproduction of Aβ, the expression of which increases with age (Sun et al., 2019). Thus, these mice are widely used to investigate the occurrence and development of AD. The cerebral cortex and hippocampus regulate learning and memory (Rakic et al., 1994). The entorhinal, prefrontal, and temporal areas are cerebral regions that are affected during the early stages of AD (Gaffan, 2002), and play an indispensable role in the cortex-hippocampal circuit for memory and learning formation. Therefore, the integrated cerebral cortex was used to identify aberrant genes and biological pathways related to neuropathological changes in AD.

In this study, 129 significantly changed genes were identified in APP/PS1 transgenic mice, consisting of 78 up-regulated and 51 down-regulated genes. The top ten most significantly changed genes were up-regulated, such as Lamr1-ps1, S100a8, S100a9, Cst7, Ccl3, AC147560.1, and Gm22133, while the down-regulated ones were Gm27505, Hist2h2aa1, and mt-Ts2. However, to the best of our knowledge, this is the first report to link Lamr1-ps1, AC147560.1, Gm22133, Gm27505, Hist2h2aa1, and mt-Ts2 genes with AD. Among the genes known to be altered in AD, S100a8 has been found to be up-regulated with age (Lodeiro et al., 2017), which was corroborated by our high-throughput results. S100a8 also plays a crucial regulatory role in inflammatory processes that occur prior to Aβ over-aggregation resulting from the positive feedback of Aβ production, which leads to memory and learning impairment (Lodeiro et al., 2017). Similarly, S100a9 drives an amyloid-neuroinflammatory cascade in the precursor phase of AD (Wang et al., 2018). Microglia-associated amyloidosis may play a pivotal role in AD pathology (Ofengeim et al., 2017). In AD and similar pathologies, Cst7 encodes cystatin F, which acts as a cellular marker of disease-associated microglia, and is responsible for the uptake of extracellular Aβ through autophagic and lysosomal pathways. Thus, Cst7 indirectly inhibits excessive Aβ deposition.

A total of 68 miRNAs were significantly dysregulated, including 39 up-regulated and 29 down-regulated miRNAs. For the first time, this study identified 25 novel aberrant miRNAs in the cerebral cortex of APP/PS1 mice, which were dysregulated at different stages of AD. Among these miRNAs, miR-10a-5p was down-regulated in the APP/PS1 mouse cortex at 1 month and 3 months old, which indicates that it is involved in the early pathogenesis of AD. Moreover, miR-10a-5p has an anti-inflammatory effect by simultaneously binding to two target genes, the mitogen-activated protein kinase kinase 7 and the β-transducin repeat-containing gene, to inhibit pro-inflammatory cytokines and chemokines in endothelial cells (Fang et al., 2010). Thus, this study hypothesized that miR-10a-5p exerts a neuroprotective effect in the pathogenesis of AD by alleviating the inflammatory response. Unlike the action of miR-706 as a pro-cell death miRNA that is detected in endoplasmic reticulum-mediated diseases (Wang et al., 2020), miR-706 in our study was down-regulated in APP/PS1 mice at 3 and 6 months of age; this suggests, for the first time, that miR-706 plays a regulatory role in the intermediate stage of AD. Furthermore, RNA sequencing identified novel mature miR-80, novel mature miR-7, and novel mature miR-3, which were reduced in the advanced stage of AD.

The miRNA-mRNA network has an important influence on complex gene expression. This study constructed an miRNA-mRNA network based on 12 highly conserved miRNAs and three reliable novel miRNAs that were significantly expressed. Interestingly, the conserved miRNAs, such as miR-10a, miR-10b, miR-361-5p, miR-296-5p, miR-144, miR-451, miR-192, miR-214, miR-215, miR-223, miR-190b, and miR-181c, have also been reported to be differentially expressed in the human anterior cingulate gyrus and motor cortex (Nelson et al., 2018). The corresponding target genes were strongly related to the etiopathogenesis of AD, and included APP, BACE1, ADAM10, ADAM12, KH domain-containing RNA binding (QKI), BDNF, phosphatase and tensin homolog (PTEN), BCL2, IGF1, and LBH, which are key regulatory genes and/or enzymes in Aβ and Tau biosynthesis and transportation, synaptic dysfunction, neuronal apoptosis, autophagy, and inflammation (Malinin et al., 2005; Farnsworth et al., 2016; Yamaguchi-Kabata et al., 2018; Silvestro et al., 2019). Assuming that this miRNA-mRNA network covers multiple aspects of AD pathology, these gene regulators might be novel targets in developing additional treatments for AD.

Among the highly conserved miRNAs identified, the differentially expressed miR-10b-5p, miR-214-3p, miR-215-5p, and miR-296-3p play roles in differentiation, cell proliferation, migration, and invasion, which suggests that they could be diagnostic biomarkers in a variety of cancer-related diseases (Tao et al., 2019; Wang et al., 2019b; Cho et al., 2020; Liu et al., 2020; Wu et al., 2020). However, to the best of our knowledge, the discovery of their action in AD pathology in the present study is novel. Moreover, miR-10b-5p has an anti-apoptotic effect by directly binding to the 3′-UTR of PTEN, as demonstrated by the regulatory function in neuron apoptosis and ER stress via activation of the PI3K/AKT pathway in AD (Cui et al., 2017; Wu et al., 2019). Hence, miR-10b-5p could be involved in neuroprotection and ER regulation through pairing with the 3’-UTR of PTEN. miR-223-3p is another conservative miRNA that participates in various neurodegenerative diseases, and has considerable potential as a non-invasive biomarker in identifying AD, Parkinson’s disease, and mild cognitive impairment (Mancuso et al., 2019).

Some novel miRNAs, such as novel mature miR-29, novel mature miR-80, and novel mature miR-102, were identified for the first time in our AD model. The target genes of novel mature miR-29 are dual specificity phosphatase 1 (DUSP1), calcium/calmodulin dependent serine protein kinase (CASK), and ankyrin 2 (ANK2), which play crucial roles in AD associated-pathogenesis (Leandro et al., 2018; Higham et al., 2019; Silva et al., 2020). Given that the ANK2 gene contributes to longevity, cognition, and neurotransmitter conduction (Jenkins et al., 2015; Michalak et al., 2017), it is possible that the novel mature miR-29-ANK2 interaction is associated with multiple pathological mechanisms in AD. The predicted genes of the down-regulated novel mature miR-80 were the 5-hydroxytryptamine receptor 6 (HTR6) and solute carrier family 14 member 1 (SLC14A1). The 5-HT6 receptor polymorphism (C267T) of HTR6 participates in the susceptibility to late-onset AD, and SLC14A1 is modified in neurodegenerative diseases such as AD (Kan et al., 2004; Recabarren and Alarcón, 2017). Novel mature miR-102 exerts neuroprotective effects by targeting NADH: ubiquinone oxidoreductase subunit A9 (NDUFA9) and interferon regulatory factor 5 gene (IRF5) (Zhu et al., 2016; Adav et al., 2019), which are involved in M2 microglia activation through the inhibition of neuroinflammation and consequently better cognition. Thus, these novel miRNAs might be promising biomarkers of AD.

Concerning the GO enrichment analysis results, the dysregulated miRNAs from the APP/PS1 mice played a crucial role in the transcription and post-transcription stages. The target genes of the aberrant miRNAs are primarily involved in the biological processes of neuron differentiation, transportation process, and apoptosis. Our pathway enrichment analysis revealed the involvement of MAPK, FOXO, mTOR, and PI3K-AKT pathways, neuroactive ligand-receptor interaction, and autophagy, as well as pathological processes already implicated in the pathogenesis of AD, such as tau phosphorylation, biosynthesis of APP, loss of neurons, and the neuroinflammatory reaction. These miRNAs and potential target molecules should be further investigated in future experiments.

Current AD therapies are still ineffective in preventing and curing this disease, and more effective interventions are therefore urgently required. Part of the solution, which could aid the development of both an effective diagnostic method and AD therapy, is the analysis of gene expression modification linked to AD for a better-individualized control of AD patients (Ansari et al., 2017). However, one limitation of this work is the high false positive rate derived from algorithms and analysis software. This limitation means that different animal models, as well as serum from patients with AD, will be needed to assess the validity of our sequencing results using quantitative polymerase chain reaction, western blots, and immunohistochemistry.

In conclusion, this study revealed the aberrant miRNA and mRNA expression profile that contributes to the pathogenesis of AD, and identified significantly expressed mRNAs, miRNAs, and miRNA potential target genes. This work therefore puts forward representative novel therapeutic targets and promising biomarkers in the diagnosis of AD.

Additional files:

Additional Figure 1 ^{(760KB, tif)} : APP/PS1 mice showing learning and memory deficits in the Morris water maze test.

Additional Figure 1

APP/PS1 mice showing learning and memory deficits in the Morris water maze test.

A-D) In the water navigation task, latency to reach the escape platform during five training days of APP/PS1 mice and WT mice were detected at the ages of 1 (A), 3 (B), 6 (C), and 9 months (D). A significant day effect on spatial learning was observed between 6-month-old (F (1, 4) = 304.216, P < 0.001) and 9-month-old (F (1, 4) = 30.908, P < 0.01) groups. No signification was observed in the day effect on spatial learning among 1-month-old (F (1, 4) = 1.178, P = 0.339) and 3-month-old (F (1, 4) = 2.015, P = 0.229) groups. (E-H) In the exploration of the space task, the time spent in the escape platform quadrant of these mice at the ages of 1 (E), 3 (F), 6 (G), and 9 months (G) were examined. APP/PS1 mice showed a significant decrease at 3-month-old and 6-month-old compared to the WT control mice. Date are analysed using repeated measures one-way analysis of variance and presented as mean ± SEM (n = 3 per group). *P < 0.05, **P < 0.01, ***P < 0.001 vs. age-matched WT mice. APP: Amyloid-beta peptide precursor protein; PS1: presenilin 1; WT: wild type.

Additional Figure 2 ^{(203.3KB, tif)} : Purity evaluation of total RNA in 1% agarose gel electrophoresis.

Additional Figure 2

Purity evaluation of total RNA in 1% agarose gel electrophoresis.

APP: Amyloid-beta peptide precursor protein; M: marker; PS1: presenilin 1; WT: wild type.

Additional Figure 3 ^{(3MB, tif)} : The 1- (A), 3- (B), 6- (C), and 9-month-old (D) Gene Ontology enrichment top network.

Additional Figure 3

The 1- (A), 3- (B), 6- (C), and 9-month-old (D) Gene Ontology enrichment top network.

Additional Figure 4 ^{(2.9MB, tif)} : The 1- (A), 3- (B), 6- (C), and 9-month-old (D) pathway enrichment top network.

Additional Figure 4

The 1- (A), 3- (B), 6- (C), and 9-month-old (D) pathway enrichment top network.

Additional Table 1: Concentration of total RNA by Spark 20M multimode microplate reader.

Additional Table 2: Website information regarding the software used in this study.

Additional Table 3: Quality assessment of mRNA sequencing reads including total read counts, total bases counts, average read length, N bases count, N bases ratio, GC bases count, and GC bases ratio.

Additional Table 4: Quality assessment of mRNA sequencing reads including Q10/Q20/Q30 bases count and Q10/Q20/Q30 bases ratio.

Additional Table 5: Summary of the genome mapping analysis in the mRNA sequencing, including total reads, total mapped, multiple mapped, and uniquely mapped.

Additional Table 6: Differentially expressed genes in the APP/PS1 mouse cortices at 1, 3, 6, and 9 months in contrast to the same age of control mice.

Additional Table 7: Summary of the miRNA sequencing reads.

Additional Table 8 ^{(245.9KB, pdf)} : Summary of the known miRNA prediction using miRBase and miRDeep2 in the APP/PS1 mouse cortices at the four tested ages.

Additional Table 8

Summary of the known miRNA prediction using miRBase and miRDeep2 in the APP/PS1 mouse cortices at the four tested ages

Additional Table 9: Summary of the novel miRNA prediction using miRBase and miRDeep2 in the APP/PS1 mouse cortices at the four tested ages.