Neurochem Res (2015) 40:109–117 DOI 10.1007/s11064-014-1471-3

ORIGINAL PAPER

Computational Identification and Experimental Validation of MicroRNAs Binding to the Fragile X Syndrome Gene Fmr1 Xi Gong • Yanlu Wang • Jianping Zeng Siguang Li • Yuping Luo

•

Received: 28 June 2014 / Revised: 15 October 2014 / Accepted: 30 October 2014 / Published online: 7 November 2014 Ó Springer Science+Business Media New York 2014

Abstract MicroRNAs (miRNAs) usually bind to their target mRNAs through imperfect base pairing in the 30 untranslated regions (30 UTRs) and regulate target gene expression via post-transcriptional suppression. In recent years, computational approaches to predict miRNA targets have facilitated the identification of potential target sites. In this study, we used three programs TargetScan, miRDB and miRanda to predict potential miRNA binding sites to the fragile X gene Fmr1 and picked out 61 miRNAs which were predicted by all three programs for further investigation. Excitingly, 5 out of these miRNAs, miR-23a, miR32, miR-124, miR-335-5p and miR-350, were experimentally verified by luciferase reporter assays. Furthermore, overexpression of miR-124 in mouse embryonic neural progenitor cells (eNPC) could not only significantly reduce Fmr1 level, but also increase Cdk4 and cyclin D1 levels which coincidently promoted eNPC proliferation. Our results imply that miR-124 plays an important role in the proliferation of mouse embryonic stem cells by promoting Cdk4 and cyclin D1 expression through directly inhibiting Fmr1 expression. Electronic supplementary material The online version of this article (doi:10.1007/s11064-014-1471-3) contains supplementary material, which is available to authorized users. X. Gong  Y. Wang  J. Zeng  S. Li (&)  Y. Luo (&) State Key Laboratory of Food Science and Technology, College of Life Sciences, Nanchang University, Nanchang 330031, China e-mail: [email protected] Y. Luo e-mail: [email protected] S. Li Department of Regenerative Medicine, Stem Cell Center, Tongji University School of Medicine, Shanghai 200092, China

Keywords Fmr1  miR-124  Proliferation  Neural progenitor cells  Cdk4  Cyclin D1

Introduction MicroRNAs (miRNAs) are single-stranded non-coding RNAs with an average length of 22 nucleotides. These small RNAs usually bind to their target mRNAs through imperfect base pairing in the 30 -untranslated regions (30 UTRs) and regulate gene expression at the post-transcriptional level, leading to translational inhibition, cleavage of the target mRNAs or mRNA decapping/deadenylation [1–3]. Mounting evidence suggests that miRNAs play a pivotal role in many key biological processes, from developmental timing, fate determination, apoptosis, and metabolism to immune response and tumorigenesis [4–8]. Recent studies have shown that miRNAs were involved in various neurological diseases. For example, the expression of miR-9 and miR-128 are upregulated, while miR-15a and miR-107 are downregulated in Alzheimer’s patients [9, 10]. In schizophrenia, miR-181b plays a role by targeting visinin-like 1 (VSNL1) and glutamate receptor subunit (GRIA2) [11]. Fragile X syndrome (FXS) is a devastating neurological disorder which affects the cognitive abilities of 1/4,000 males and 1/8,000 females worldwide [12]. In this disorder, transcriptional silencing of the Fmr1 gene causes loss of fragile X mental retardation protein (FMRP), a synaptic RNA-binding protein with regulatory roles in synaptic plasticity [13]. In addition to synaptic function, FMRP was shown to play a role in germline proliferation and differentiation [14, 15]. Because the size and binding specificity of miRNAs are limited, a single microRNA has the potential to regulate

123

110

multiple target mRNAs and one mRNA can be targeted by a large number of miRNAs. Therefore, the process of validating the interaction between the miRNA and the specific mRNA in the laboratory is time consuming and costly. A computational approach to predict miRNA targets facilitates the process of narrowing down potential target sites for experimental validation. In this study, three programs, TargetScan, miRDB and miRanda were utilized to predicted potential miRNA binding sites to Fmr1 mRNA. 61 miRNAs were predicted by all three programs and five miRNAs, miR-23a, miR-32, miR-124, miR-335-5p and miR-350, were experimentally verified by luciferase assays. Amazed, in the lentiviral mediated miR-124-overexpressing cells, both Fmr1 mRNA levels and FMRP levels were significantly reduced and ultimately modulated mouse embryonic neural progenitor cell (eNPC) proliferation. These data demonstrate that the functional interaction between miRNA and Fmr1 plays an important modulatory role in NPCs proliferation.

Materials and Methods Screen of MicroRNAs that Target Fmr1 mRNA MiRNA binding sites in Fmr1 gene were downloaded from three different databases: miRanda, August 2010 Release (http://www.microrna.org/microrna/getGeneForm.do) [16], TargetScan Mouse, Release 6.2 (http://www.targetscan. org/mmu_61) [17] and miRDB, April 2012 Release (http:// mirdb.org/miRDB). Only the miRNAs predicted by all three algorithms were selected for further identification. Expression Analysis of Predicted miRNAs To analyze the specific temporal and spatial expression patterns of miRNAs, the Mouse Genome Informatics (MGI) database (Mouse Genome Database, the Jackson Laboratory, Bar Harbor, Maine; http://www.informatics. jax.org/) [18] were adopted to check the expression of miRNAs in mouse brain. Primary Embryonic NPCs Cultures Primary embryonic NPCs were prepared from E12.5 fetal mice according to a protocol detailed previously [19] with some modification. Briefly, embryonic cerebral cortices were dissected, cut into small pieces in cold phosphatic buffer solution (PBS), and then incubated with a 0.05 % trypsin–EDTA solution (Gibco), at 37 °C. The dissociated cells were filtered with 70 lm filter membrane,the single cells were centrifuged at 1,0009g for 3 min. Finally, the cell pellet was resuspended in DMEM-F12 (1:1) medium

123

Neurochem Res (2015) 40:109–117

(Gibco, 11320033), then the number of live cells was counted by trypan blue exclusion assay in a hemocytometer. Dissociated cells were cultured in DMEM-F12 (1:1) medium supplemented with 2 % B27 (Gibco, 0080085-SA), 20 ng/ml epidermal growth factor (EGF, R&D, 2028-EG-200), and 10 ng/ml basic fibroblast growth factor (bFGF, R&D, 3139-FB-025). The medium was changed every other day. After 5 days of culture, primary neurospheres were formed. All cultures were maintained in a humidified incubator at 37 °C and 5 % CO2. Human HEK-293 cells were grown in Dulbecco’s modified Eagle medium (DMEM, Gibco) with 10 % heat-inactivated fetal bovine serum, 100 u/ml penicillin, and 100 lg/ml streptomycin at 37 °C in a 5 % CO2 atmosphere. Construction of a Lentiviral Vector Expressing shRNA Short hairpin RNA (shRNA) expression constructs were derived from a generated U6-shRNA lentiviral construct (Lenti-137) by using a PCR-shagging approach as described previously [20]. Long oligos containing miRNA mature sequence (Supplementary Table 1) were used as reverse primers in combination with a common forward primer complementary to the 50 end of the U6 promoter (50 -AAAGTTAA CTAGTGGATCCGACGCCGCCATCTC-30 ) to amplify the entire U6 promoter and shRNA in a single PCR product. PCR amplification was initiated by denaturing at 94 °C for 5 min, followed by 30 cycles of 30 s at 94 °C, 30 s at 60 °C and 40 s at 72 °C, with a final extension step of 5 min at 72 °C. The PCR products were cloned into the TOPO TA vector to generate recombinant constructs TOPO-shRNA. U6-shRNA expression constructs were excised from the TOPO vectors with restriction endonucleases HpaI and ClaI, and then were subcloned into the lentiviral vector to generate recombinant constructs LentishRNA which express green fluorescent protein gene (GFP). All recombinant constructs were verified by sequencing. Luciferase Reporter Assay Based on the mouse Fmr1 mRNA sequences in GenBank, the mouse 30 UTR of the Fmr1 gene was amplified from the genomic DNA of mouse embryonic eNPCs by using the following primers: 50 -GATACTCGAGGGCTGCGCAC GGGTAAAGA-30 (forward) and 50 -CATGGCGGCCGC TGTAATATGAATCAACTCCAACTC-30 (reverse) and cloned into the psiCHECK-2 dual luciferase reporter vector (Promega, C8021) using Xho I and Not I restriction sites. The recombinant plasmids (psiCHECK2-mFmr1-30 UTR) was confirmed by sequencing. Pre-confluent (60–70 %) HEK293T cells in 24-well plates were co-transfected with 1 lg of the dual luciferase reporter plasmid (psiCHECK2-mFmr1-30 UTR plasmid), 1 lg of TOPO-shRNA using the calcium phosphate

Neurochem Res (2015) 40:109–117

method. After 48 h, the cell lysates were assayed, and luciferase activities were measured with the dual-luciferase reporter system (Promega, E1980) according to the manufacturer’s instructions. R-Luc activity was normalized to F-Luc activity to account for variation in transfection efficiencies. All luciferase experiments were performed at least three times. Virus Production The lentiviral vector expressing shRNA was co-transfected by calcium phosphate transient transfection into 293T cells with the packing vectors pMDL, pRev, and pVSV-G. The calcium phosphate-DNA precipitate was allowed to stay on the cells for 14–16 h, and then replaced with fresh medium. The medium containing viral was collected at 48, 72 and 96 h, and filtered through 0.22-lm pore nitrocellulose filters and then centrifuged in a Beckman ultracentrifuge (Beckman, Avanti J-301) at 55,0009g for 2 h at 16 °C. The precipitate was resuspended in NPC complete medium. The viral supernatant was directly infected cells or stored at -80 °C. Western Blot Analysis For endogenous protein expression analysis, NPCs were transfected with lentiviral vector expressing shRNA. We also infected anti-miR-124 (Guangzhou RiboBio Co. Ltd., miR20000134-1-5), which complemented endogenous miR-124 in mouse eNPCs to reduce miR-124 expression. Cell lysates were prepared 48 h after virus infection. Protein extracts were obtained using RIPA lysis buffer [50 mM Tris (pH 7.4), 150 mM NaCl, 1 % NP-40, 0.1 % SDS] in the presence of protease inhibitors. Protein concentration was determined by the BCA assay using bovine serum albumin as the standard, and equal amounts of proteins were separated by SDS-PAGE and blotted onto polyvinylidene fluoride (PVDF) membrane (Millipore, IPVH00010). For immunoblot experiments, membranes were blocked with TBS-T containing non-fat 5 % dry milk for 1 h, and incubated over night at 4 °C with primary antibody. The following primary antibodies were used: anti-FMRP primary antibody (Abcam, ab69815), antiCdk4 (Abcam, ab6315) and anti-Cyclin D1 (Abcam, ab6152). Anti-Actin (Sigma Aldrich, A2066) and antiGAPDH (Sigma-Aldrich, G8795) were used as internal loading control. The horseradish peroxidase (HRP)-conjugated secondary antibodies were visualized by enhanced chemiluminescence system (Thermo, 34094) according to the manufacturer’s instructions. Band intensities were quantified by densitometric analysis. For quantitative analysis of WB the signal for each protein was normalized to

111

the housekeeping protein detected on the same blot, and the ratio with average control values was determined. BrdU Proliferation Assay Proliferative effects were assessed in eNPCs transfected with lentiviral vector expressing shRNA using the BrdU labeling method. Cells were plated onto poly-L-ornithine and laminin coated glass coverslips in 24-well plates at a density of 5 9 104 cells/well in NPCs complete medium. At 20 h postplating, the culture medium was replaced with fresh medium containing 5 lM 5-bromo-20 -deoxyuridine (BrdU, SigmaAldrich). Following an incubation of 8 h, cells were fixed with 4 % paraformaldehyde (PFA) for 30 min at room temperature and washed with PBS, then processed for BrdU immunostaining. The anti-BrdU antibody (Abcam, ab6326) and the fluorescent nuclear dye 40 ,6-dimidino-20 -phenylindole dihydrochloride (DAPI, Sigma-Aldrich, B2261) were used. Each assay was carried out in duplicate in at least three independent experiments. Statistical Analysis Statistical analysis was performed using ANOVA and Student’s t test. All data are shown as the means with standard error of mean (mean ± SEM). Probabilities of p \ 0.05 were considered significant.

Results MiRNA Prediction To identify microRNAs that may potentially regulate Fmr1, we used the online computer software to predict the binding sites of microRNAs on the 30 UTR of mouse Fmr1 mRNA (NM_008031). Due to the fact that different prediction programs focus on different aspects for miRNA target site prediction (pattern-based search, seed matching, conservation, energy or structure), various properties of the miRNA binding site are covered. With the aim to yield a more accurate miRNA target site prediction, the three programs, miRanda, miRDB and TargetScan Mouse, were used to predict the binding sites of miRNAs to Fmr1 30 UTR. We generated Venn diagram to show the overlap of the predicted miRNAs from three programs, 500 miRNAs are predicted to bind to mouse Fmr1 30 UTR and 164 of them are predicted by at least two programs (Fig. 1a). The 61 miRNAs predicted by all three programs were shown in Supplementary Table 2. Prediction according to multiple programs helps to reduce the number of analyzed miRNAs

123

112

Neurochem Res (2015) 40:109–117

2071

B

2078

mFmr1 3’UTR

5’---GCGCUAACUUGCUUAAAUGUGAA---3’

mmu-miR-23a

3’-

CCUUUAGGGACCGUUACACUA -5’ Seed sequence

Relative luciferase activity(rLuc/fLuc)

A

1.5

1.0

***

0.5

0.0

1746

mFmr1 3’UTR

5’---AUUUGUUUGGUUAUAGUGCAAUA ---3’

mmu-miR-32

3’-

ACGUUGAAUCAUUACACGUUAU -5’ Seed sequence

Relative luciferase activity(rLuc/fLuc)

1739

1.5

Relative Luciferase activity(rLuc/fLuc)

control

1.5

1.0

***

0.5 0.0 control

543

549

mFmr1 3’UTR

5’---AGAGUUGUAUGAUCUGUGCCUUU---3’

mmu-miR-124

3’-CCG

UAAGUGGCGCACGGAAU-5’ Seed sequence

mFmr1 3’UTR

5’---CCUUGUAAUGUAACUGCUCUUGG ---3’

mmu-miR-335

3’-

UGUAAAAAGCAAUAACGAGAACU -5’ Seed sequence

Relative luciferase activity(rLuc/fLuc)

1672

miR-32

1.0

***

0.5

0.0 control

1666

miR-23a

miR-124

1.5

1.0

***

0.5

0.0 control

354

mFmr1 3’UTR

5’---UCGGAGUAAAUCACAUUUGUGAU ---3’

mmu-miR-350

3’- CUUUCACAUACCCG--AAACACUU -5’ Seed sequence

miR-335

1.5

Relative luciferase activity(rLuc/fLuc)

358

1.0

**

0.5

0.0 control

123

miR-350

Neurochem Res (2015) 40:109–117 b Fig. 1 Prediction and experimental validation of miRNA targets.

a The Venn diagram shows the overlap of the predicted miRNAs from three programs: 500 miRNAs are predicted to bind to mouse Fmr1 30 UTR and 164 of them are predicted by at least two programs, significantly, the only 61 miRNAs are predicted by three programs. b Schematic diagrams show the predicted seed region expected to bind the mFmr1 30 UTR and luciferase reporter assays. HEK-293T cells were transiently co-transfected with the luciferase reporter vector containing wild-type mFmr1 30 UTR in the presence of miRNAs expressing constructs (miR-23a, miR-32, miR-124, miR-335 and miR-350). Luciferase activity was evaluated 24 h after transfection as described in ‘‘Materials and Methods’’. These data are a representative of at least three independent experiments. Asterisks denote instances in which differences were statistically significant (**p \ 0.005, ***p \ 0.001, n = 3)

and improves the selection of candidates for further experimental validations. Experimental Validation of MiRNA Targets 5 of 61 miRNAs predicted by computational approach, miR-23a, miR-124, miR-335-5p, miR-32 and miR-350, were randomly selected for experiment validation. To demonstrate that the selected miRNAs directly regulate Fmr1 expression by interaction with the 30 UTR of the mouse Fmr1 gene, we performed dual-luciferase assays with the psiCHECK-2 reporter vector which express both Renilla and Firefly luciferase and harbour the 30 UTR of Fmr1 downstream of the Renilla luciferase coding sequence. This reporter vector, together with the respective miRNAs or negative control miRNA, was transfected into HEK-293T cells. Dual-luciferase assays demonstrated that the five miRNAs identified by bioinformatical approaches all exhibited significantly reduced luciferase activity compared with the negative control: miR-23a lowered the luminescent signal to 63.7 % (Fig. 1b), miR-32 to 48.7 % (Fig. 1c), miR-124 to 63 % (Fig. 1d), miR-335 to 34.8 % (Fig. 1e) and miR-350 to 21.7 % of control (Fig. 1f). Additionally, miR-323-3p, miR-19a, miR-19b [21], miR129-5p [22] and miR-130b [23] which were predicted by our method, had already been experimentally validated to directly regulate Fmr1 expression by interaction with the 30 UTR of the human or mouse Fmr1 gene. These data suggest that the predicted miRNAs by three programs could interacted with the Fmr1 30 UTR.

113

lentiviral system in mouse eNPCs. In 5 miRNA-overexpressing eNPCs, miR-32 and miR-124 could suppress FMRP expression (Fig. 2). Especially, in the miR-124over-expressing eNPCs, both mFmr1 mRNA levels (Fig. 2b) and protein levels (Fig. 2c) were significantly reduced compared with the control. MiR-124 is one of the most abundant miRNAs in brain [24], we infected antimiR-124, which complemented endogenous miR-124 in mouse eNPCs, and found FMRP protein levels were increased in these eNPCs compared with the negative control (Fig. 2c). These results suggest that Fmr1 is regulated by endogenous miR-124 in eNPCs. Previous work showed FMRP could control proliferation of adult neural stem cells in mice [15] and neural stem cells and the germline in Drosophila [25, 26]. To determine whether miR-124 could affect eNPC proliferation, we performed an immunocytochemical experiment with BrdU to label proliferating cells. The effects on proliferation were investigated by calculating the proportion of BrdU? cells within the population of GFP?. According to count, miR-124-over-expressing eNPCs exhibited moderate increase in BrdU incorporation compared with the negative control (Fig. 3a, b). FMRP is a selective RNA-binding protein that forms a messenger ribonucleoprotein (mRNP) complex and negatively regulates the expression of its target genes at the post-transcriptional level [13, 27, 28]. Several factors involved in stem cell proliferation, such as cyclin-dependent kinase 4 (CDK4) and cyclin D1, are regulated by Fmrp [15]. Both CDK4 and cyclin D1 expression levels are important for the proliferation of neural stem cell. The overexpression of Cdk4 and cyclin D1 were effective in preventing G1 lengthening and neurogenesis, leading to an expansion of the neural progenitor population in both the developing and adult mouse brain [29, 30]. Hence, we investigated Cdk4 and cyclin D1 expression by qRT-PCR and western-blot. In miR-124over-expressing eNPCs, we observed increased Cdk4 and cyclin D1 protein levels compared with the control (Fig. 4). These results imply that miR-124 promotes Cdk4 and cyclin D1 through directly regulated FMRP expression, and then alters the proliferation of mouse embryonic stem cell.

Discussion Functional Validation of Bioinformatical Candidate MiRNAs FMRP has been identified and validated to express in adult and embryonic NPCs [15, 23]. To investigate the effect of predicted miRNAs on Fmr1 expression in mouse eNPCs, we isolated eNPCs from fetal brains of ICR mice at embryonic day 12.5, and over expressed miRNAs by

MiRNAs are endogenously encoded single-stranded RNAs that can posttranscriptionally regulate gene expression by targeting mRNAs for cleavage or translational repression [31]. One mRNA can be targeted by a large number of miRNAs, but a computational approach for predicting miRNA targets facilitates the process of narrowing down potential target sites for experimental validation. We

123

114

Neurochem Res (2015) 40:109–117

B

A

GAPDH control

miR-335

miR-32

miR-350

miR-23a

1.00

**

0.75 0.50 0.25

1.5

1.0

* 0.5

0.0 iR m

3a iR

-2

50 -3 iR

m

2 -3 m

iR m

co nt ro l m iR -3 35

co

0.00

-1

nt ro

24

l

mFMRP/GAPDH

1.25

Relative mFmr1 mRNA levels

FMRP

1.25

FMRP GAPDH control

miR-124

FMRP/GAPDH

C

1.00 0.75

**

0.50 0.25 0.00 control

FMRP β-actin control

anti-miR-124

FMRP/GAPDH

2.0

miR-124

**

1.5 1.0 0.5 0.0 control

anti-miR-124

Fig. 2 Effects of candidated miRNAs on Fmr1 expression in eNPCs. a Western blot shows that over-expression of miR-32 significantly reduced mFmr1 expression, as decreased protein levels were detected. (**p \ 0.01, n = 2). b QRT-PCR shows that mFmr1 mRNA level was decreased (*p \ 0.05, n = 3) when miR-124 was over-expressed

in eNPCs. c Western blot shows that over-expression of miR-124 significantly reduced mFmr1 expression, conversely, mFmr1 expression was increased when endogenous miR-124 was down-regulated by anti-miR-124 in mouse eNPCs. GAPDH and b-Actin were used as the loading control

established a computational approach for the identification of miRNAs putatively influencing the expression of Fmr1 gene and found that 61 miRNAs were predicted by all three programs and five random miRNAs, miR-23a, miR-32, miR-124, miR-335-5p and miR-350, were experimentally verified by luciferase assays. Further experiments showed that only overexpression of miR-32 and miR-124 could reduce FMRP expression in eNPCs. These studies reinforce that luciferase assay is utilized to identify miRNA binding to its target gene but further studies of post-transcriptional regulation of target gene should be demonstrated. MiR-124 was one of the first miRNAs identified in mammals due to its abundant expression in the mature brain and now is widely studied. As previously reported, miR-124 induces neurogenesis by suppressing small

C-terminal domain phosphatase 1 (SCP1) expression during central nervous system (CNS) development [32]. In 2009, Yoo et al. [33] found that miR-124 mediates neuronal differentiation by repressing the neural progenitorspecific BAF complex 53 kDa subunit (BAF53a). These results indicate that miR-124 plays an essential role in neuronal differentiation. At the present, our studies have demonstrated that miR-124 promotes Cdk4 and cyclin D1 via directly downregulation of Fmr1 expression, and then alters mouse eNPCs proliferation. Fragile X syndrome is a neurodevelopmental disorder caused by the selective loss of FMRP production. Recently, an increasing number of studies showed that miRNAs are involved in diseased and the mechanisms of miRNA-disease associations are very complex. In this study, we found that 61

123

Neurochem Res (2015) 40:109–117

A

BrdU

BrdU

GFP

GFP

Dapi

Dapi

Merge

Merge

control

miR-124

***

80

60

40

20

-1 iR m

nt

ro

l

24

0

co

% of Brdu+ Cell

B

(Among total GFP+ Cell)

Fig. 3 Down-regulation of FMRP by miR-124 leads to increased eNPCs proliferation. a Immunocytochemistry shows an increased number of BrdUpositive cells in miR-124-overexpressing cells. b Quantitative analysis shows that miR-124over-expressing eNPCs exhibited a 10.7 % increase in BrdU incorporation compared to the negative control

115

123

24

0.0

m

Cyclin D1/¦Â-actin

CDK4/¦Â-actin

**

4

iR

-1

24

2

m

co

nt

ro

l

β-actin

iR

nt co

iR m 6

Cyclin D1

0.0

-1

ro nt co

B

CDK4

0.5

24

0.5

1.0

-1

1.0

1.5

l

**

ro

1.5

l

A

Relative Cylin D1 mRNA levels

Neurochem Res (2015) 40:109–117

Relative CDK4 mRNA levels

116

0 control

miR-124

**

12 11 10 9 8 7 6 5 4 3 2 1 0 control

miR-124

Fig. 4 Cdk4 and cyclin D1 expression in miR-124-over-expressing cells. a QRT-PCR shows that cdk4 mRNA level was increased (*p \ 0.05, n = 3) when miR-124 was over-expressed in eNPCs. Nonetheless, cyclin D1 mRNA level was invariable. b Western blot

shows that Cdk4 and cyclin D1 expression were significantly increased in miR-124-over-expressing eNPCs. b-Actin were used as the loading control

miRNAs were predicted by three programs to interact with Fmr1, and 19 of them were expressed in brain. These data will do great help in understanding the associations between miRNAs and FXS, furthermore, a large-scale analysis these miRNAs associating FXS will offer a platform to dissect the patterns of the miRNA and disease associations.

4. Ishino Y, Hayashi Y, Naruse M, Tomita K, Sanbo M, Fuchigami T, Fujiki R, Hirose K, Toyooka Y, Fujimori T, Ikenaka K, Hitoshi S (2014) Bre1a, a histone H2B ubiquitin ligase, regulates the cell cycle and differentiation of neural precursor cells. J Neurosci 34:3067–3078 5. Lungu G, Stoica G, Ambrus A (2013) MicroRNA profiling and the role of microRNA-132 in neurodegeneration using a rat model. Neurosci Lett 553:153–158 6. Baltimore D, Boldin MP, O’Connell RM, Rao DS, Taganov KD (2008) MicroRNAs: new regulators of immune cell development and function. Nat Immunol 9:839–845 7. Giraldez AJ, Cinalli RM, Glasner ME, Enright AJ, Thomson JM, Baskerville S, Hammond SM, Bartel DP, Schier AF (2005) MicroRNAs regulate brain morphogenesis in zebrafish. Science 308:833–838 8. Abbott AL, Alvarez-Saavedra E, Miska EA, Lau NC, Bartel DP, Horvitz HR, Ambros V (2005) The let-7 MicroRNA family members mir-48, mir-84, and mir-241 function together to regulate developmental timing in Caenorhabditis elegans. Dev Cell 9:403–414 9. Lukiw WJ (2007) MicroRNA speciation in fetal, adult and Alzheimer’s disease hippocampus. NeuroReport 18:297–300 10. Wang WX, Rajeev BW, Stromberg AJ, Ren N, Tang G, Huang Q, Rigoutsos I, Nelson PT (2008) The expression of microRNA miR-107 decreases early in Alzheimer’s disease and may accelerate diseaseprogression through regulation of beta-site amyloid precursor protein-cleaving enzyme 1. J Neurosci 28:1213–1223

Acknowledgments This work is partially supported by the National Natural Science Foundations of China (Nos. 30971473, 31171317, 31271375 and 31271450) and the Scientific Research Foundation to support returnees.

References 1. Bartel DP, Chen CZ (2004) Micromanagers of gene expression: the potentially widespread influence of metazoan microRNAs. Nat Rev Genet 5:396–400 2. Guo H, Ingolia NT, Weissman JS, Bartel DP (2010) Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466:835–840 3. Chekulaeva M, Filipowicz W (2009) Mechanisms of miRNAmediated post-transcriptional regulation in animal cells. Curr Opin Cell Biol 21:452–460

123

Neurochem Res (2015) 40:109–117 11. Beveridge NJ, Tooney PA, Carroll AP, Gardiner E, Bowden N, Scott RJ, Tran N, Dedova I, Cairns MJ (2008) Dysregulation of miRNA 181b in the temporal cortex in schizophrenia. Hum Mol Genet 17:1156–1168 12. Penagarikano O, Mulle JG, Warren ST (2007) The pathophysiology of fragile x syndrome. Annu Rev Genomics Hum Genet 8:109–129 13. Bassell GJ, Warren ST (2008) Fragile X syndrome: loss of local mRNA regulation alters synaptic development and function. Neuron 60:201–214 14. Epstein AM, Bauer CR, Ho A, Bosco G, Zarnescu DC (2009) Drosophila fragile X protein controls cellular proliferation by regulating cbl levels in the ovary. Dev Biol 330:83–92 15. Luo Y, Shan G, Guo W, Smrt RD, Johnson EB, Li X, Pfeiffer RL, Szulwach KE, Duan R, Barkho BZ, Li W, Liu C, Jin P, Zhao X (2010) Fragile X mental retardation protein regulates proliferation and differentiation of adult neural stem/progenitor cells. PLoS Genet 6:e1000898 16. Betel D, Wilson M, Gabow A, Marks DS, Sander C (2008) microRNA target predictions: the microRNA.org resource: targets and expression. Nucleic Acids Res 36:D149–D153 17. Friedman RC, Farh KK, Burge CB, Bartel DP (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 19:92–105 18. Bult CJ, Eppig JT, Kadin JA, Richardson JE, Blake JA, Mouse Genome Database Group (2008) The mouse genome database (MGD): mouse biology and model systems. Nucleic Acids Res 36:D724–D728 19. Ren YJ, Zhang H, Huang H, Wang XM, Zhou ZY, Cui FZ, An YH (2009) In vitro behavior of neural stem cells in response to different chemical functional groups. Biomaterials 30:1036–1044 20. Paddison PJ, Caudy AA, Bernstein E, Hannon GJ, Conklin DS (2002) Short hairpin RNAs (shRNAs) induce sequence-specific silencing in mammalian cells. Genes Dev 16:948–958 21. Yi YH, Sun XS, Qin JM, Zhao QH, Liao WP, Long YS (2010) Experimental identification of microRNA targets on the 30 untranslated region of human FMR1 gene. J Neurosci Methods 190:34–38 22. Zongaro S, Hukema R, D’Antoni S, Davidovic L, Barbry P, Catania MV, Willemsen R, Mari B, Bardoni B (2013) The 30

117

23.

24.

25.

26.

27.

28.

29.

30.

31. 32.

33.

UTR of FMR1 mRNA is a target of miR-101, miR-129-5p and miR-221: implications for the molecular pathology of FXTAS at the synapse. Hum Mol Genet 22:1971–1982 Gong X, Zhang K, Wang Y, Wang J, Cui Y, Li S, Luo Y (2013) MicroRNA-130b targets Fmr1 and regulated embryonic neural progenitor cell proliferation and differentiation. Biochem Biophys Res Commun 439:493–500 Lagos-Quintana M, Rauhut R, Yalcin A, Meyer J, Lendeckel W, Tuschl T (2002) Identification of tissue-specific microRNAs from mouse. Curr Biol 12:735–739 Epstein AM, Bauer CR, Ho A, Bosco G, Zarnescu DC (2009) Drosophila fragile X protein controls cellular proliferation by regulating cbl levels in the ovary. Dev Biol 330:83–92 Callan MA, Cabernard C, Heck J, Luois S, Doe CQ, Zarnescu DC (2010) Fragile X protein controls neural stem cell proliferation in the Drosophila brain. Hum Mol Genet 19:3068–3079 Lu R, Wang H, Liang Z, Ku L, O’donnell WT, Li W, Warren ST, Feng Y (2004) The fragile X protein controls microtubule-associated protein 1B translation and microtubule stability in brain neuron development. Proc Natl Acad Sci USA 101:15201–15206 Sung YJ, Dolzhanskaya N, Nolin SL, Brown T, Currie JR, Denman RB (2003) The fragile X mental retardation protein FMRP binds elongation factor 1A mRNA and negatively regulates its translation in vivo. J Biol Chem 278:15669–15678 Lange C, Huttner WB, Calegari F (2009) Cdk4/cyclinD1 overexpression in neural stem cells shortens G1, delays neurogenesis, and promotes the generation and expansion of basal progenitors. Cell Stem Cell 5:320–331 Artegiani B, Lindemann D, Calegari F (2011) Overexpression of cdk4 and cyclin D1 triggers greater expansion of neural stem cells in the adult mouse brain. J Exp Med 208:937–948 Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281–297 Visvanathan J, Lee S, Lee B, Lee JW, Lee SK (2007) The microRNA miR-124 antagonizes the anti-neural REST/SCP1 pathway during embryonic CNS development. Genes Dev 21:744–749 Yoo AS, Staah BT, Chen L, Crabtree GR (2009) MicroRNAmediated switching of chromatin-remodelling complexes in neural development. Nature 460:642–646

123