Role of microRNA-21 in the formation of insulin-producing cells from pancreatic progenitor cells

Biochimica et Biophysica Acta 1859 (2016) 280–293

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Role of microRNA-21 in the formation of insulin-producing cells from pancreatic progenitor cells Chunyu Bai 1, Xiangchen Li 1, Yuhua Gao 1, Kunfu Wang, Yanan Fan, Shuang Zhang, Yuehui Ma ⁎, Weijun Guan ⁎ Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China

a r t i c l e

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Article history: Received 27 August 2015 Received in revised form 17 November 2015 Accepted 2 December 2015 Available online 3 December 2015 Keywords: Pancreatic progenitor cells Insulin-producing cells miR-21 Insulin secretion

a b s t r a c t MicroRNAs (miRNAs) regulate insulin secretion, pancreas development, and beta cell differentiation. In this study, to screen for miRNAs and their targets that function during insulin-producing cells (IPCs) formation, we examined the messenger RNA and microRNA expression profiles of pancreatic progenitor cells (PPCs) and IPCs using microarray and deep sequencing approaches, respectively. Combining our data with that from previous reports, we found that miR-21 and its targets play an important role in the formation of IPCs. However, the function of miR-21 in the formation of IPCs from PPCs is poorly understood. Therefore, we over-expressed or inhibited miR-21 and expressed small interfering RNAs of miR-21 targets in PPCs to investigate their functions in IPCs formation. We found that miR21 acts as a bidirectional switch in the formation of IPCs by regulating the expression of target and downstream genes (SOX6, RPBJ and HES1). Small interfering RNAs were used to knock down these genes in PPCs to investigate their effects on IPCs formation. Single expression of si-RBPJ, si-SOX6 and si-HES1 in PPCs showed that si-RBPJ was an inhibitor, and that si-SOX6 and si-HES1 were promoters of IPCs formation, although si-HES1 induced formation of IPCs at higher rates than si-SOX6. These results suggest that endogenous miRNAs involved in the formation of IPCs from PPCs should be considered in the development of an effective cell transplant therapy for diabetes. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Pancreatic beta cells are responsible for the regulation of serum glucose levels through secretion of the hormone insulin. Type 1 diabetes results from autoimmune destruction of beta cells in the pancreatic islets, whereas the more common type 2 diabetes results from peripheral tissue insulin resistance and beta cell dysfunction. While replacement of pancreatic beta cells would be the ideal therapy for type 1 diabetes [1], the generation of insulin-secreting cells from stem cells or other cells is an attractive alternative [2,3]. In pancreatic islets, a small subset of cells express Nestin, and these cells are thought to represent precursors of differentiated pancreatic endocrine cells [4]. These Nestin-positive cells, called pancreatic progenitor cells (PPCs), may play an important role in the growth and maintenance of islets [5–7]. MicroRNAs (miRNAs) are non-coding small RNAs (19–25 nt) that regulate gene expression through post-transcriptional interference with specific messenger RNAs (mRNAs). A role for miRNAs in the control of differentiated beta cell function has been demonstrated by the generation of a mouse model with beta-cell-specific ablation of Dicer1 [8]. Disruption of the gene encoding this enzyme using a rat insulin promoter 2 (RIP)–Cre transgene led to

⁎ Corresponding authors at: No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China. E-mail addresses: [email protected] (Y. Ma), [email protected] (W. Guan). 1 These authors contributed equally to this work.

http://dx.doi.org/10.1016/j.bbagrm.2015.12.001 1874-9399/© 2015 Elsevier B.V. All rights reserved.

altered islet morphology, reduced beta cell number, and impaired glucose-induced insulin secretion [9]. A number of miRNAs are reported to be important regulators of beta cell differentiation and function, including miR-375 [10], miR-26, miR-24, miR-148 [11], miR-200, miR30d, miR-124a [12], miR-204 [13], miR-223, miR-21 [14], let-7 [15,16] and miR-9, miR-15a, miR-16, miR-146a, miR-29a, and miR-34a [17]. miR-21 is a transcriptional regulator of beta cell function, and its expression is up-regulated by exposure to proinflammatory cytokines or in prediabetic (non-obese diabetic) mice [18]. Furthermore, miR-21 is critical in the regulation of beta cell apoptosis in type 1 diabetes [19]. Expression of miR-21 is induced by members of the nuclear factor κB (NFκB) family, c-Rel, and p65 [20], and this increase in miR-21 levels results in the suppression of beta cell death by reducing the level of the tumor suppressor protein, programmed cell death protein 4 (Pdcd4) [21,22]. miR-21 also plays an important role in hepatocytes, where its expression is induced by NF-κB, leading to down-regulation of the expression of phosphatase and tensin homolog (PTEN), a protein that inhibits Akt activation. High-fat diets result in the up-regulation of miR-21 expression in rats, and liver biopsies of obese human patients also show an increase in miR-21 expression and a decrease in PTEN expression in comparison with normal controls [20]. However, the function of miR-21 in pancreatic beta cell differentiation has not been fully investigated. In the current study we screened for miRNAs and their targets that function in IPCs formation by examining mRNA and miRNA expression profiles in PPCs and insulin-producing cells (IPCs) using microarray and deep sequencing approaches. Combining our data with that from previous reports [23–26],

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we found that miR-21 and its targets play an important role in the formation of IPCs. To elucidate the mechanism of transcriptional regulation during the formation of insulin-producing cells and to better understand the role of miR-21 in the differentiation of IPCs from PPCs, we over-expressed or inhibited miR-21 expression and knocked down its target genes in vitro. 2. Materials and methods 2.1. Animals The chicken is a classic model for the study of vertebrate developmental biology that has been used for many decades [27] and was an appropriate model to use in this study. Chick embryos were provided by the chicken breeding farm of the Chinese Academy of Agricultural Sciences, Beijing, China. Animal experiments were performed in accordance with the guidelines established by the Institutional Animal Care and Use Committee of the Chinese Academy of Agricultural Sciences, and all experimental protocols were approved by the Experimentation Committee of the Chinese Academy of Agricultural Sciences. 2.2. Isolation and purification of pancreatic progenitor cells from chick embryos Nestin-positive PPCs were isolated from 15-day-old chick embryonic pancreas by the collagenase digestion method [28] and cultured in advanced RPMI 1640 medium (11.1 mM glucose) supplemented with 10% fetal bovine serum (FBS), 1 mM sodium pyruvate, 20 ng/mL basic fibroblast growth factor, 20 ng/mL epidermal growth factor, and 71.5 mM βmercaptoethanol. The fraction containing the majority of Nestinpositive stem cells was obtained by flow cytometry purification. Immunofluorescence was used to analyze progenitor cells for specific markers, including Nestin, pancreas/duodenum homeobox protein 1 (PDX1), and Hairy Enhancer of Split-1 (HES1). 2.3. Differentiation of pancreatic progenitor cells into insulin-producing cells For differentiation of PPCs into endocrine pancreatic cells, clonal cell masses were picked, plated on 24-well cell culture plates, and cultured for 7 days in Dulbecco's modified Eagle's medium (DMEM)/F12 containing glucose (11.1 mM) and a cocktail of several growth factors, including 10 mM nicotinamide, 100 pM hepatocyte growth factor, 10 nM exendin-4, and 2 nM activin A. To determine whether insulin release from differentiated cells was glucose dependent, we used two glucose concentrations (5.5 mM and 17.5 mM) [29] and measured insulin release using an insulin enzyme-linked immunoabsorbent assay (ELISA) kit. IPCs were washed with Krebs buffer and then preincubated in low-glucose (5.5 mM) Krebs buffer for 2 h to remove residual insulin. IPCs were then washed three times in Krebs buffer and incubated in low-glucose Krebs buffer for 30 min, and the supernatant was collected. Then, IPCs were washed three times in Krebs buffer and incubated in high-glucose (17.5 mM) Krebs buffer for 30 min, and the supernatant was collected. This sequence was repeated three additional times. 2.4. Microarray analysis Total RNA samples from PPCs, IPCs, and PPCs transfected with miR-21 or anti-miR-21 from three independent experiments were hybridized to Chicken Whole Genome Expression chips (Agilent Technologies, California, US, 4 × 44 K, Design ID 026441) according to the manufacturer's instructions. The RNA labeling, hybridization, washing, and chip reading were performed by Shanghai Biotechnology Corporation (Shanghai, China). Briefly, total RNA was amplified and labeled using a Low Input Quick Amp Labeling Kit (Agilent technologies). Labeled complementary RNA (cRNA) was purified using an RNeasy minikit (Qiagen, Germany)

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and hybridized to chicken chips using a Gene Expression Hybridization Kit (Agilent technologies). The hybridized chips were washed using a Gene Expression Wash Buffer Kit (Agilent Technologies) and then scanned on an Agilent Microarray Scanner. Crude data were extracted using Feature Extraction software 11.5 (Agilent Technologies). The normalization of raw data per chip (to the 50th percentile) and per gene (to the median) was performed using the quantile algorithm of MAS 5.0 (Gene Spring GX software 11.5, Agilent Technologies). The SBC Analysis System (an online system for the analysis of microarrays, http://sas. ebioservice.com/portal/root/molnet_shbh/index.jsp, Shanghai Biotechnology Corporation, China) was used to select the differentially expressed genes [30]. Significance of differential expression was assessed with Student's t-test. Fold changes ≥2 and a p-value b0.05 were used as the thresholds. Gene Ontology (GO) clusters of biological processes were analyzed using the SBC Analysis System, and pathway analysis was performed based on the Kyoto Encyclopedia of Genes and Genomes database (KEGG). Heat maps were generated using Java TreeView software. Hierarchical clustering was carried out using Euclidian distance as the distance metric and average linkage between clusters. Microarray data and RNA sequence data have been deposited in the NCBI Gene Expression Omnibus database (accession number SRP034850). 2.5. Small RNA library construction, sequencing and data analysis The Illumina TruSeq Small RNA Sample Preparation protocol was used to prepare RNA. Briefly, total RNA samples from PPCs and IPCs were size fractionated using 15% polyacrylamide gel electrophoresis, and the 16–35 nt fraction was collected. The 5′ and 3′ RNA adaptors were ligated to the RNA, and RNAs of 64–99 nt were isolated by gel elution and ethanol precipitation. Polymerase chain reaction (PCR) products were purified and small RNA libraries were sequenced using the Illumina Genome Analyzer. Sequencing was carried out at the Shanghai Biotechnology Corporation. The raw data were processed using Illumina Genome Analyzer Pipeline software and then submitted for data filtration. Clean reads were obtained after filtering low-quality reads and trimming the adaptor sequences. All of the clean reads were initially searched against miRBase (version 21; http://www.mirbase.org/) to identify chicken miRNAs. Unmappable reads were annotated and classified by reference to noncoding RNAs in the Ensemble (ftp://ftp.ensembl.org/pub/release-69/ fasta/sus_scrofa/ncrna/), piRNA (http://pirnabank.ibab.ac.in/) and Rfam (version 10; http://rfam.sanger.ac.uk/) databases. The mappable sequences were used for further analysis. Meanwhile, many unannotated sequences that did not find a match in any of the above databases were analyzed by miRDeep (http://deepbase.sysu.edu.cn/miRDeep. php) to predict novel miRNA candidates. After all annotation steps, the sequencing libraries were used for size distribution and saturation analysis. All sequencing data have been deposited in the NCBI Sequence Read Archive database (accession number SRP034850). miRNA expression was compared between PPCs and IPCs to determine which miRNAs were differentially expressed. The expression levels of miRNAs within each library were normalized to get the expression in transcripts per million (Tpm) mapped reads. R package DEGseq software was used to identify differentially expressed miRNAs. P-values for differentially expressed miRNAs were calculated using the MA-plot-based random sampling model (MARS). P-values were adjusted using q-value [31]. q-Value b 0.01 and |log2(fold change)| N 1 were set as thresholds for significantly differential expression by default. Computational prediction of miRNA targets was performed in online databases miRDB (http://www.miRdb.org/), miRanda (http://www.miRanda-im.org/), miRwalk (http://www.ma.uni-heidelberg.de/apps/zmf/miRwalk/) and TargetScan (http://www.targetscan.org/). 2.6. GO and KEGG pathway analysis Data screening was carried out based on a gene expression fold change of ≥2 and statistical significance of p b 0.05. Biological themes

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Fig. 1. Expression of specific genes in PPCs and IPCs. A. Detection of PPC markers by immunofluorescence staining. Nestin staining was located in the cytoplasm, while PDX1 and HES1 staining were located in the nucleus. The Nestin, PDX1, and DAPI signals appear as a garland-like structure in merged images. PDX1, a transcription factor, is recognized as a marker of pancreatic precursors in pancreas development. HES1 plays an important role in maintaining the proliferation of pancreatic precursors. B. Purification of PPCs by FCM. C. Hierarchical cluster of genes differentially expressed between PPCs and IPCs (all differentially expressed genes are listed in Table S2). The expression profiles of specific genes are shown in the heat map as follows: up-regulated (red), down-regulated (green), and no change (black) (P b 0.05). D. Relative expression levels of genes involved in pancreas development during the formation of IPCs; data were normalized to normal PPCs (day 0). The x-axis shows days after induction. E. Immunofluorescence analysis of sections from differentiated clusters and PPCs stained for insulin (green) and glucagon-like peptide-1 (GLP1; red). Scale bar = 50 μm.

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Fig. 2. Genes differentially expressed between PPCs and IPCs. A. Volcano plot of significantly differentially expressed genes identified using a T-assay. Genome-wide expression profiles were compared between PPCs and IPCs, with three replicates for each cell type. A total of 7625 genes (Table S2) showed differences in expression at the level of 2-fold and more (P b 0.05), of which 4022 were up-regulated and the remainder were down-regulated. B. Enriched pathways and gene ontology terms of significantly differentially expressed genes. C. Fold change in gene expression levels between PPCs and IPCs for components of the Notch signaling pathway (P b 0.05). D. Relative expression levels of components of the Notch signaling pathway during formation of IPCs; data were normalized to normal PPCs (day 0). The x-axis shows days after induction.

of the differentially expressed genes were identified according to the biological processes of GO categories using the online tool of the Database for Annotation, Visualization, and Integrated Discovery (DAVID). KEGG pathway analysis was performed using the SAS online program (http://www.ebioservice.com/eng/index.asp) with a hit number ≥ 5 and an enrichment test p-value b 0.05. 2.7. Venn diagrams Venn Tools were used to demonstrate superposition relationships between target genes of miRNAs and microarray results. Venn diagrams were generated using software at http://bioinfogp.cnb.csic.es/tools/ venny/index.html.

2.9. Western blotting RBPJ, SOX6 and HES1 were detected by Western blot analysis following over-expression of miR-21 or siRNA. Cells were lysed using M-PER Protein Extraction Reagent (Pierce, USA) supplemented with a protease inhibitor (PMSF). Protein concentrations of the extracts were measured with the BCA assay (Pierce, USA) and equalized with extraction reagent. Equal amounts of extracts were loaded and subjected to SDS-PAGE, followed by transfer onto nitrocellulose membranes. Primary antibodies (RBPJ, 1:500; SOX6, 1:200; HES1, 1:500) and horseradish peroxidasecoupled secondary antibodies (1:2000) were purchased from Abcam, USA. Membranes were probed using ultra-enhanced chemiluminescence Western blotting detection reagents. GAPDH was used as an internal control. Protein abundance was analyzed using Image J tools.

2.8. Construction of lentiviral vectors To observe genetic conservation, sequence similarity of miR-21 in different species, including human (hsa), chicken (gga), mouse (mmu), pig (ssc) and cow (bta), was analyzed using DNAMAN tools. Precursor sequences for miRNAs, anti-miRNAs and small interfering RNA (siRNA) were synthesized and cloned into a lentiviral vector. The vector was then incorporated into a lentivirus in HEK-293T cells. Viral particles were used to infect PPCs. The lentivirus also expressed green fluorescent protein as a marker of infection efficiency. Western blot analysis was used to detect expression of the protein products of genes targeted by miRNAs and siRNAs.

2.10. Real-time PCR miRNA real time PCR: Real time PCR was performed to validate the expression of miRNAs. Twenty-eight miRNAs, which were identified by deep sequencing as differentially expressed and are known to be involved in pancreatic development, were chosen for validation. These were miR-223, miR-7, miR-222b-3p, miR-204, miR-146b-5p, miR-146b3p, miR-146a, miR-142-5p, miR-142-3p, miR-375, miR-33-5p, miR-30d, miR-21, miR-221, miR-19b, miR-33-3p, miR-125b, miR-23b, miR-200a, miR-217, miR-92, miR-24, miR-9-3p, miR-27b, miR-205b, miR-454-3p,

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Fig. 4. Microarray analysis of the expression of miR-21 targets in PPCs, IPCs, PPCs with miR-21 over-expression and PPCs with anti-miR-21. miR-21 targets were identified by comparing expression of genes in the above cell groups. Fifty-four direct targets were predicted and, among these, analysis of variance (P b 0.05) identified approximately 40 target genes that were differentially regulated between the various cell groups. Hierarchical clustering of these statistically significant targets further confirmed the distinct clustering of gene expression profiles according to different stages of PPC development, largely in agreement with the results of bioinformatic miRNA target prediction. SOX6 and RBPJ, two direct targets of miR-21, were identified (black arrows), while HES1 (black arrow), a downstream gene of RBPJ, was an indirect target of miR-21. RBPJ and HES1 negatively regulate IPCs formation.

miR-1552-5p and miR-1552-3p. Mature miRNAs were obtained from PPCs and IPCs using the miRcute miRNA Isolation Kit (Tiangen, Beijing, China), and then miRNAs were reverse-transcribed after being linked poly (A)-tailed using the miRcute miRNA cDNA kit (Tiangen, Beijing, China). qPCR of miRNAs was performed using the miRcute miRNA qPCR Detection Kit (SYBR Green, Tiangen, Beijing, China) and an ABI 7500 real time PCR system according to the manufacturer's instructions

under the following conditions: 94 °C for 1 min, 40 cycles at 94 °C for 20 s, 60 °C for 30 s and 70 °C for 10 s. U6 small nuclear RNA was used for normalization. Each experiment was performed in duplicate in 96well plates and repeated three times. Relative amounts of miRNA were calculated by the comparative threshold cycle (CT) method as 2−ΔCT, where ΔCT = CTmicroRNA / CTgeometric mean of U6. Changes in gene expression were assessed by the t-test, and p b 0.05 was considered significant.

Fig. 3. miRNAs play critical roles in the formation of IPCs. A. Scatterplot comparing the transcripts per million (TMP) for each mi-RNA gene in PPCs and IPCs (genes are listed in Table S3). The target genes of these differentially expressed miRNAs were predicted using TargetScan tools and analyzed using Venny tools. B. The target genes of differentially expressed miRNAs and the genes differentially expressed between PPCs and IPCs are compared in a Venn diagram. A total of 727 genes were found in the intersection and are listed in Table S4. C. Heat map showing changes in expression of the intersecting 727 genes between PPCs and IPCs. D. Enriched pathways and gene ontology terms of intersecting genes. Pathway analysis demonstrated that miRNAs could regulate the signaling pathways that control the formation of IPCs from PPCs, such as Notch, PDGF, Wnt, and cadherin signaling pathways. E. Expression levels of miRNAs in PPCs and IPCs determined by real-time PCR. Levels are expressed as a percentage of the U6 expression level, which was measured in parallel in the same samples. Results are means ± SEM of three independent experiments.

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mRNA real time PCR: Total RNA was extracted from PPCs and IPCs using Trizol reagent (Invitrogen, USA). RNA was reverse transcribed, followed by 30 PCR cycles using the RNA PCR kit ver 3.0 (Takara, China). Sequence-specific reverse transcription PCR primers for mRNAs and the endogenous control (GAPDH) were synthesized by Sangon Biotech (Shanghai, China). Real-time PCR was performed in a 20 μl volume containing 10 μl SYBR premix Ex Taq buffer (Takara), 0.4 μl ROX Reference Dye, 0.8 μM of each forward and reverse primer, 1 μl template cDNA, and 7 μl ddH2O. The cycling conditions consisted of an initial 10 s at 95 °C, followed by 40 cycles of two-temperature cycling: 5 s at 95 °C (for denaturation) and 34 s at 60 °C (for annealing and polymerization). Each experiment was performed in duplicate in 96well plates and repeated three times. Gene expression was detected on an ABI 7500 real-time PCR system (USA). The expression level was calculated using the 2− ΔΔCt method to compare relative expression. All primers used in this study are listed in Supplementary Table S1.

The methods used for immunohistochemistry (IHC) were as previously described [34]. Pancreas samples were dissected from chicks and fixed in a 4% PFA/PBS solution overnight and embedded in paraffin. Five-micrometer longitudinal sections cut from paraffin blocks were rehydrated with xylene followed by decreasing concentrations of ethanol, microwaved in 0.01 M sodium citrate (pH 6.0) for 20 min, and permeabilized with 1% Triton X-100 in PBS prior to incubation with primary antibody at 4 °C overnight. Primary antibodies against Nestin, SOX6, RBPJ, and HES1 were used at dilutions of 1:100, 1:50, 1:100, and 1:100, respectively. Sections were then incubated with GARB (1:400) and GAMB (1:100) for 3 h at room temperature and then with FITC-conjugated streptavidin (1:50) for 3 h at room temperature. After counterstaining with 4′,6-diamidino-2-phenylindole (DAPI), images were obtained by confocal microscopy.

2.11. Flow cytometry

2.15. Transplantation studies

To determine levels of Nestin and proinsulin expression, we analyzed cells with a Beckman Coulter FC500 flow cytometer. Briefly, cells were detached with 0.125% trypsin, centrifuged, separated into 500 μL aliquots, and labeled with custom-made FITC-conjugated polyclonal antibodies against chicken Nestin or proinsulin (MBL International, Japan) as per the manufacturer's instructions [32]. Flow cytometric data were analyzed using CXP software (Beckman Coulter, USA). Mean fluorescence intensity was determined after subtraction of a respective negative control.

The Institutional Animal Care and Use Committee of the Chinese Academy of Agricultural Sciences approved all animal procedures. Transplantation studies were performed as previously described [35]. Immunodeficient SCID mice, aged 4 weeks, were obtained from Beijing HFK Bioscience. PPCs or IPCs clusters (3 × 106 cells per animal) were loaded into a catheter for cell delivery below the kidney capsule. After 2 weeks, mice were injected intraperitoneally with 2 g of (+)-D-glucose/kg body weight and serum was collected after 0 and 30 min. Serum chicken insulin levels were quantified using an ultrasensitive insulin ELISA. Kidneys containing cell grafts were dissected from mice, fixed in 4% PFA overnight, embedded in paraffin, sectioned, and analyzed by IHC.

2.12. Luciferase reporter assay Firefly luciferase reporter genes were constructed using the pCS2-Luc vector and the 3ʹ untranslated regions (UTRs) of chicken SOX6 and RBPJ. Primers for PCR amplification of the 3ʹ UTRs were as follows: SOX6, 5ʹCGTGAATTCGGGGCTGGGGGTGG-3ʹ (forward) and 5ʹ-TCACTCGAGCTGT TCTCTCTGCCCCCC-3ʹ (reverse); RBPJ, 5ʹ-CGTGAATTCGAGTTAGTATAT TGCACATGAAAGATCT-3ʹ (forward) and 5ʹ-TCACTCGAGTTGGCTTGGCTT ACAGCAT-3ʹ (reverse). The underlined sequences indicate introduced EcoRI and XhoI sites. Constructs containing a mutated SOX6 (SOX6MUT) or RBPJ (RBPJ-MUT) 3ʹ UTR were used as negative controls. Mutations at positions 3–5 of the miR-21 seed sequence were introduced using the QuikChange mutagenesis kit (Stratagene, USA). 293T cells were cultured in H-DMEM supplemented with 10% FBS. The cells were seeded into 24-well plates (2 × 104 cells/well). After 24 h of culture, the cells were transfected using Lipofectamine 3000 (Invitrogen, USA) with a mixture containing 1 mg/mL firefly luciferase reporter plasmid, 20 nM miR-21 or control precursor, and 20 ng/mL of a plasmid encoding Renilla reniformis luciferase (pRL-TK, Promega, USA). Cells transfected without the precursor served as controls for normalization. Luciferase activity was measured 48 h post-transfection using a dual-luciferase assay system (Promega). All transfections were repeated independently at least three times. 2.13. In situ hybridization Pancreatic tissue from 15-day-old chick embryos was fixed in 4% paraformaldehyde (PFA) in phosphate-buffered saline (PBS) overnight at 4 °C and then treated with proteinase K for 30 min. In situ hybridization was performed as previously described [33]. Locked nucleic acid (LNA) probes were designed and synthesized by Sangon Biotech. The sequence of the LNA probe complementary to mature miR-21 was AUCG AAUAGUCUGACUACAACU. LNA probes were labeled with digoxigenin using the DIG Oligonucleotide 3ʹ-end Labeling Kit (Roche, USA) and purified using Sephadex G-25 MicroSpin columns (Amersham, Sweden). Images of pancreatic tissue were taken using a confocal microscope with a digital acquisition system (Nikon T-2000, Japan).

2.14. Immunohistochemistry

2.16. Statistical analysis Data are expressed as the mean ± SD. Differences between experimental groups were assessed using the two-tailed t-test. Statistical significance was defined as ∗P b 0.05 and ∗∗P b 0.01. 3. Results 3.1. Differentiation of PPCs into IPCs PPCs were isolated from embryonic day 15 chicks and formed colonies 4 days after seeding in primary culture. Many erythrocytes were mixed with the progenitor cells, but the progenitor cell population was purified from hemocytes and other cells using flow cytometry. PDX1, a transcription factor, is recognized as a marker of pancreatic precursors in pancreas development. We examined cells for Nestin, PDX1, and HES1 expression using immunofluorescence, and cells in clusters were positive for all three proteins (Fig. 1A and B). We used a cocktail of growth factors to induce the formation of IPCs from progenitor cells, observed all changes in differentially expressed genes (Fig. 1C), and then used real time PCR to verify the transcription of islet hormone genes, including PDX1, ISL1, INS, SOX6, HES1 and MYT1 (Fig. 1D). Expression of insulin and glucagon-like peptide-1(GLP1) in islet-like structures was detected by immunofluorescence staining (Fig. 1E). Insulin release is an important characteristic of beta cells, and immunofluorescence staining demonstrated that the clusters derived from progenitor cells could release insulin at glucose concentrations of 5.5 mM and 17.5 mM, while undifferentiated progenitor cells did not release insulin. 3.2. Differentially expressed genes in PPCs and IPCs The relative expression levels of mRNAs in PPCs and IPCs were evaluated using microarray analysis. Genome-wide expression profiles were compared between PPCs and IPCs, using three replicates for each cell type (all differentially expressed genes are listed in Table S2). A total of

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Fig. 5. Identification of miR-21 targets. A and B. Predicted base-pairing of mature miRNA sequences and 3′-untranslated regions (UTRs) of target genes. Seed sequences are shown in red. Complementary sites of miR-21 and the 3′-UTRs of RBPJ and SOX6 are connected by vertical lines. The mutant sequences (RBPJ-MUT and SOX6-MUT) are identical to the wild-type constructs (RBPJ-WT and SOX6-WT) except for four point mutations that disrupt base-pairing at the 5′ end of miR-21 (underlined). Mutating the miR-21 target site in the 3′-UTR of RBPJ or SOX6 abolished inhibition of luciferase activity by endogenous miR-21 in 293T cells (paired two-tailed t test). C. Co-expression of miR-21 and its targets in pancreatic tissue. Expression of the putative target genes was negatively regulated by miR-21, but Nestin and HES1, markers of PPCs, were co-expressed in tissue with miR-21 (scale bar =100 μm).

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7625 genes showed differences in expression at the level of 2-fold and more (P b 0.05), of which 4022 were up-regulated and the remainder were down-regulated (Fig. 2A). Significantly differentially expressed genes were selected and analyzed for their molecular function or for their roles in biological processes or as cellular components. Some signaling pathways were identified through Gene Ontology analyses, including Notch, platelet-derived growth factor (PDGF), transforming growth factor β, and Wnt signaling pathways (Fig. 2B). The Notch signaling pathway plays a critical role in many developmental processes, influencing differentiation, proliferation, and apoptosis [36,37]. Endocrine differentiation in the early embryonic pancreas is regulated by Notch signaling. Activated Notch signaling maintains PPCs in an undifferentiated state, whereas suppression of Notch signaling leads to endocrine cell differentiation [38]. The Notch signaling pathway promotes self-renewal of PPCs and inhibits terminal differentiation of these progenitor cells. The PDGF signaling pathway promotes proliferation, survival, and migration of diverse cell types [39] (Fig. 2C and D). 3.3. Differential expression profile of microRNAs in PPCs and IPCs Many miRNAs regulate insulin secretion from beta cells, such as miR-375, miR-124a, and let-7 [15,16]. miRNAs in animals exhibit tissue-specific or developmental-stage-specific expression, indicating that they play important roles in many biological processes. However, no report has described the functions of miRNAs in the formation of IPCs from PPCs. Therefore, we applied a deep sequencing approach to reveal the differential expression profile of miRNAs between PPCs and IPCs, which is shown in Table S3 and Fig. 3. One hundred and twentynine miRNAs exhibited a significant change in expression level after the formation of IPCs (p b 0.05, Fig. 3A, Table S3). The target genes of these differentially expressed miRNAs were predicted using TargetScan tools (http://www.targetscan.org/) and analyzed using Venn tools (http://bioinfogp.cnb.csic.es/tools/venny/index.html). The target genes of differentially expressed miRNAs and the genes differentially expressed between PPCs and IPCs were compared in a Venn diagram. This analysis identified 727 genes in the intersection of the Venn diagram (Fig. 3B and C, listed in Table S4), and these genes were then investigated by Gene Ontology and pathway analysis (Fig. 3D). Pathway analysis demonstrated that miRNAs could regulate the signaling pathways that control the formation of IPCs from PPCs, such as Notch, PDGF, Wnt, and cadherin signaling pathways. miRNAs that were upor down-regulated during the differentiation of beta cells could regulate crucial components of these signaling pathways to promote the formation of IPCs. Twenty-eight miRNAs that were differentially expressed and involved in pancreatic development were chosen for validation using real-time PCR. These validation results were largely in agreement with the deep sequencing data (Fig. 3E). 3.4. Identification of miR-21 targets miR-21 target genes involved in the formation of IPCs were predicted and analyzed using TargetScan and miRDB (predicted target genes of miR-21 are listed in Table S5). Potential target genes of miR-21 during the formation of IPCs were further identified using microarrays after transfection of PPCs with miR-21 or anti-miR-21. Fifty-four direct target genes of miR-21 that are involved in IPCs formation were found and are

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listed in Table S6. Analysis of variance (P b 0.05) identified approximately 40 targets that are differentially regulated between the various IPC/ PPC groups (Fig. 4). Hierarchical clustering of these statistically significant targets further confirmed the distinct clustering of gene expression profiles according to different states of PPCs, which was largely in agreement with the miRNA target prediction. In fact, approximately 40 targets were significantly decreased during IPCs formation or in PPCs over-expressing miR-21, whereas the expression of other targets was either not changed or increased. SOX6 and RBPJ were listed among miR-21 targets, while HES1, a downstream gene of RBPJ, was an indirect target of miR-2. HES1 plays an important role in maintaining the undifferentiated state of PPCs. RBPJ and HES1 are negatively regulated in the absence of Notch signaling (including Notch 1, Notch 2, Notch 3, and Notch 4); RBPJ inhibits the expression of HES1 and the formation of IPCs. To determine whether the predicted miR-21 target sites in the 3ʹ-UTRs of SOX6 and RBPJ mRNAs were responsible for the silencing of gene expression by miR-21, we cloned the putative 3ʹ-UTR target sites downstream of a luciferase reporter gene and co-transfected this vector into 293T cells with either pre-miR-21 or a control precursor. In cells transfected with pre-miR-21 and pRL-SOX6-WT or pRL-RBPJ-WT, luciferase activity was decreased relative to that in cells co-transfected with control precursor and pRL-SOX6-MUT or pRL-RBPJ-MUT (Fig. 5A and B). In addition, in situ hybridization and IHC were used to measure the expression of miR-21 and its targets in 15-day-old chick embryonic pancreatic tissue sections. The results demonstrated that miR-21 and its targets were co-expressed in pancreatic islet structures and that target gene expression was negatively regulated by miR-21 (Fig. 5C). These results demonstrate that miR-21 can directly target seed sequences in the 3ʹ-UTRs of target genes to repress gene expression. 3.5. miR-21 inhibits the differentiation of PPCs into IPCs PPCs were transfected with miR-21 or anti-miR-21 using a lentivirus expression system. The sequence similarity of miR-21 from different species was also investigated (Fig. 6A). After differentiation, cells adopted a rounded or ovoid morphology and assembled into islet-like structures. Insulin secretion by beta cells was significantly elevated after transfection of miR-21 or anti-miR-21. The targets of miR-21 in the formation of IPCs were predicted and analyzed using TargetScan, miRDB and microarrays. Based on this analysis, in combination with data from a previous study [23–26], SOX6 and RBPJ were selected as candidate targets. To investigate the functions of miR-21 in PPCs, we over-expressed miR-21 or anti-miR-21 in these cells. The expression of proteins encoded by miR-21 target genes, including SOX6 and RBPJ, was decreased in miR21-transfected PPCs compared with control cells (Fig. 6B), and increased in anti-miR-21-transfected PPCs (Fig. 6C). Flow cytometry data showed that the efficiency of beta cell differentiation from progenitor cells was higher with a combination of anti-miR-21 and growth factors than with growth factors alone or with over-expression of miR-21 (Fig. 7A). 3.6. Several potential miR-21 targets are involved in the formation of IPCs from PPCs Several transcription factor genes from the pool of predicted miR-21 targets were selected for inhibition by siRNA (Fig. 6D). SOX6 plays an important role in pancreas development by suppressing PDX1 expression in

Fig. 6. Roles of miR-21 and its putative targets in the formation of IPCs from PPCs. A. Sequence similarity of miR-21 in different species, including human (hsa), chicken (gga), mouse (mmu), pig (ssc) and cow (bta). The results showed that the miR-21 sequence is highly conserved. B. Western blot analysis of proteins expressed by gene targets of miR-21 following miRNA over-expression in PPCs. C. Western blot analysis of proteins expressed by gene targets of miR-21 following anti-miR-21 over-expression in PPCs. Protein abundance was analyzed using ImageJ tools. Over-expression of anti-mir-21 or miR-21 for 72 h inhibited endogenous expression of RBPJ, SOX6 and HES1 (n = 5; paired two-tailed t test). GAPDH was used as an endogenous control. Le-eGFP, lentivirus expressing enhanced green fluorescent protein (vector control); Le-miR-21, lentivirus expressing miR-21; Le-anti-miR-21, lentivirus expressing anti-miR-21. D. Sequences of siRNAs for putative target genes. Sense (passenger) and antisense (guide) strands were included; the antisense strand directs the RNA-induced silencing complex (RISC) to mRNAs with a complementary sequence, while the sense strand is degraded. In the case of perfect complementarity, RISC cleaves the mRNA. E. Western blot analysis of proteins expressed by siRNA-targeted genes in PPCs. Protein abundance was analyzed using ImageJ tools. Over-expression of siRNA for 72 h inhibited endogenous expression of RBPJ, SOX6, and HES1 (n = 5; paired two-tailed t test). GAPDH was used as an endogenous control. Le-eGFP, lentivirus expressing enhanced green fluorescent protein (vector control); LesiRNA, lentivirus expressing siRNA.

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Fig. 7. Insulin expression and glucose-induced insulin secretion in IPCs derived with different combinations of inducers. A. Analyses of proinsulin expression using flow cytometry. Proinsulin is the prohormone precursor to insulin made in the beta cells of pancreatic islets. The percentage of cells expressing insulin was highest in group 11. B. Glucose-induced insulin secretion in IPCs derived with different combinations of inducers. The box plots show that insulin secretion increased when the glucose concentration increased in all groups except group 1 (n = 30; paired two-tailed t test). Group 1 (G1), PPCs; group 2 (G2), PPCs induced with a cocktail of growth factors; group 3 (G3), PPCs over-expressing miR-21 and induced with growth factors; group 4 (G4), PPCs over-expressing si-RBPJ and induced with growth factors; group 5 (G5), PPCs over-expressing si-SOX6 and induced with growth factors; group 6 (G6), PPCs over-expressing si-HES1 and induced with growth factors; group 7 (G7), PPCs over-expressing an inhibitor of miR-21 (anti-miR-21) and induced with growth factors; group 8 (G8), PPCs over-expressing si-RBPJ and si-SOX6 and induced with growth factors; group 9 (G9), PPCs over-expressing miR-21 and si-HES1 and induced with growth factors; group 10 (G10), PPCs over-expressing anti-miR-21 and si-SOX6 and induced with growth factors; group 11 (G11), PPCs over-expressing anti-miR-21 and si-HES1 and induced with growth factors; group 12 (G12), PPCs over-expressing si-SOX6 and si-HES1 and induced with growth factors.

beta cells and attenuating glucose-stimulated insulin secretion [23]. SOX6 is a target of miR-21, and was down-regulated by over-expression of miR21 in PPCs. RBPJ, also known as CBF1, is the human homolog of the Drosophila gene Suppressor of Hairless. Its promoter region is classically used to activate or transactivate Notch signaling downstream factors [40,41]. HES1 also plays an important role in the Notch signaling pathway. In the absence of Notch signaling, RBPJ inhibits the expression of HES1. After Notch signals have been processed within the cell, however, the plasma membrane releases the intracellular domain of Notch, which moves to the nucleus, where it associates with RBPJ. This binding causes a conformational change that allows co-repressors to dissociate and coactivators to bind. The new activating complex then promotes HES1 expression [24,25]. The mRNA levels of Notch pathway members were not significantly changed during the formation of IPCs from PPCs; therefore, RBPJ acted as a repressor to regulate the expression of HES1. Small interfering RNAs against RBPJ, SOX6, and HES1 (si-RBPJ, si-Sox6, and si-HES1, respectively) were used to study the functions of these potential miR-21 targets in the formation of IPCs (Fig. 6E). miR-21, anti-miR-21, si-RBPJ, si-Sox6 and si-HES1 were expressed in PPCs singly or in combinations and the effect on the formation of IPCs is shown in Fig. 7A. Single

expression of si-RBPJ, si-SOX6 and si-HES1 in PPCs showed that si-RBPJ was an inhibitor, and that si-SOX6 and si-HES1 were promoters of IPCs formation, although si-HES1 induced formation of IPCs at higher rates than si-SOX6. These results demonstrated that miR-21 acts as a bidirectional switch in the formation of IPCs through regulating the expression of its target genes. Combinations of anti-miR-21 and si-HES1 or si-Sox6 induced formation of IPCs at higher rates than other combinations of inducers. Analysis of glucose-induced insulin secretion demonstrated that IPCs derived with different combinations of inducers could release insulin at glucose concentrations of 5.5 mM and 17.5 mM, while undifferentiated progenitor cells did not release insulin (Fig. 7B). 3.7. Cell transplantation To assess the in vivo function of IPCs successfully differentiated from progenitor cells, we transplanted beta cells under the kidney capsule of immunodeficient SCID mice that had been induced to become hyperglycemic by intraperitoneal injection of (+)-D-glucose. After a brief surgical recovery period (2 weeks), mice transplanted with beta cells were injected with glucose, and serum was collected 30 min later.

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Fig. 8. IPCs transplanted below the renal capsule of immunodeficient SCID mice became functional after 2 weeks. A. Hematoxylin and eosin staining of day 14 graft. k, kidney; g, graft. B, C, and D. Representative immunofluorescence images of kidneys 2 weeks post-transplantation, stained for proinsulin to confirm the presence of engrafted IPCs (scale bar = 100 μm); B, DAPI; C, proinsulin/FITC; D, merged. E. ELISA detection of chicken insulin in serum of individual mice transplanted with 3 × 106 IPCs or PPCs. Measurements were taken before (blue bars) and 30 min after (red bars) a glucose injection at 2 weeks post-transplantation.

Measurement of chicken insulin in the serum by ELISA revealed insulin secreted into the host bloodstream. Conversely, when progenitor cells were transplanted into mice, no insulin was detected after 2 weeks (Fig. 8E). Two weeks post-transplantation, animals were sacrificed and the engrafted kidneys were removed for histological analysis. IHC showed the presence of beta cells adjacent to the mouse kidney (Fig. 8A–D). Analysis of proinsulin staining revealed that the IPCs remained monohormonal after transplantation (Fig. 8E). 4. Discussion The Notch signaling pathway plays critical roles in many developmental processes, influencing differentiation, proliferation and apoptosis [36,37]. Endocrine differentiation in the early embryonic pancreas is regulated by Notch signaling. Activated Notch signaling maintains pancreatic progenitor cells in an undifferentiated state, whereas suppression of Notch leads to endocrine cell differentiation [38]. Other signaling pathways also participate in pancreatic biological function. Among them, activation of PDGF signaling stimulates DNA synthesis in cultured islets [42,43] and regulates age-dependent proliferation of beta cells [44]. The expression of PDGF subunit A was up-regulated after differentiation of beta cells, indicating that PDGF signaling may regulate the proliferation of beta cells after differentiation. Wnt family members are secreted proteins that signal through the frizzled superfamily of G protein-coupled receptors and are involved in many physiological and pathophysiological activities. However, Notch signaling mediates cell fate choices in all vertebrates largely by specifying the transcriptional output of one cell in response to a neighboring cell. The DNA-binding protein RBPJ is the principal effector of this pathway in mammals and, together with the transcription factor activity of Notch, RBPJ regulates the expression of target genes. HES1 also plays an important role in the Notch signaling pathway. After Notch signals have been processed within the cell (including Notch 1, Notch 2, Notch 3, and Notch 4), the plasma membrane releases the intracellular domain of Notch, which moves to the nucleus, where it associates with RBPJ. This

binding causes a conformational change that allows co-repressors to dissociate and co-activators to bind, and then the new activating complex promotes HES1 expression. In the absence of Notch signaling, RBPJ inhibits the expression of HES1 [24,25]. HES1 is a potent repressor of the gene encoding the pro-endocrine factor, NGN3. Knockout studies have shown that NGN3, a basic helix-loop-helix transcription factor, is required for the development of all endocrine cell lineages of the pancreas. Therefore, the Notch pathway is typically associated with proliferation and maintenance of an undifferentiated state. Online tools indicated that RBPJ was a potential target of miR-21, suggesting that miR-21 could play a critical role in the formation of IPCs by targeting RBPJ. Our work demonstrated that over-expression of miR-21 reduced the expression of RBPJ in PPCs but increased the expression of HES1. Furthermore, an inhibitor of miR-21 reduced the expression of miR-21 in PPCs to promote the formation of IPCs. SOX6, another potential miR-21 target, influenced IPCs formation and insulin secretion. Differentiation of PPCs involves a cascade of inhibition and activation of specific transcription factors such as PDX1. PDX1 expression is maintained in PPCs during pancreas development. In mature beta cells, PDX1 transactivates the insulin gene and other genes involved in glucose sensing and metabolism, such as GLUT2 and the glucokinase gene [45]. The homeodomain of PDX1 binds target sequences called A-boxes (A/T-rich elements) within the insulin gene promoter. The terminal activation domain of PDX1 recruits the co-activator p300 and stimulates insulin gene expression synergistically with E12 and E47, which bind to E-boxes that are also located in the insulin gene promoter [46–48]. SOX 6 suppresses PDX1-mediated stimulation of the insulin gene promoter through a direct protein–protein interaction in beta cells [23]. Therefore, SOX6 directly binds PDX1 and negatively regulates IPCs differentiation and glucose-stimulated insulin secretion. In this study, a luciferase activity assay demonstrated that miR-21 directly targets SOX6, and that down-regulation of SOX6 expression using siRNA increased the percentage of cells that were insulin positive. These results indicate that miR-21 acts as a bidirectional switch in the formation of IPCs by regulating the expression of target genes. Small RNA interference is a

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process in which RNA molecules inhibit gene expression, typically by causing the destruction of specific mRNA molecules. Specific siRNAs were designed and used to verify the function of miR-21 targets and to promote the formation of IPCs. The results demonstrated that HES1 and SOX6 are important regulators in the formation of IPCs from pancreatic progenitors. HES1 is typically associated with proliferation and the maintenance of an undifferentiated state, while SOX 6 plays an important role in pancreas development by suppressing PDX1 expression in beta cells and attenuating glucose-stimulated insulin secretion. In conclusion, we used over-expression of miR-21 and specific siRNAs to elucidate the role of miR-21 and its targets in the proliferation and differentiation of pancreatic progenitors. SOX6 is an inhibitor of IPCs formation through suppression of PDX1-mediated stimulation of the insulin gene promoter. RBPJ inhibits expression of HES1, which is a potent repressor of islet hormone genes. miR-21 directly targets SOX6 and RBPJ during IPCs formation, therefore, miR-21 acts as a bidirectional switch in the formation of IPCs by regulating the expression of target genes. Additional studies focusing on specific miRNAs involved in the formation of IPCs from progenitors may advance the development of effective cell transplant therapies for diabetes mellitus. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.bbagrm.2015.12.001. Competing financial interests The authors declare that they have no competing interests. Author contributions C.B, Y.G and X.L have the equal contribution to this work. C.B and Y.G participated in the analysis of the sequencing data and drafted the manuscript, X.L and K.W performed the analysis of the sequencing data and Luciferase reporter assay, S.Z and Y.F carried out the Western blotting and cell Transplantation, and W.G and Y.M conceived of the study, and participated in its design and coordination. Acknowledgments This research was supported by the Agricultural Science and Technology Innovation Program (cxgc-ias-01), the National Natural Science Foundation of China (Grant No. 31472099), the National Infrastructure of Animal Germplasm Resources (2015), the Ministry of Agriculture of China for Transgenic Research Program (2014ZX08009–003–006, 2014ZX08012–002–06) and China Postdoctoral Science Foundation (2015M571181). References [1] H. Zulewski, Stem cells with potential to generate insulin producing cells in man, Swiss Med. Wkly. 136 (2006) 647–654. [2] R. Scharfmann, Alternative sources of beta cells for cell therapy of diabetes, Eur. J. Clin. Investig. 33 (2003) 595–600. [3] G. Pennarossa, S. Maffei, M. Campagnol, L. Tarantini, F. Gandolfi, T.A. Brevini, Brief demethylation step allows the conversion of adult human skin fibroblasts into insulin-secreting cells, Proc. Natl. Acad. Sci. U. S. A. 110 (2013) 8948–8953. [4] E. Hunziker, M. Stein, Nestin-expressing cells in the pancreatic islets of Langerhans, Biochem. Biophys. Res. Commun. 271 (2000) 116–119. [5] J. Lardon, I. Rooman, L. Bouwens, Nestin expression in pancreatic stellate cells and angiogenic endothelial cells, Histochem. Cell Biol. 117 (2002) 535–540. [6] L. Selander, H. Edlund, Nestin is expressed in mesenchymal and not epithelial cells of the developing mouse pancreas, Mech. Dev. 113 (2002) 189–192. [7] M.K. Treutelaar, J.M. Skidmore, C.L. Dias-Leme, M. Hara, L. Zhang, D. Simeone, D.M. Martin, C.F. Burant, Nestin-lineage cells contribute to the microvasculature but not endocrine cells of the islet, Diabetes 52 (2003) 2503–2512. [8] V. Plaisance, G. Waeber, R. Regazzi, A. Abderrahmani, Role of microRNAs in islet beta-cell compensation and failure during diabetes, J. Diabetes Res. 2014 (2014) 618652. [9] M. Kalis, C. Bolmeson, J.L. Esguerra, S. Gupta, A. Edlund, N. Tormo-Badia, D. Speidel, D. Holmberg, S. Mayans, N.K. Khoo, A. Wendt, L. Eliasson, C.M. Cilio, Beta-cell specific deletion of Dicer1 leads to defective insulin secretion and diabetes mellitus, PLoS One 6 (2011), e29166.

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