MiR-592 has been identified as a neural-enriched microRNA, plays an important role in mNPCs differentiation, could induce astrogliogenesis differentiation arrest or/and enhance neurogenesis in vitro. Previous studies showed that long noncoding RNAs (lncRNAs) were involved in the neuronal development and activity. To investigate the role of miR-592 in neurogenesis, we described the expression profile of lncRNAs in miR-592 knockout mouse embryonic stem cells (mESCs) and the corresponding normal mESCs by microarray. By the microarray analysis and luciferase reporter assays, we demonstrated that lncRNA - AK048794, regulated by transcription factor GATA1, functioned as a competing endogenous RNA (ceRNA) for miR-592 and led to the de-repression of its endogenous target FAM91A1, which is involved in mESC pluripotency maintenance. Taken together, these observations imply that AK048794 modulated the expression of multiple genes involved in mESC pluripotency maintenance by acting as a ceRNA for miR-592, which may build up the link between the regulatory miRNA network and mESC pluripotency.

Introduction

MicroRNAs (miRNAs), a type of small noncoding RNA that posttranscriptionally regulates gene expression by inducing mRNA degradation, translational repression, or deadenylation, are also well known as the key posttranscriptional modifier contributing to the control of mNSC self-renewal [1], pluripotency [2,3], and differentiation [46]. In our previous study, we demonstrated that miR-592, a neural-enriched miRNA, played an important role in embryonic neurogenesis or/and astrogliogenesis by inducing astrogliogenesis differentiation arrest or/and enhancing neurogenesis in vitro [7]. However, it remains unclear about the molecular mechanism of miR-592 in the regulation of neuronal differentiation.

In addition to miRNA, long noncoding RNAs (lncRNAs) have previously been identified as novel regulators of the transcriptional and epigenetic networks [8,9], and involved in the neuronal development and activity [1012]. LncRNAs are mRNA-like transcripts lacking significant open reading frames and longer than 200 nt (nucleotides) in length. Over the past decade, increasing evidence suggested that lncRNAs are important and powerful cis- and trans-regulators of gene activity that can function as scaffolds for chromatin-modifying complexes and nuclear bodies, and as enhancers and mediators of long-range chromatin interactions [13,14]. A peculiar mode of action is that lncRNAs function as competing endogenous RNAs (ceRNAs) by binding to and sequestering specific miRNAs to protect the target mRNAs from repression [15]. This represents a new type of regulatory circuitry in which different types of RNAs (both coding and noncoding) can cross-talk to each other by competing for shared miRNAs [15,16]. Previous studies in embryonic stem cells (ESCs) indicate that lncRNAs are emerging players in embryogenesis and in developmental processes [1719]. To investigate the molecular mechanism of miR-592-induced neurogenesis, we, therefore, propose that some lncRNAs may function as ceRNAs to link miR-592 and are involved in the differentiation from miR-592 knockout mouse ESCs (mESCs) to neurons.

In the present study, we precisely describe the lncRNAs expression profile in mESCs (KO-miR-592-mESCs) and the corresponding normal mESCs by microarray. After analysis of lncRNAs expression levels between two different samples, we picked out the highly or lowly expressed lncRNAs. The potential functions of these lncRNAs were analyzed by gene ontology (GO) and pathway analysis based on the differentially expressed mRNAs. For further screening of important lncRNAs, lncRNAs classification and subgroup analysis were also performed. Then, by bioinformatic analysis and molecular biological methods, we demonstrated that lncRNA - AK048794, regulated by GATA1, functioned as ‘miR-592 sponges’ to protect the target family with sequence similarity 91, member A1 (FAM91A1) from repression, and plays an important role in mESC pluripotency maintenance. Our study could provide a comprehensive understanding of miR-592-related lncRNAs in mESCs and help to elucidate the molecular mechanisms of miR-592-induced neurogenesis.

Materials and methods

Cell culture and transfection

Mouse ESCs (mESCs) J were obtained from Prof. Xue (Tongji University, Shanghai, China), and miR-592 knockout mESCs JM8A3 were obtained from the Mutant Mouse Resource Research Centers (MMRRC, USA). mESCs were cultured under feeder layer conditions in accordance with the protocol from the MMRRC. For transfection, all vectors, mimics, and inhibitors were transfected into mESCs and 293T cells with Xfect Transfection Reagent (Clontech, CA, USA), Xfect mESC Transfection Reagent (Clontech, CA, USA) and/or Xfect RNA Transfection Reagent (Clontech, CA, USA), respectively.

RNA extraction

Total RNAs and miRNAs were prepared from the mESCs using the miRNeasy Mini Kit (Qiagen). PolyA+ RNA fraction was obtained using the Oligotex mRNA Mini Kit (Qiagen). Nuclear and cytoplasm RNA were isolated using the Ambion® PARIS™ kit (Ambion). The NanoDrop ND-1000 spectrophotometer was used to measure RNA quantity and quality. The RNA integrity was assessed by standard denaturing agarose gel electrophoresis.

Microarray and data analysis

Sample labeling and array hybridization were performed according to the Agilent One-Color Microarray-Based Gene Expression Analysis protocol (Agilent Technology) with minor modifications. Briefly, mRNA was purified from total RNA after removal of rRNA (mRNA-ONLY™ Eukaryotic mRNA Isolation Kit, Epicentre). Then, each sample was amplified and transcribed into fluorescent cRNA along the entire length of the transcripts without 3′-bias utilizing a random priming method. The labeled cRNAs were hybridized onto the Mouse LncRNA Array v2.0 (8 × 60 K, Arraystar). After having washed the slides, the arrays were scanned by the Agilent Scanner G2505C.

Agilent Feature Extraction software (v11.0.1.1) was used to analyze the acquired array images. Quantile normalization and subsequent data processing were performed using the GeneSpring GX v11.5.1 software package (Agilent Technologies). After quantile normalization of the raw data, lncRNAs and mRNAs that at least six out of six samples have flags in present or marginal (‘all targets value’) were chosen for further data analysis. Differentially expressed lncRNAs and mRNAs with statistical significance between two groups were identified through Volcano Plot filtering and differentially expressed lncRNAs and mRNAs between two samples were identified through Fold Change filtering.

GO and pathway analysis

GO categories derived from Gene Ontology (www.geneontology.org) and pathway analysis were performed to determine the roles these differentially expressed mRNAs played in these GO terms or biological pathways. The GO terms consist of three families: biological process, cellular component, and molecular function [20]. The P-value denotes the significance of GO terms enrichment and the pathway correlation.

LncRNA classification and subgroup analysis

Differentially expressed large intergenic noncoding RNAs (lincRNAs) and nearby coding gene (distance < 300 kb) pairs were screened by the criterion that both lincRNAs and its nearby coding genes were changed >2.0-fold.

5′-RACE

The Smarter RACE 5′/3′ Kit (Clontech, CA, USA) was used to identify the transcriptional start site of AK048794 gene according to the manufacturer's instructions. AK048794-specific primers were used for the amplification by primary PCR and nested PCR. After purifying, the PCR products were then cloned into vectors using the In-Fusion HD Cloning Kit (Clontech, CA, USA) and sequenced.

Transcription factor binding CHART-PCR

Accessibility of DNA to digestion with DNase I (Takara, Dalian, China) was analyzed using chromatin accessibility by real-time PCR (CHART-PCR) as described previously [21,22]. After purification, 0.1 μg of DNA from nuclease-digested or nondigested control cells was used in semi-quantitative PCR. Percent protection was calculated as the amount of DNA recovered from the digested cells relative to the control cells.

Construction of reporter and expression plasmids and mutagenesis

The fragment of AK048794 containing the miR-592-binding site was subcloned into the Xbal restriction site downstream of the Firefly luciferase gene of pGL3-Control Vector (Promega, USA) as Rluc-AK048794, while Rluc-ΔAK048794 contained that fragment without miR-592-binding site. The pRL-TK vector (Promega, USA) containing Renilla luciferase gene was used as an internal control reporter vector. According to the short hairpin RNA design principle and the multiple clone site of pSuper EGFP1, we designed the siRNAs for AK048794, and GATA1. The R5 region of AK048794 was amplified by PCR and cloned upstream of the luciferase gene of pGL3-Basic vector (Promega, USA) as pGL3-R5. The vectors of pGL3-R5a, pGL3-R5b, pGL3-cdxA1, pGL3-cdxA2, pGL3-SRY, and pGL3-GATA1 were also obtained by using the above method. All constructs were confirmed by DNA sequencing.

MiRNA mimics and 2′-O′-methyl oligonucleotide

MiR-592 mimics and miR-592 mutant mimics were chemically synthesized by Exiqon (Denmark). miRCURY LNA™ microRNA inhibitor, 5′-fluorescein-labeled, complementary to miR-592 was chemically synthesized by Exiqon (Denmark). The miRCURY LNA™ microRNA Mimic Negative Control and miRCURY LNA™ microRNA inhibitor negative control were also obtained from Exiqon.

Luciferase assay

In target gene identification assays, recombinant pGL3-Control plasmids containing AK048794- and/or FAM91A1-binding sites were co-transfected with miR-592 mimics as well as the internal control pRL-TK vector. In the promoter assay, promoter reporter plasmid DNA was transiently transfected into 293T cells using Xfect Transfection Reagent (Clontech, CA, USA). The pRL-TK vector (Promega, USA) containing Renilla luciferase gene was used as an internal control reporter vector. In the miRNA sensor reporter assay, recombinant pGL3-Control plasmids containing miR-592-binding sites were co-transfected with miR-592 mimics. Luciferase activity was measured as chemiluminescence in a luminometer (PerkinElmer Life Sciences, Boston, MA, USA) using the Dual-Luciferase reporter assay system (Promega, USA) according to the manufacturer's protocol.

RNA and miRNA expression analysis

In lncRNA and mRNA expression analysis, total RNA was extracted from cells using the miRNeasy Mini Kit (Qiagen). Reaction mixture containing 1 μg of RNA was reverse transcribed into cDNA using the PrimeScript™RT reagent Kit (TaKaRa, China). As for miRNA expression analysis, miRNA was extracted from miRNeasy Mini Kit (Qiagen, Germany) and RNA-tailing, primer extension, and real-time quantification of miRNAs were conducted as described previously [23]. Quantitative real-time PCR was performed using the SYBR Premix Ex Taq™ (TaKaRa, Dalian, China) with primers specific for AK048794, FAM91A1, and miRNA-592. GAPDH was used as the internal reference RNA for lncRNA and mRNA expression analysis, while 5S ribosome RNA was used for miRNA. Gene expression levels were calculated based on the comparative quantitative method (the ΔΔCT method) [24]. All real-time quantitative PCR reactions were performed in the PCR System 7500 (ABI).

Western blot

Cell lysates were lysed by RIPA buffer (Pierce, USA) with Complete Protease and Phosphatase Inhibitor Cocktail (Pierce, USA). Lysates were centrifuged at 10 000 × g for 10 min at 4°C, and the resulting supernatant was saved for Western blot. Proteins were separated by SDS–PAGE on 10% polyacrylamide gels and were transferred onto Hybond-P polyvinylidene difluoride membranes (Millipore, USA). The membranes were then incubated with antibodies against FAM91A1 (Abcam, MA, USA) and GAPDH (Abcam, MA, USA). Protein bands were visualized using IR Dye 800-conjugated secondary antibody of Rabbit IgG (Rockland, Philadelphia, USA). Images were documented and band density was analyzed using the Odyssey Infrared Imaging System (LI-COR Biosciences, USA).

Statistical methods

Data were expressed as mean ± SD. Statistical analyses were conducted with Student's t-test and performed with SAS (v8.2) software. A value of P < 0.05 was considered as statistically significant.

Results

Quality assessment of microarray data and hierarchical clustering

Box-Plot was used to visualize the distributions of the intensities from all samples. After quantile normalization, the distributions of the log2-ratios among the six samples were nearly the same (Supplementary Figure S1A,B). Then, Scatter-Plot was used to visualize the lncRNAs and mRNAs expression variation between mESCs and KO-miR-592 mESCs (Supplementary Figure S1C,D). The distinguishable lncRNAs and mRNAs expression pattern among samples were performed by hierarchical clustering (Supplementary Figure S1E,F). Cluster analysis helps us to arrange samples into groups based on their expression levels, which allows us to hypothesize about the relationships among samples.

Gene expression profile by microarray analysis

Arraystar Mouse LncRNA Microarray (v2.0) was used to analyze the global profiling of mouse lncRNAs and protein-coding transcripts in mESC and KO-miR-592 mESC. Volcano Plot filtering was applied to screen the differentially expressed lncRNAs and mRNAs with statistical significance (fold change ≥ 2.0, P < 0.05) between the two groups (Supplementary Figure S2). After quantile normalization of the raw data, the expression profiles of 27 842 lncRNAs and 20 090 mRNAs were obtained from parental mESCs and KO-miR-592 mESCs. A total of 527 lncRNAs (336 up-regulated and 191 down-regulated) and 449 mRNAs (194 up-regulated and 255 down-regulated) were significantly differently expressed in KO-miR-592 mESCs compared with parental mESCs (fold change ≥ 2.0, P < 0.05). It was worth noting that 13 lncRNAs and 30 mRNAs were highly differentially expressed with >10-fold changes. Top 10 up-regulated and down-regulated lncRNAs were listed in Table 1. Among these, ENSMUST00000117250 was the most significantly up-regulated and uc007kom.1 was found to be the most significantly down-regulated. The top 10 up-regulated and top 10 down-regulated mRNAs were presented in Table 2.

Table 1
The top 10 up-regulated and down-regulated lncRNAs
Up-regulated lncRNAs Down-regulated lncRNAs 
Sequence name Absolute fold Sequence name Absolute fold 
ENSMUST00000117250 35.84697 uc007kom.1 32.238155 
AK145778 20.749418 NR_028363 23.861416 
MM9LINCRNAEXON11092- 17.357393 ENSMUST00000166076 19.430948 
uc007qwq.1 14.566083 ENSMUST00000163114 17.8188 
uc007epc.1 13.039708 ENSMUST00000100281 11.231941 
uc007eem.1 11.266418 AK080629 10.127088 
ENSMUST00000122062 11.116644 ENSMUST00000107322 8.771743 
MM9LINCRNAEXON10181- 9.313132 ENSMUST00000144637 8.172927 
uc008rpd.1 8.825803 BU937478 7.863474 
NR_024208 8.7670765 ENSMUST00000128126 7.65482 
Up-regulated lncRNAs Down-regulated lncRNAs 
Sequence name Absolute fold Sequence name Absolute fold 
ENSMUST00000117250 35.84697 uc007kom.1 32.238155 
AK145778 20.749418 NR_028363 23.861416 
MM9LINCRNAEXON11092- 17.357393 ENSMUST00000166076 19.430948 
uc007qwq.1 14.566083 ENSMUST00000163114 17.8188 
uc007epc.1 13.039708 ENSMUST00000100281 11.231941 
uc007eem.1 11.266418 AK080629 10.127088 
ENSMUST00000122062 11.116644 ENSMUST00000107322 8.771743 
MM9LINCRNAEXON10181- 9.313132 ENSMUST00000144637 8.172927 
uc008rpd.1 8.825803 BU937478 7.863474 
NR_024208 8.7670765 ENSMUST00000128126 7.65482 
Table 2
The top 10 up-regulated and down-regulated mRNAs
Up-regulated mRNAs Down-regulated mRNAs 
Gene symbol Absolute fold Gene symbol Absolute fold 
Apoa2 67.19993 Gm8994 117.57279 
Apobec3 30.293776 Gm2022 44.09689 
Crisp3 27.377287 Gm8300 39.817192 
Crisp1 25.07871 Gm5662 38.087368 
Smcr8 14.655644 Gm13871 34.024464 
Nphs1 14.153232 Tcstv3 30.929089 
Akap9 13.887177 Zscan4c 28.902937 
Rpl29 13.356557 Thg1l 23.869658 
Vmn2r74 9.69192 Gm4340 22.76367 
4930427A07Rik 9.634988 Ccnb1ip1 20.434841 
Up-regulated mRNAs Down-regulated mRNAs 
Gene symbol Absolute fold Gene symbol Absolute fold 
Apoa2 67.19993 Gm8994 117.57279 
Apobec3 30.293776 Gm2022 44.09689 
Crisp3 27.377287 Gm8300 39.817192 
Crisp1 25.07871 Gm5662 38.087368 
Smcr8 14.655644 Gm13871 34.024464 
Nphs1 14.153232 Tcstv3 30.929089 
Akap9 13.887177 Zscan4c 28.902937 
Rpl29 13.356557 Thg1l 23.869658 
Vmn2r74 9.69192 Gm4340 22.76367 
4930427A07Rik 9.634988 Ccnb1ip1 20.434841 

Validation of the microarray data using qPCR

To validate the microarray results, we randomly selected eight differentially expressed lncRNAs (AK163718, AK029705, AK086964, AK054246, MM9LINCRNAEXON11619+, NR_028593, ENSMUST00000134140, and ENSMUST00000122159) between mESCs and KO-miR-592 mESCs to confirm their expression levels by qRT-PCR. As a result, all of them except AK029705 showed the same trends of up-regulation and down-regulation as the microarray data (Supplementary Figure S3A). Moreover, we randomly selected eight dysregulated mRNAs, consisting of four up-regulated (Vmn2r74, Crisp1, Trim12a, and Akap9) and four down-regulated (Srpk2, Gm1973, Bex6, and Tcstv1). All the eight mRNAs showed the same change patterns as shown in microarray analysis (Supplementary Figure S3B).

GO and KEGG biological analysis

The Gene Ontology project provides a controlled vocabulary to describe gene and gene product attributes in any organism (http://www.geneontology.org). We performed GO analysis for genes encoding proteins as shown in Supplementary Figure S4. The differentially expressed mRNAs were principally enriched for response to cytokine stimulus, cellular response to interferon-β, response to interferon-β linked with biological processes (Supplementary Figure S4A), and extracellular region, cell part, cell involved in cellular components (Supplementary Figure S4B), as well as binding, endopeptidase activity, protein domain-specific binding in molecular functions (Supplementary Figure S4C).

We also performed gene set analysis by mapping the differentially regulated genes to KEGG pathways to further identify target mRNAs and their common cellular process. The dysregulated mRNAs were associated with 12 biological pathways, including Herpes simplex infection, transcriptional misregulation in cancer, cytosolic DNA-sensing, HTLV-I infection, neurotrophin signaling pathway, as well as others (Supplementary Figure S4D).

LncRNA classification and subgroup analysis

Several lines of evidence revealed that most of lncRNAs may act in cis and regulate gene expressions within their chromosomal neighboring regions [25]. LincRNAs are a group of lncRNAs which are highly conserved and involved in diverse biological processes, including ESC pluripotency, cell proliferation, and so on [19,26]. The profiling data showed that 5466 lincRNAs were detected, and we found 22 differentially expressed lincRNAs-nearby coding gene pairs (distance <300 kb; Table 3).

Table 3
Differentially expressed lncRNAs and their nearby coding gene pairs that were differentially expressed in KO-miR-592 mESCs compared with parental mESCs (distance < 300 bp)
lncRNA mRNA Direction (LncRNA–mRNA) 
Sequence name Gene symbol P value Fold change Nearby gene Nearby gene symbol P value Fold change 
AI467187 mouselincRNA0937 0.03935105 2.4796956 NM_021409 Pard6b 0.019284442 2.5681632 Up–down 
MM9LINCRNAEXON10107-  0.025340622 2.144906 NM_021789 Trappc4 0.008041317 2.1427655 Up–up 
uc008uyc.1 Khdrbs1 0.04063068 2.4305396 NM_008228 Hdac1 0.017130913 2.0151606 Up–up 
MM9LINCRNAEXON10774- mouselincRNA1143 0.002995573 2.726408 NM_008228 Hdac1 0.017130913 2.0151606 Up–up 
AV469754 mouselincRNA0840 0.001124576 2.9591606 NM_001001714 Sohlh1 0.027363246 2.5312207 Up–down 
NR_015555 4933404O12Rik 0.000432049 3.0035229 NM_201373 Trim56 0.004050048 2.7075264 Down–up 
MM9LINCRNAEXON10622+ mouselincRNA1238 0.00190739 2.8206506 NM_201373 Trim56 0.004050048 2.7075264 Down–up 
MM9LINCRNAEXON10518+ mouselincRNA1347 0.022200359 3.7907264 NM_001167697 Gpr19 0.020226035 2.406554 Down–down 
MM9LINCRNAEXON10181- mouselincRNA1444 0.0104024 9.313132 NM_015802 Dlc1 0.012212455 3.317751 Up–down 
MM9LINCRNAEXON10455- mouselincRNA1287 0.007086216 2.1380143 NM_007836 Gadd45a 0.007972568 2.484614 Down–up 
uc009lxf.1 AK162599 0.034669437 2.0579424 NM_001013379 Zfp930 0.04421766 2.143631 Up–up 
MM9LINCRNAEXON10518+ mouselincRNA1347 0.022200359 3.7907264 NM_013546 Hebp1 0.0390062 2.123748 Down–down 
uc007pip.1 AK087718 0.0018373 5.8311977 NM_080857 Asb13 0.047810946 2.59445 Up–up 
ENSMUST00000129597 2810429I04Rik 0.004890576 2.9111414 NM_080857 Asb13 0.047810946 2.59445 Up–up 
MM9LINCRNAEXON10834- mouselincRNA0984 0.008094655 2.024017 NM_028667 D3Ertd751e 0.006692512 2.296628 Up–down 
AK033836  0.00354399 2.7171798 NM_001163143 C2cd4a 0.033761036 2.045644 Up–down 
ENSMUST00000148377 4930444M15Rik 0.00425595 2.8232715 NM_009366 Tsc22d1 0.005498246 3.3073585 Up–up 
NR_003270 Snhg3 0.001044681 2.1217794 NM_027925 Trnau1ap 0.005423703 2.2876732 Up–down 
ENSMUST00000141090 Snhg3 0.04487023 3.0704257 NM_027925 Trnau1ap 0.005423703 2.2876732 Up–down 
AK217838 mouselincRNA1079 0.00135239 2.001341 NM_194055 Esrp1 0.027783569 2.4297478 Up–up 
ENSMUST00000133824 Gm15514 0.022143107 4.385399 NM_001002894 Nlrp14 0.032013245 3.0130959 Down–down 
NR_028593 Gm10069 0.024204915 2.8214796 NM_133927 Itfg2 0.0440522 6.111181 Down–down 
lncRNA mRNA Direction (LncRNA–mRNA) 
Sequence name Gene symbol P value Fold change Nearby gene Nearby gene symbol P value Fold change 
AI467187 mouselincRNA0937 0.03935105 2.4796956 NM_021409 Pard6b 0.019284442 2.5681632 Up–down 
MM9LINCRNAEXON10107-  0.025340622 2.144906 NM_021789 Trappc4 0.008041317 2.1427655 Up–up 
uc008uyc.1 Khdrbs1 0.04063068 2.4305396 NM_008228 Hdac1 0.017130913 2.0151606 Up–up 
MM9LINCRNAEXON10774- mouselincRNA1143 0.002995573 2.726408 NM_008228 Hdac1 0.017130913 2.0151606 Up–up 
AV469754 mouselincRNA0840 0.001124576 2.9591606 NM_001001714 Sohlh1 0.027363246 2.5312207 Up–down 
NR_015555 4933404O12Rik 0.000432049 3.0035229 NM_201373 Trim56 0.004050048 2.7075264 Down–up 
MM9LINCRNAEXON10622+ mouselincRNA1238 0.00190739 2.8206506 NM_201373 Trim56 0.004050048 2.7075264 Down–up 
MM9LINCRNAEXON10518+ mouselincRNA1347 0.022200359 3.7907264 NM_001167697 Gpr19 0.020226035 2.406554 Down–down 
MM9LINCRNAEXON10181- mouselincRNA1444 0.0104024 9.313132 NM_015802 Dlc1 0.012212455 3.317751 Up–down 
MM9LINCRNAEXON10455- mouselincRNA1287 0.007086216 2.1380143 NM_007836 Gadd45a 0.007972568 2.484614 Down–up 
uc009lxf.1 AK162599 0.034669437 2.0579424 NM_001013379 Zfp930 0.04421766 2.143631 Up–up 
MM9LINCRNAEXON10518+ mouselincRNA1347 0.022200359 3.7907264 NM_013546 Hebp1 0.0390062 2.123748 Down–down 
uc007pip.1 AK087718 0.0018373 5.8311977 NM_080857 Asb13 0.047810946 2.59445 Up–up 
ENSMUST00000129597 2810429I04Rik 0.004890576 2.9111414 NM_080857 Asb13 0.047810946 2.59445 Up–up 
MM9LINCRNAEXON10834- mouselincRNA0984 0.008094655 2.024017 NM_028667 D3Ertd751e 0.006692512 2.296628 Up–down 
AK033836  0.00354399 2.7171798 NM_001163143 C2cd4a 0.033761036 2.045644 Up–down 
ENSMUST00000148377 4930444M15Rik 0.00425595 2.8232715 NM_009366 Tsc22d1 0.005498246 3.3073585 Up–up 
NR_003270 Snhg3 0.001044681 2.1217794 NM_027925 Trnau1ap 0.005423703 2.2876732 Up–down 
ENSMUST00000141090 Snhg3 0.04487023 3.0704257 NM_027925 Trnau1ap 0.005423703 2.2876732 Up–down 
AK217838 mouselincRNA1079 0.00135239 2.001341 NM_194055 Esrp1 0.027783569 2.4297478 Up–up 
ENSMUST00000133824 Gm15514 0.022143107 4.385399 NM_001002894 Nlrp14 0.032013245 3.0130959 Down–down 
NR_028593 Gm10069 0.024204915 2.8214796 NM_133927 Itfg2 0.0440522 6.111181 Down–down 

Interaction between AK048794 and miR-592

Previous studies have suggested that lncRNAs may have the binding site of miRNAs and interact with miRNAs to influence the function of miRNAs [27,28]. To understand which lncRNA may act as endogenous sponge RNAs for miR-592 and be involved in mESCs self-renewal and differentiation into neurons, we applied RegRNA 2.0 and Target Scan online software to find 14 putative target lncRNAs for miR-592, with the highest difference noted for AK048794 (Table 4). The microarray results of AK048794 were consistent with qRT-PCR data (Figure 1A), and the RT-PCR analysis indicates that AK048794 was polyadenylated and located in both nucleas and cytoplasm (Figure 1B). AK048794 was originated from the RIKEN full-length enriched mouse cerebellum cDNA library and mapped to the intergenic region between Arv1 and Fam89a (Supplementary Figure S5). Some short reading frames can be detected in the mature transcript by Coding Protein Calculator (CPC), but none of their AUGs shows the Kozak consensus. Therefore, the AK048794 was classified as a bona fide long intergenic noncoding (linc) RNA.

LincRNA AK048794 profiling.

Figure 1.
LincRNA AK048794 profiling.

(A) Validation of AK048794 microarray data by qRT-PCR. (B) RT-PCR for AK048794 expression performed in mESCs. The nuclear (nuc) and cytoplasmic (cyt) polyadenylated (pA+) RNA fractions are shown.

Figure 1.
LincRNA AK048794 profiling.

(A) Validation of AK048794 microarray data by qRT-PCR. (B) RT-PCR for AK048794 expression performed in mESCs. The nuclear (nuc) and cytoplasmic (cyt) polyadenylated (pA+) RNA fractions are shown.

Table 4
Fourteen up-regulated lncRNAs having an miR-592-binding site
Sequence name Gene symbol P value Fold change Regulation 
AK048794  0.007335324 7.8555846 Up 
uc009jim.1 AK043303 0.001695412 5.034281 Up 
uc009sfx.1 Cdcp1 0.000315732 4.056129 Up 
AK138169  0.014923606 2.495471 Up 
AK135874  0.006883984 2.4948838 Up 
AK029705  0.009367746 2.3214118 Up 
NR_004443 Nphs1as 0.027498005 2.3021297 Up 
AK050897  0.003294098 2.2856145 Up 
NR_028110 Ppp2r3d 0.008902455 2.2435775 Up 
AK086964  0.040805608 2.186464 Up 
uc009she.1 Ppp2r3a 0.019926637 2.117916 Up 
AK054246  0.005898508 2.0948906 Up 
AK163718  0.023245405 2.0909636 Up 
AK140555  0.030321233 2.0306587 Up 
Sequence name Gene symbol P value Fold change Regulation 
AK048794  0.007335324 7.8555846 Up 
uc009jim.1 AK043303 0.001695412 5.034281 Up 
uc009sfx.1 Cdcp1 0.000315732 4.056129 Up 
AK138169  0.014923606 2.495471 Up 
AK135874  0.006883984 2.4948838 Up 
AK029705  0.009367746 2.3214118 Up 
NR_004443 Nphs1as 0.027498005 2.3021297 Up 
AK050897  0.003294098 2.2856145 Up 
NR_028110 Ppp2r3d 0.008902455 2.2435775 Up 
AK086964  0.040805608 2.186464 Up 
uc009she.1 Ppp2r3a 0.019926637 2.117916 Up 
AK054246  0.005898508 2.0948906 Up 
AK163718  0.023245405 2.0909636 Up 
AK140555  0.030321233 2.0306587 Up 

To verify the interaction between AK048794 and miR-592, the binding sites in AK048794 were cloned into luciferase reporter pGL3-Control vector, and the constructs were co-transfected into 293T cells with miR-592 mimics. Luciferase assays revealed that overexpression of miR-592 could significantly reduce the Renilla luciferase activities from the reporter RLuc-AK048794 (Figure 2A). To confirm this putative target site of miR-592, deletion assay was performed. Luciferase assays indicated that overexpression of miR-592 had no effectiveness on deleted AK048794 RNA compared with the controls. Meanwhile, microRNA Mimic Negative Control abolished the suppressive effects on the reporter RLuc-AK048794. Furthermore, overexpression of miR-592 suppressed the luciferase activity of luciferase reporter RLuc-AK048794 in a dose-dependent manner (Figure 2B).

Interaction between AK048794 and miR-592.

Figure 2.
Interaction between AK048794 and miR-592.

(A) pGL3-Basic vectors containing AK048794 RNA with (RLuc-AK048794, wild-type) or without (Rluc-ΔAK048794, deletion) predicted miR-592-binding site were co-transfected with miR-592 or miR-592mut into 293T cells. Luciferase assays were performed 24 h posttransfection. *P < 0.05. (B) The indicated amount of RLuc-AK048794 and/or pGL3-Basic vectors were transfected into 293T cells with miR-592 mimics. Luciferase assays were performed 24 h posttransfection. (C) Total RNA from mESCs was extracted after 24 h treatment with either siAK048794 or pSupper-EGFP control, as indicated. Then AK048794 RNA levels were measured by real-time PCR. *P < 0.05. (D) Small RNA from mESCs was extracted after 24 h treatment with either siAK048794 or pSupper-EGFP control, as indicated. Then miR-592 expression levels were detected by real-time PCR. *P < 0.05. (E) The miR-592 sensor reporter plasmid was co-transfected with siAK048794 into 293T cells and subjected to luciferase assay. *P < 0.05, versus cells transfected with empty vectors. All results are shown as means of three independent experiments ± standard deviation (SD).

Figure 2.
Interaction between AK048794 and miR-592.

(A) pGL3-Basic vectors containing AK048794 RNA with (RLuc-AK048794, wild-type) or without (Rluc-ΔAK048794, deletion) predicted miR-592-binding site were co-transfected with miR-592 or miR-592mut into 293T cells. Luciferase assays were performed 24 h posttransfection. *P < 0.05. (B) The indicated amount of RLuc-AK048794 and/or pGL3-Basic vectors were transfected into 293T cells with miR-592 mimics. Luciferase assays were performed 24 h posttransfection. (C) Total RNA from mESCs was extracted after 24 h treatment with either siAK048794 or pSupper-EGFP control, as indicated. Then AK048794 RNA levels were measured by real-time PCR. *P < 0.05. (D) Small RNA from mESCs was extracted after 24 h treatment with either siAK048794 or pSupper-EGFP control, as indicated. Then miR-592 expression levels were detected by real-time PCR. *P < 0.05. (E) The miR-592 sensor reporter plasmid was co-transfected with siAK048794 into 293T cells and subjected to luciferase assay. *P < 0.05, versus cells transfected with empty vectors. All results are shown as means of three independent experiments ± standard deviation (SD).

To further investigate the regulation role of AK048794 on miR-592, the effect of siRNA-mediated knockdown of AK048794 expression was investigated. Knockdown efficiency of 50% was obtained for AK048794 siRNA (Figure 2C). The expression levels of miR-592 were increased in siAK048794-treated mESCs compared with those treated with negative control siRNA (Figure 2D). To verify whether AK048794 can affect miR-592 activities, sensors were constructed. As expected, the Renilla luciferase activities of miR-592 sensors greatly decreased when 293T cells were treated with siRNA against AK048794 compared with those of negative control (Figure 2E). These results indicated that AK048794 may act as an miRNA decoy for miR-592.

AK048794-modulated expression of endogenous miR-592 targets FAM91A1

To determine whether AK048794 regulates mESCs proliferation or/and differentiation by affecting miR-592 targets, we did a search for miR-592 target mRNAs through online software: Targetscan (www.targetscan.org). With the differentially expressed mRNAs in KO-miR-592 mESCs compared with parental mESCs (fold change ≥ 2.0, P < 0.05), we found out that FAM91A1 was a putative target of miR-592 with the highest context + score percentile by target scan (Figure 3A). Furthermore, the luciferase reporter assay showed that miR-592 inhibited luciferase activity of luciferase reporter harboring 3′-UTR region of FAM91A1 gene (Figure 3B). Meanwhile, enhanced miR-592 expression in mESCs dramatically down-regulates FAM91A1 mRNA and protein levels, suggesting that miR-592 affects FAM91A1 at the transcriptional level by inducing mRNA degradation (Figure 3C,D). In conclusion, the above data demonstrate that endogenous miR-592 inhibits FAM91A1 mRNA translation in mESCs by binding to the FAM91A1 3′-UTR.

miR-592 target FAM91A1 identified with bioinformatics and luciferase assay.

Figure 3.
miR-592 target FAM91A1 identified with bioinformatics and luciferase assay.

(A) Summary of FAM91A1 containing miR-592-binding site with a highest context + score percentile. (B) miR-592 specifically represses its targets in the luciferase assay in 293T cells. Relative luciferase level = (Sluc/Srenilla)/(Cluc/Crenilla). Luc, raw firefly luciferase activity; Renilla, internal transfection control, renilla activity; **P < 0.01. The error bar represents the SD from three independent experiments. Expression levels of FAM91A1 were analyzed in mESCs transfected with miR-592 and/or miR-592mut. In real-time PCR, GAPDH mRNA is the normalization control (C). In western blot (D), protein level quantification was normalized to GAPDH. Data were expressed as fold increase in relative mRNA and protein expression to that in cells with microRNA Mimic Negative Control-transfection, and were presented as mean ± SE of three different experiments. *P < 0.05. The expression mRNA levels (E) and the protein levels (F) of FAM91A1 were analyzed in both control mESCs and KO-miR-592-mESCs after siAK048794 transfection, while pSupper-EGFP vectors were transfected as internal controls. *P < 0.05. All results are shown as means of three independent experiments  ± SD.

Figure 3.
miR-592 target FAM91A1 identified with bioinformatics and luciferase assay.

(A) Summary of FAM91A1 containing miR-592-binding site with a highest context + score percentile. (B) miR-592 specifically represses its targets in the luciferase assay in 293T cells. Relative luciferase level = (Sluc/Srenilla)/(Cluc/Crenilla). Luc, raw firefly luciferase activity; Renilla, internal transfection control, renilla activity; **P < 0.01. The error bar represents the SD from three independent experiments. Expression levels of FAM91A1 were analyzed in mESCs transfected with miR-592 and/or miR-592mut. In real-time PCR, GAPDH mRNA is the normalization control (C). In western blot (D), protein level quantification was normalized to GAPDH. Data were expressed as fold increase in relative mRNA and protein expression to that in cells with microRNA Mimic Negative Control-transfection, and were presented as mean ± SE of three different experiments. *P < 0.05. The expression mRNA levels (E) and the protein levels (F) of FAM91A1 were analyzed in both control mESCs and KO-miR-592-mESCs after siAK048794 transfection, while pSupper-EGFP vectors were transfected as internal controls. *P < 0.05. All results are shown as means of three independent experiments  ± SD.

To verify whether AK048794 can function as a sponge for miR-592, we utilized siRNA against AK048794 and examined the effect of AK048794 in modulating FAM91A1 expression in mESCs and KO-miR-592 mESCs, respectively. The results showed that down-regulation of AK048794 lead to a reduction in FAM91A1 expression levels, whereas no changes were detected in KO-miR-592 mESCs (Figure 3E,F). The results indicated that down-regulation of the expression levels of AK048794 would liberate miR-592 and improve the repression for FAM91A1, while no changes were observed in the absence of miR-592. Altogether, these data indicate that AK048794, by binding miR-592, acts as a ceRNA for its target gene — FAM91A1.

GATA1-binding site contributes to the activation of AK048794 promoter

The transcription start site (TSS) of the AK048794 was identified by 5′-RACE analysis and the sequencing of PCR products indicated that the first base was cytosine (A; Figure 4A), which located 421 bp upstream from the first base provided by UCSC Genome Bioinformatics. Then, we performed CHART-PCR assays to search for the regulation sites at the promoter region. Nuclei of mESCs were isolated and subjected to digestion with DNase I, and the genomic fragments between −1500 and +500 bp (relative to the TSS) were analyzed (Figure 4B). As shown in Figure 4C, the regions spanning the proximal promoter R5 region was more sensitive to DNase I digestion than other regions and exhibited the lowest protection level of 18% in mESCs. Furthermore, to confirm the results performed by DNase I, R5 region was cloned upstream of firefly luciferase reporter vectors. The promoter reporter containing R5 region induced a significant increase in luciferase activity compared with the controls (Figure 4D).

Analysis of AK048794 promoter activity.

Figure 4.
Analysis of AK048794 promoter activity.

(A) 5′-RACE was performed using the reverse 5′-RACE nested primer (left panel). Mapping the TSS of AK048794 in mESCs (right panel). (B) Schematic representation of the DNA regions around AK048794 promoter, which can be amplified by corresponding primer sets. (C) DNase I accessibility of AK048794 gene in mESCs. Purified DNA from mESCs was harvested and treated with DNase I for 5 min at room temperature. Then the genomic DNA was purified and quantitated relative to DNA from undigested nuclei using the primers described in (B) by quantitative PCR and listed as percent protected. **P < 0.01. (D) pGL3-R5 vectors transfected into 293T cells and the luciferase assays were performed 24 h posttransfection. **P < 0.01. (E) R5 region was divided into two fragments: A and B. Transcription factor-binding sites present in this R5 promoter region (from −200 to −133) were predicated by web software TFSEARCH and MatInspector, as indicated by the rectangle box. mESCs were transfected with (F) pGL3-R5a, pGL3-R5b, and (G) pGL3-cdxA1, pGL3-cdxA2, pGL3-SRY and pGL3-GATA1. Firefly luciferase activity was normalized to Renilla luciferase activity, and the relative luciferase activities are presented as fold increase over the promoter-less pGL3-basic vector. **P < 0.01 and *P < 0.05. The error bar represents the SD from three independent experiments.

Figure 4.
Analysis of AK048794 promoter activity.

(A) 5′-RACE was performed using the reverse 5′-RACE nested primer (left panel). Mapping the TSS of AK048794 in mESCs (right panel). (B) Schematic representation of the DNA regions around AK048794 promoter, which can be amplified by corresponding primer sets. (C) DNase I accessibility of AK048794 gene in mESCs. Purified DNA from mESCs was harvested and treated with DNase I for 5 min at room temperature. Then the genomic DNA was purified and quantitated relative to DNA from undigested nuclei using the primers described in (B) by quantitative PCR and listed as percent protected. **P < 0.01. (D) pGL3-R5 vectors transfected into 293T cells and the luciferase assays were performed 24 h posttransfection. **P < 0.01. (E) R5 region was divided into two fragments: A and B. Transcription factor-binding sites present in this R5 promoter region (from −200 to −133) were predicated by web software TFSEARCH and MatInspector, as indicated by the rectangle box. mESCs were transfected with (F) pGL3-R5a, pGL3-R5b, and (G) pGL3-cdxA1, pGL3-cdxA2, pGL3-SRY and pGL3-GATA1. Firefly luciferase activity was normalized to Renilla luciferase activity, and the relative luciferase activities are presented as fold increase over the promoter-less pGL3-basic vector. **P < 0.01 and *P < 0.05. The error bar represents the SD from three independent experiments.

We then performed a prediction using the software MatInspector and TFSEARCH to investigate the possible transcription factor-binding sites on the R5, and it revealed nine motif-binding sites within this region. To confirm the role of R5 in transcriptional activity, A fragment and B fragment of R5 region were cloned into the pGL3-Basic vector separately, yielding the pGL3-R5a and pGL3-R5b vectors (Figure 4E). The results indicated that a significant change in luciferase activity was detected after pGL3-R5a transfected into 293T cells compared with that of pGL3-R5b. (Figure 4F). Then, four reporter constructors containing the putative transcription factor-binding sites from R5b region were generated through PCR amplification from genomic DNA of mESCs. As shown in Figure 4G, pGL3-GATA1 and pGL3-SRY increased promoter activity especially pGL3-GATA1 exhibited the highest level, whereas no significant activity was observed in the other constructs. To further confirm GATA1 in transcription activity for AK048794, siGATA1 and pGL3-GATA1 were co-transfected into 293T cells, and the promoter activities were decreased (Figure 5A). What's more, when siGATA1 transfected into mESCs, it resulted in the down-regulation of AK048794 and FAM91A1 at both mRNA and protein levels, whereas an up-regulation was detected for miR-592 (Figure 5B–E). Taken together, all these results demonstrated that GATA1 motif may act as an enhancer necessary for increasing transcriptional activity of AK048794.

GATA1 activation promotes AK048794 expression.

Figure 5.
GATA1 activation promotes AK048794 expression.

(A) Down-regulation of GATA1 by the siRNA against GATA1 (siGATA1) decreased GATA1-dependent reporter expression. Relative luciferase level = (Sluc/Srenilla)/(Cluc/Crenilla). *P < 0.05. The error bar represents the SD from three independent experiments. (B and C) Endogenous AK048794 and miR-592 expression levels were quantified by real-time PCR in mESCs after transfection with siGATA1. The expression mRNA levels (D) and protein levels (E) of FAM91A1 were detected in mESCs after transfection with siGATA1 by real-time PCR and western blot. GAPDH and U6 are the normalization control. Data are represented as mean ± SEM. *P < 0.05, n = 3.

Figure 5.
GATA1 activation promotes AK048794 expression.

(A) Down-regulation of GATA1 by the siRNA against GATA1 (siGATA1) decreased GATA1-dependent reporter expression. Relative luciferase level = (Sluc/Srenilla)/(Cluc/Crenilla). *P < 0.05. The error bar represents the SD from three independent experiments. (B and C) Endogenous AK048794 and miR-592 expression levels were quantified by real-time PCR in mESCs after transfection with siGATA1. The expression mRNA levels (D) and protein levels (E) of FAM91A1 were detected in mESCs after transfection with siGATA1 by real-time PCR and western blot. GAPDH and U6 are the normalization control. Data are represented as mean ± SEM. *P < 0.05, n = 3.

AK048794 regulates endogenous Oct4, Nanog, and Sox2 expression in self-renewing mESCs

To investigate the role of miR-592 in mESC pluripotency maintenance and differentiation, the expression of pluripotent markers was analyzed in mESCs and KO-miR-592 mESCs. It revealed that the expression levels of Oct4, Sox2, and Nanog were up-regulated in KO-miR-592 mESCs (Figure 6A). Then, to verify whether miR-592 was inhibited by AK048794 under GATA1 control and influences Oct4, Sox2, and Nanog expression levels, we performed RNA interference experiment in control mESCs. The results showed that mRNA and protein levels of Oct4, Sox2, and Nanog were decreased when si-AK048794 and/or si-GATA1 were transfected into control mESCs under self-renewal conditions (Figure 6B–D). Moreover, stable expression of miR-592 restored si-AK048794 and/or si-GATA1-mediated suppression on Oct4, Sox2, and Nanog expression in control mESCs. To determine whether AK048794 could regulate mESC pluripotency by affecting miR-592 targets, we up-regulated miR-592 and/or down-regulated AK048794 in control and KO-miR-592 mESCs, respectively (Figure 6E,F). Overexpression of miR-592 resulted in reduction in Oct4, Sox2, and Nanog expression levels in both mESCs and KO-miR-592 mESCs. However, a decrease in the mRNA and protein levels of Oct4, Sox2, and Nanog was observed after siAK048794 transfected into mESCs, while no significant changes were detected in KO-miR-592 mESCs, indicating that AK048794 regulates mESC pluripotency through binding with miR-592. On the other hand, overexpression of FAM91A1 eliminated siAK048794-mediated suppression on Oct4, Sox2, and Nanog expression in mESCs, and led to an increase in KO-miR-592 mESCs. Taken together, these results suggest that AK048794 may interfere with miR-592-mediated inhibition of FAM91A1 and play an important role for mESC pluripotency maintenance.

AK048794 regulates Oct4, Nanog, and Sox2 expression in self-renewing mESCs.

Figure 6.
AK048794 regulates Oct4, Nanog, and Sox2 expression in self-renewing mESCs.

(A) Relative mRNA levels of OCT4, SOX2, and NANOG in KO-miR-592 mESC samples and parental control mESCs. Relative mRNA levels of OCT4 (B), SOX2 (C), and NANOG (D) in mESCs transfected with siAK048794, siGATA1, siAK048794 with anti-miR-592, and siGATA1 with anti-miR-592. Relative protein levels of OCT4, SOX2, and NANOG in (E) mESCs and (F) KO-miR-592 mESCs transfected with miR-592 mimic, siAK048794, and siAK048794 with FAM91A1. RNA and protein levels were assayed by quantitative real-time PCR and western blot analysis; GAPDH and U6 are the normalization control. Data are represented as mean ± SEM. **P < 0.01 and *P < 0.05, n = 3.

Figure 6.
AK048794 regulates Oct4, Nanog, and Sox2 expression in self-renewing mESCs.

(A) Relative mRNA levels of OCT4, SOX2, and NANOG in KO-miR-592 mESC samples and parental control mESCs. Relative mRNA levels of OCT4 (B), SOX2 (C), and NANOG (D) in mESCs transfected with siAK048794, siGATA1, siAK048794 with anti-miR-592, and siGATA1 with anti-miR-592. Relative protein levels of OCT4, SOX2, and NANOG in (E) mESCs and (F) KO-miR-592 mESCs transfected with miR-592 mimic, siAK048794, and siAK048794 with FAM91A1. RNA and protein levels were assayed by quantitative real-time PCR and western blot analysis; GAPDH and U6 are the normalization control. Data are represented as mean ± SEM. **P < 0.01 and *P < 0.05, n = 3.

Discussion

It is becoming largely accepted that the noncoding portion of the genome rather than its coding counterpart is likely to account for the greater complexity of higher eukaryotes [2931]. MicroRNAs have been identified as essential posttranscriptional modulators during ESC differentiation. Likewise, long noncoding RNAs are now attracting much interest and emerge as a new class of regulatory layer in the complexity of mammalian gene regulatory networks [8,32]. Recently, the ceRNA hypothesis propose that a large number of noncoding RNAs might function as molecular sponges for miRNAs and hence functionally liberate other RNA transcripts targeted by the aforementioned active miRNA [15,19,33,34]. In the present study, we identified AK048794 as an endogenous miRNA sponge for miR-592 and abolished the endogenous suppressive effect on FAM91A1 to affect mESC pluripotency.

Our previous study has identified miR-592 as a neural-enriched miRNA that plays a crucial role in NPCs differentiation [7]. To further elucidate the molecular mechanisms of miR-592-induced neurogenesis, we performed a comprehensive analysis of lncRNAs profile between control mESCs and KO-miR592 mESCs by microarray. A total of 27 842 lncRNAs and 20 090 mRNAs were detected. By the microarray analysis, we identified 336 up-regulated and 101 down-regulated lncRNAs that were significantly differentially expressed (fold change ≥ 2.0, P < 0.05) in KO-miR592 mESCs in comparison with parental control mESCs, and summarized their general characteristics and functional annotations. Simultaneously, microarray data also identified 359 differentially expressed protein-coding mRNAs. The functional properties of lncRNAs seem to be associated, in part, with their genomic context. However, no data base can be applied to identify the functional annotations of lncRNAs, and most of lncRNAs functions were not well understood. One approach for the functional prediction of lncRNAs was performed by GO and pathway analysis based on the differentially expressed mRNAs which are the potential targets of lncRNAs [25]. Using this method, we showed the possible functions of the differentially expressed miR-592-related lncRNAs indirectly. The GO results gave us an overview of the biological process, cellular component, and molecular function of the differentially expressed mRNAs. Through GO analysis, we found that the differentially expressed mRNAs were principally enriched for response to cytokine stimulus and interferon-β (Supplementary Figure S4A). The pathway analysis showed that the dysregulated mRNAs were associated with several biological pathways, including Herpes simplex infection, transcriptional misregulation in cancer, cytosolic DNA-sensing, HTLV-I infection, neurotrophin signaling pathway, as well as others (Supplementary Figure S4D). These results indicated the general functional roles of the differentially expressed lncRNAs and help us to find some important lncRNA–mRNA pairs that contribute to the miR-592-induced neurogenesis.

According to the ceRNA hypothesis, lncRNAs may elicit their biological activity through their ability to act as endogenous decoys for miRNAs; such activity would in turn affect the distribution of miRNAs on their targets [15]. In order to find out which lncRNA could function as ‘miR-592 sponge’, we searched for lncRNAs containing miR-592 recognition motifs from these dysregulated lncRNAs. The result showed that AK048794 sequence contained the miR-592 binding site with significant up-regulation in KO-miR592 mESCs compared to the normal control mESCs. AK048794 was validated as a target for miR-592, since it induced translational repression of a reporter gene. Meanwhile, not only the activities but also expression levels of miR-592 can be affected by AK048794. Thus, we conclude that AK048794 may act as a competing endogenous ‘miRNA sponge’ for miR-592, and hence may functionally liberate other RNA transcripts targeted by miR-592. Thus, up-regulated AK048794 increases the cellular concentrations of particular miR-592 response elements and can reduce the repression of other transcripts that contain the same elements. According to the expression levels of AK048794 in KO-miR592 mESCs, we can speculate that target genes sharing the same binding site of miR-592 should have a similar expression profile to AK048794. Based on this hypothesis and the above data, we identified FAM91A1 as a target gene of miR-592. But the biological function of FAM91A1 is largely unknown. Subsequent data proved that depletion of AK048794 reduced the levels of FAM91A1 in mESCs, while no changes were detected in KO-miR592 mESCs. These data indicated that AK048794 functioned as an miRNA decoy for miR-592 and abolished the endogenous suppressive effect on FAM91A1 through binding to miR-592.

On the other hand, we found the specific interaction of GATA1 with the AK048794 promoter through its binding site located between −200 and −133 nt (Figure 4E), which contributes to the activation of the AK048794 promoter. Down-regulation of AK048794 by siGATA1 could enhance miR-592 activities which induce the mRNA degradation of FAM91A1. GATA1 belongs to the GATA family of transcription factors. It plays an important role in erythroid development by regulating the switch of fetal hemoglobin to adult hemoglobin [35]. Mutations in this gene have been associated with X-linked dyserythropoietic anemia and thrombocytopenia [3638].

Additionally, we found that decreased expression of AK048794 negatively modulated the expression levels of Oct4, Sox2, and Nanog. And rescue of AK048794 by using anti-miR-592 and FAM91A1 could lead to a partial reverse for Oct4, Sox2, and Nanog expression levels in mESCs, while no significant changes were detected in KO-miR-592 mESCs. These data allowed us to conclude that AK048794 serves as a ceRNA by sequestering miR-592 to maintain mESC pluripotency. This mechanism will lead to a better understanding of the role of miR-592 in the regulation of neurogenesis.

Abbreviations

lncRNAs, long noncoding RNAs; ceRNA, competing endogenous RNA; ESCs, embryonic stem cells; FAM91A1, family with sequence similarity 91, member A1; GO, gene ontology; HTLV-I, human T-cell lymphotropic virus I; KEGG, Kyoto Encyclopedia of Genes and Genomes; lincRNAs, large intergenic noncoding RNAs; mESCs, miR-592 knockout mouse embryonic stem cells; miRNAs, microRNAs; mNPCs, mouse neural progenitor cells; nt, nucleotides; SD, standard deviation; TSS, transcription start site.

Author Contribution

Y.Z., Q.-S.D., S.-C.Z., Y.-H.H., H.-L.H., B.Z., and R.-R.G. performed the experiments. J.Z. and J.Z. supervised the experiments and all authors analyzed the results. Y.Z. designed the project and wrote the paper.

Funding

This work was supported by the Natural Science Foundation of China [31371379 and 31271120] and the National Key Basic Research Program of China [2011CB965102].

Competing Interests

The Authors declare that there are no competing interests associated with the manuscript.

References

References
1
Cimadamore
,
F.
,
Amador-Arjona
,
A.
,
Chen
,
C.
,
Huang
,
C.-T.
and
Terskikh
,
A.V.
(
2013
)
SOX2-LIN28/let-7 pathway regulates proliferation and neurogenesis in neural precursors
.
Proc. Natl Acad. Sci. USA
110
,
E3017
E3026
doi:
2
Melton
,
C.
and
Blelloch
,
R
. (
2010
)
MicroRNA regulation of embryonic stem cell self-renewal and differentiation
.
Adv. Exp. Med. Biol.
695
,
105
117
doi:
3
Melton
,
C.
,
Judson
,
R.L.
and
Blelloch
,
R
. (
2010
)
Opposing microRNA families regulate self-renewal in mouse embryonic stem cells
.
Nature
463
,
621
626
doi:
4
Brett
,
J.O.
,
Renault
,
V.M.
,
Rafalski
,
V.A.
,
Webb
,
A.E.
and
Brunet
,
A.
(
2011
)
The microRNA cluster miR-106b∼25 regulates adult neural stem/progenitor cell proliferation and neuronal differentiation
.
Aging
3
,
108
124
doi:
5
Wang
,
J.
,
Park
,
J.W.
,
Drissi
,
H.
,
Wang
,
X.
and
Xu
,
R.-H.
(
2014
)
Epigenetic regulation of miR-302 by JMJD1C inhibits neural differentiation of human embryonic stem cells
.
J. Biol. Chem.
289
,
2384
2395
doi:
6
Cui
,
Y.
,
Xiao
,
Z.
,
Chen
,
T.
,
Wei
,
J.
,
Chen
,
L.
,
Liu
,
L.
et al.  (
2014
)
The miR-7 identified from collagen biomaterial-based three-dimensional cultured cells regulates neural stem cell differentiation
.
Stem Cells Dev.
23
,
393
405
doi:
7
Zhang
,
J.
,
Zhang
,
J.
,
Zhou
,
Y.
,
Wu
,
Y.
,
Ma
,
L.
,
Wang
,
R.
et al.  (
2013
)
Novel cerebellum-enriched miR-592 may play a role in neural progenitor cell differentiation and neuronal maturation through regulating Lrrc4c and Nfasc in rat
.
Curr. Mol. Med.
13
,
1432
1445
doi:
8
Mercer
,
T.R.
,
Dinger
,
M.E.
and
Mattick
,
J.S.
(
2009
)
Long non-coding RNAs: insights into functions
.
Nat. Rev. Genet.
10
,
155
159
doi:
9
Carninci
,
P.
,
Kasukawa
,
T.
,
Katayama
,
S.
,
Gough
,
J.
,
Frith
,
M.C.
,
Maeda
,
N.
et al.  (
2005
)
The transcriptional landscape of the mammalian genome
.
Science
309
,
1559
1563
doi:
10
Clark
,
B.S.
and
Blackshaw
,
S.
(
2014
)
Long non-coding RNA-dependent transcriptional regulation in neuronal development and disease
.
Front. Genet.
5
,
164
doi:
11
Briggs
,
J.A.
,
Wolvetang
,
E.J.
,
Mattick
,
J.S.
,
Rinn
,
J.L.
and
Barry
,
G.
(
2015
)
Mechanisms of long non-coding RNAs in mammalian nervous system development, plasticity, disease, and evolution
.
Neuron
88
,
861
877
doi:
12
Barry
,
G.
,
Briggs
,
J.A.
,
Vanichkina
,
D.P.
,
Poth
,
E.M.
,
Beveridge
,
N.J.
,
Ratnu
,
V.S.
et al.  (
2014
)
The long non-coding RNA Gomafu is acutely regulated in response to neuronal activation and involved in schizophrenia-associated alternative splicing
.
Mol. Psychiatry
19
,
486
494
doi:
13
Huarte
,
M.
,
Guttman
,
M.
,
Feldser
,
D.
,
Garber
,
M.
,
Koziol
,
M.J.
,
Kenzelmann-Broz
,
D.
et al.  (
2010
)
A large intergenic noncoding RNA induced by p53 mediates global gene repression in the p53 response
.
Cell
142
,
409
419
doi:
14
Khalil
,
A.M.
,
Guttman
,
M.
,
Huarte
,
M.
,
Garber
,
M.
,
Raj
,
A.
,
Morales
,
D.R.
et al.  (
2009
)
Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression
.
Proc. Natl Acad. Sci. USA
106
,
11667
11672
doi:
15
Salmena
,
L.
,
Poliseno
,
L.
,
Tay
,
Y.
,
Kats
,
L.
and
Pandolfi
,
P.P.
(
2011
)
A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language?
Cell
146
,
353
358
doi:
16
Poliseno
,
L.
,
Salmena
,
L.
,
Zhang
,
J.
,
Carver
,
B.
,
Haveman
,
W.J.
and
Pandolfi
,
P.P.
(
2010
)
A coding-independent function of gene and pseudogene mRNAs regulates tumour biology
.
Nature
465
,
1033
1038
doi:
17
Cho
,
M.S.
,
Lee
,
Y.-E.
,
Kim
,
J.Y.
,
Chung
,
S.
,
Cho
,
Y.H.
,
Kim
,
D.-S.
et al.  (
2008
)
Highly efficient and large-scale generation of functional dopamine neurons from human embryonic stem cells
.
Proc. Natl Acad. Sci. USA
105
,
3392
3397
doi:
18
Dinger
,
M.E.
,
Amaral
,
P.P.
,
Mercer
,
T.R.
,
Pang
,
K.C.
,
Bruce
,
S.J.
,
Gardiner
,
B.B.
et al.  (
2008
)
Long noncoding RNAs in mouse embryonic stem cell pluripotency and differentiation
.
Genome Res.
18
,
1433
1445
doi:
19
Wang
,
Y.
,
Xu
,
Z.
,
Jiang
,
J.
,
Xu
,
C.
,
Kang
,
J.
,
Xiao
,
L.
et al.  (
2013
)
Endogenous miRNA sponge lincRNA-RoR regulates Oct4, Nanog, and Sox2 in human embryonic stem cell self-renewal
.
Dev. Cell
25
,
69
80
doi:
20
Ashburner
,
M.
,
Ball
,
C.A.
,
Blake
,
J.A.
,
Botstein
,
D.
,
Butler
,
H.
,
Cherry
,
J.M.
et al.  (
2000
)
Gene ontology: tool for the unification of biology
.
Nat. Genet.
25
,
25
29
doi:
21
Rao
,
S.
,
Procko
,
E.
and
Shannon
,
M.F.
(
2001
)
Chromatin remodeling, measured by a novel real-time polymerase chain reaction assay, across the proximal promoter region of the IL-2 gene
.
J. Immunol.
167
,
4494
4503
doi:
22
Sun
,
F.
,
Xie
,
Q.
,
Ma
,
J.
,
Yang
,
S.
,
Chen
,
Q.
and
Hong
,
A.
(
2009
)
Nuclear factor Y is required for basal activation and chromatin accessibility of fibroblast growth factor receptor 2 promoter in osteoblast-like cells
.
J. Biol. Chem.
284
,
3136
3147
doi:
23
Shi
,
R.
and
Chiang
,
V.L.
(
2005
)
Facile means for quantifying microRNA expression by real-time PCR
.
BioTechniques
39
,
519
525
doi:
24
Livak
,
K.J.
and
Schmittgen
,
T.D.
(
2001
)
Analysis of relative gene expression data using real-time quantitative PCR and the method
.
Methods
25
,
402
408
doi:
25
Guttman
,
M.
,
Donaghey
,
J.
,
Carey
,
B.W.
,
Garber
,
M.
,
Grenier
,
J.K.
,
Munson
,
G.
et al.  (
2011
)
lincRNAs act in the circuitry controlling pluripotency and differentiation
.
Nature
477
,
295
300
doi:
26
Loewer
,
S.
,
Cabili
,
M.N.
,
Guttman
,
M.
,
Loh
,
Y.-H.
,
Thomas
,
K.
,
Park
,
I.H.
et al.  (
2010
)
Large intergenic non-coding RNA-RoR modulates reprogramming of human induced pluripotent stem cells
.
Nat. Genet.
42
,
1113
1117
doi:
27
Tay
,
Y.
,
Kats
,
L.
,
Salmena
,
L.
,
Weiss
,
D.
,
Tan
,
S.M.
,
Ala
,
U.
et al.  (
2011
)
Coding-independent regulation of the tumor suppressor PTEN by competing endogenous mRNAs
.
Cell
147
,
344
357
doi:
28
Cazalla
,
D.
,
Yario
,
T.
and
Steitz
,
J.A.
(
2010
)
Down-regulation of a host microRNA by a Herpesvirus saimiri noncoding RNA
.
Science
328
,
1563
1566
doi:
29
Mattick
,
J.S.
(
2011
)
The central role of RNA in human development and cognition
.
FEBS Lett.
585
,
1600
1616
doi:
30
Nagano
,
T.
and
Fraser
,
P
. (
2011
)
No-nonsense functions for long noncoding RNAs
.
Cell
145
,
178
181
doi:
31
Cesana
,
M.
,
Cacchiarelli
,
D.
,
Legnini
,
I.
,
Santini
,
T.
,
Sthandier
,
O.
,
Chinappi
,
M.
et al.  (
2011
)
A long noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA
.
Cell
147
,
358
369
doi:
32
Ponting
,
C.P.
,
Oliver
,
P.L.
and
Reik
,
W
. (
2009
)
Evolution and functions of long noncoding RNAs
.
Cell
136
,
629
641
doi:
33
Johnsson
,
P.
,
Ackley
,
A.
,
Vidarsdottir
,
L.
,
Lui
,
W.-O.
,
Corcoran
,
M.
,
Grandér
,
D.
et al.  (
2013
)
A pseudogene long-noncoding-RNA network regulates PTEN transcription and translation in human cells
.
Nat. Struct. Mol. Biol.
20
,
440
446
doi:
34
Wang
,
K.
,
Long
,
B.
,
Zhou
,
L.Y.
,
Liu
,
F.
,
Zhou
,
Q.Y.
,
Liu
,
C.Y.
et al.  (
2014
)
CARL lncRNA inhibits anoxia-induced mitochondrial fission and apoptosis in cardiomyocytes by impairing miR-539-dependent PHB2 downregulation
.
Nat. Commun.
5
,
3596
doi:
35
Bank
,
A
. (
2006
)
Regulation of human fetal hemoglobin: new players, new complexities
.
Blood
107
,
435
443
doi:
36
White
,
J.G.
,
Nichols
,
W.L.
and
Steensma
,
D.P.
(
2007
)
Platelet pathology in sex-linked GATA-1 dyserythropoietic macrothrombocytopenia I ultrastructure
.
Platelets
18
,
273
283
doi:
37
Millikan
,
P.D.
,
Balamohan
,
S.
,
Raskind
,
W.
and
Kacena
,
M.
(
2011
)
Inherited thrombocytopenia due to GATA-1 mutations
.
Semin. Thromb. Hemost.
37
,
682
689
doi:
38
Singleton
,
B.K.
,
Roxby
,
D.J.
,
Stirling
,
J.W.
,
Spring
,
F.A.
,
Wilson
,
C.
,
Poole
,
J.
et al.  (
2013
)
A novel GATA1 mutation (Stop414Arg) in a family with the rare X-linked blood group Lu(a-b-) phenotype and mild macrothrombocytic thrombocytopenia
.
Br. J. Haematol.
161
,
139
142
doi:

Supplementary data