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Keywords:

  • MICRORNA;
  • MICROARRAY;
  • OSTEOPOROSIS;
  • OVARIECTOMY;
  • PPARγ;
  • CREB

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

Growing evidence shows the possibility of a role of microRNAs (miRNA) in regulating bone mass. We investigated the change of miRNAs and mRNA expression profiles in bone tissue in an ovariectomized mice model and evaluated the regulatory mechanism of bone mass mediated by miRNAs in an estrogen-deficiency state. Eight-week-old female C3H/HeJ mice underwent ovariectomy (OVX) or sham operation (Sham-op), and their femur and tibia were harvested to extract total bone RNAs after 4 weeks for microarray analysis. Eight miRNAs (miR-127, -133a, -133a*, -133b, -136, -206, -378, -378*) were identified to be upregulated after OVX, whereas one miRNA (miR-204) was downregulated. Concomitant analysis of mRNA microarray revealed that 658 genes were differentially expressed between OVX and Sham-op mice. Target prediction of differentially expressed miRNAs identified potential targets, and integrative analysis using the mRNA microarray results showed that PPARγ and CREB pathways are activated in skeletal tissues after ovariectomy. Among the potential candidates of miRNA, we further studied the role of miR-127 in vitro, which exhibited the greatest changes after OVX. We also studied the effects of miR-136, which has not been studied in the context of bone mass regulation. Transfection of miR-127 inhibitor has enhanced osteoblastic differentiation in UAMS-32 cells as measured by alkaline phosphatase activities and mRNA expression of osteoblast-specific genes, whereas miR-136 precursor has inhibited osteoblastic differentiation. Furthermore, transfection of both miR-127 and miR-136 inhibitors enhanced the osteocyte-like morphological changes and survival in MLO-Y4 cells, whereas precursors of miR-127 and -136 have aggravated dexamethasone-induced cell death. Both of the precursors enhanced osteoclastic differentiation in bone marrow macrophages, indicating that both miR-127 and -136 are negatively regulating bone mass. Taken together, these results suggest a novel insight into the association between distinct miRNAs expression and their possible role through regulatory network with mRNAs in the pathogenesis of estrogen deficiency–induced osteoporosis. © 2014 American Society for Bone and Mineral Research.


Introduction

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

Estrogen plays a fundamental role in the maintenance of skeletal homeostasis. The relationship between reduced estrogen levels in women with ovarian failure and the development of postmenopausal osteoporosis has been well established over the past 60 years.[1-3] In addition, estrogen-replacement therapy has proven to be efficacious in increasing bone mineral density and preventing fractures.[4, 5]

MicroRNAs (miRNAs) have emerged as a new dimension of gene regulation in recent years. MicroRNAs are noncoding RNAs of ∼22 nucleotides that function at the posttranscriptional level, usually regulating translational repression of their target mRNAs by base-pairing to the 3' untranslated region (UTR).[6] It has been suggested that miRNAs regulate the gene expression of more than 50% of protein-coding genes in human[7] and can coordinately regulate a group of genes encoding proteins with related functions.[8, 9] Growing evidence indicates that these miRNAs play a key regulatory role in diverse physiological and pathological processes and are showing great potential as possible biomarkers and new therapeutic targets for various diseases.[10-12]

Recently, a number of studies showed the possibility of the role of miRNAs in regulating bone mass. The first evidence came from the studies on the cartilage cell-specific Dicer knockout mice, which exhibited severe skeletal defects during development.[13] Several miRNAs have been shown to specifically express in the developing skeletal system,[14] and these expression patterns provided the first step in a mechanistic analysis of candidate miRNAs and their function. More recent studies focused on the regulation of osteoblastic or osteoclastic differentiation by miRNA. During BMP2-induced osteoblastic differentiation of the mesenchymal C2C12 cells, 22 miRNAs were significantly downregulated.[15] Among them, miR-133 and miR-135 inhibited differentiation of osteoprogenitors by attenuating Runx2 and Smad5 pathways.[15] The expression of miR-206 decreased over the course of osteoblastic differentiation, and transgenic mice with osteoblast-specific overexpression miR-206 developed a low bone mass phenotype with impaired osteoblast differentiation by targeting connexin 43.[16] In addition, miRNA-204 targets Runx2 in bone marrow–derived mesenchymal stem cells (MSCs), thereby inhibiting osteoblastic differentiation, whereas it reciprocally stimulates adipogenesis of mesenchymal progenitor cells.[17] Another miRNA, miR-2861, which is conserved across species, was shown to target the HDAC5 and thereby enhanced the osteoblastic differentiation.[18] Furthemore, homozygous mutation in pre-miR-2861 that blocked expression of miR-2861 was shown to cause primary osteoporosis in two related adolescents.[18] Recently, Kim and colleagues showed that miR-182 functions as a FoxO1 inhibitor to antagonize osteoblast proliferation and differentiation, with a subsequent negative effect on osteogenesis.[19] Regarding osteoclast differentiation, miR-223 has been shown to play an essential role by regulating NFI-A and M-CSFR.[20, 21] In receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis, miR-21 was identified as an miRNA expression signature through positive feedback loop of c-Fos/miR-21/PDCD4.[22] These studies have provided direct or indirect evidence of the role of miRNAs in bone mass regulation; however, there has been no study that investigated miRNA changes by estrogen deprivation in vivo, which is important in the context of postmenopausal osteoporosis.

Here, we showed the change of miRNA/mRNA expression profiling in bone tissue in an ovariectomy (OVX)-induced osteoporosis mouse model using microarray analysis and also investigated the regulatory mechanism of miRNAs on bone mass through integrative miRNA/mRNA regulatory network in estrogen deficiency state.

Materials and Methods

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

Animals

Female C3H/He mice aged 8 weeks were purchased from Orient Corporation (Kapyoung, Korea). Bilateral OVX (n = 3) or sham operation (Sham-op, n = 3) was performed by standard method under general anesthesia induced by subcutaneous injections of xylazine (2.2 mg/kg Rompun, Bayer, Monheim, Germany) and tiletamine/zolazepam (6.0 mg/kg Zoletil 100, Virbac, Carros Cedex, France).[23] Whole-body bone mineral density (BMD) and bone mineral content (BMC) were measured before and 4 weeks after operation using PIXImus II densitometer (software version 2.0; GE Lunar, Madison, WI, USA) as previously described.[24] To measure micro-computed tomography (µCT), femurs were dissected free of soft tissue, fixed overnight in 70% ethanol, and analyzed with a µCT scanner and associated analysis software (model 1076, Skyscan, Antwerp, Belgium). Image acquisition was performed at 35 kV of energy and 220 A of intensity with a voxel size of 9 µm.

All animal experiments were performed under the approval from the Institutional Animal Care and Use Committee of Seoul National University (approval number SNU-100201-2), and all animals were housed in the Centers for Laboratory Animal Care at the Seoul National University College of Medicine.

mRNA microarray chip processing and analysis of gene expression data

Four weeks after OVX or Sham-op (aged 12 weeks), the mice were euthanized, and femur and tibia were isolated. To harvest bone tissue, we have removed epiphyseal cartilage from femurs and tibias and also flushed out bone marrow. Therefore, the bone tissue is supposed to consist of trabecular and cortical compartment of these long bones. Total RNA was extracted using the mirVana miRNA isolation kit (Ambion, Austin, TX, USA) and was amplified and labeled according to the Affymetrix GeneChip Whole Transcript Sense Target Labeling protocol. The resultant labeled cDNA was hybridized to Affymetrix Mouse Gene 1.0 ST arrays (Santa Clara, CA, USA). The microarray images were scanned with the Agilent microarray scanner (Agilent G2565CA). The scanned raw expression values were background-corrected, normalized, and summarized using robust multiarray averaging.[25] To detect differentially expressed genes, moderated t statistics based on an empirical Bayes approach was used,[26] and p < 0.05 was considered significant.

miRNA microarray chip processing and analysis of gene expression data

Total RNA isolation and small RNA (including miRNA) enrichment procedure were performed with the mirVana miRNA Isolation Kit (Ambion). RNA concentration was quantified using the Nano Drop spectrophotometer and the RNA integrity was evaluated using Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA). Agilent Mouse miRNA Microarray Kit (V3) was used as an miRNA microarray chip for hybridization. RNA labeling and hybridization were performed according to the manufacturer's instructions. The microarray images were scanned with the Agilent microarray scanner (Agilent G2565CA). The total gene signals were extracted as “GeneView” data files using Agilent Feature Extraction software version 10.7.3.1 with default protocols and settings.[27] The signals of miRNAs whose expressions were defined as “undetected” by Agilent Feature Extraction software were regarded as missing values. The expression signals were further log2-transformed. The comparisons were performed with moderated t statistics,[26] and p < 0.05 was considered significant.

Integrative analysis of miRNA and mRNA expression profiles to reconstruct an miRNA/mRNA regulatory network

The posttranscriptional regulatory network of miRNAs and mRNAs in the estrogen deficiency–induced bone loss model has been defined as a directed, bipartite graph in which miRNA-mRNA relationships are supported by both targeting predictions and expression data. Specifically, the network has been reconstructed using the subset of differentially expressed miRNAs and mRNAs characterized by (1) a regulatory relationship according to miRNA target prediction programs Target scan[28] and Pictar predictions[29] and (2) expression profiles strongly anticorrelated. Because miRNAs tend to downregulate target mRNAs, the expression profiles of genuinely interacting pairs are expected to be anticorrelated. The networks were drawn using Cytoscape, and the functional clustering analysis was performed using Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics Resources 6.7 (http://david.abcc.ncifcrf.gov/home.jsp).[30]

Gene set enrichment analysis

We performed the gene set enrichment analysis (GSEA) (Pub Med ID 16199517)[31] to find molecular pathways or gene ontologies that are made up of differentially expressed mRNAs and putative targets of differentially expressed miRNAs after OVX. Difference in group average of log-transformed intensity was used as a metric for sorting probes in each data set. After sorting genes according to the metric, enrichment scores were calculated for 143 different gene sets that are curated in the mSigDB database (http://www.broadinstitute.org/gsea/msigdb/). Specifically, curated gene sets, computational gene sets, and gene ontology gene sets were explored for differential enrichment in upregulated or downregulated genes. The p values for the enrichment scores were calculated after permuting class labels of each experimental condition and gene sets with p values less than 0.05 were extracted. The significant gene sets with an absolute enrichment score more than 0.35, p value less than 0.05, and more than 15 component genes were visualized as a network with nodes representing gene sets and edges connecting two nodes if the two gene sets share a significant number of genes with hypergeometric test. Two gene sets had a significant number of genes in common if the hypergeometric test p value was within the smallest 1% of the p values for every pair of gene sets.

Cell cultures

Murine osteoblastic UAMS-32 cells were cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), and osteoblastic differentiation was induced by recombinant human BMP-2 (300 ng/mL; R&D Systems, Minneapolis, MN, USA) for 3 days. Osteocyte-like cell line, MLO-Y4 cells, were kindly provided by Dr Lynda Bonewald (University of Missouri-Kansas City, Kansas City, MO, USA). MLO-Y4 cells were maintained on rat-tail collagen type 1–coated dishes (BD Bioscience, San Jose, CA, USA) in αMEM with 5% FBS and 5% bovine calf serum. Primary bone marrow macrophages (BMMs), which are precursors of osteoclasts, were isolated from 6-week-old C3H/He female mice as described previously.[32] Briefly, total bone marrow cells from femur and tibia were flushed out using a 27-gauge needle, and the resulting cells were cultured in αMEM supplemented with 10% FBS, 1% glutamine, and 30 ng/mL M-CSF and cultured for 3 days. To induce osteoclast formation, BMMs were treated with M-CSF (30 ng/mL, R&D Systems) and RANKL (50 ng/mL, Peprotech, Rocky Hill, NJ, USA) for 4 to 5 days.

Transfection of miRNA inhibitors or precursor

Anti-miR miRNA inhibitor, Pre-miR miRNA precursor, and fluorescein (FAM)-labeled negative control (Ambion) were transfected into cells at approximately 60% to 70% confluence using lipofectamin 2000 (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions. Transfection was done 12 hours before any treatment, and the transfection efficiency was evaluated by FAM-labeled control using fluorescent microscopy for each experiment.

Real-time RT-PCR for miRNA and mRNA analysis

For miRNA RT-PCR, 10 ng of total RNA was reverse-transcribed using miScript Reverse Transcription Kit (Qiagen, Hilden, Germany). qRT-PCR was performed using miRNA-specific miScript Primer Assay and miScript SYBR Green PCR Kit (Qiagen). RNU6 was used as the internal control. For mRNA qRT-PCR, 1 µg of total RNA was reverse-transcribed using a Reverse Transcription System Kit (Promega, Madison, WI, USA) and PCR amplified with gene-specific primers and SYBR Green PCR master mix (PerkinElmer Life Sciences, Waltham, MA, USA) using ABI PRISM 7900 HT sequence detection system (Applied Biosystems, Carlsbad, CA, USA). β-actin was used as the internal control. Thermal cycling conditions were as follows: 10 seconds at 95°C, 40 cycles of 30 seconds at 95°C, 30 second at 52°C, and 30 seconds at 72°C. Data were analyzed by the relative quantification (ΔΔCT) method. Primer sequences used for amplification are listed in Supplemental Tables S1 and S2.

Alkaline phosphatase (ALP) assay and staining

The degree of osteoblastic differentiation of C3H10T1/2 cells was measured by ALP activities and staining. To assess ALP activities, cells in 24-well plates were washed three times with ice-cold PBS scraped after adding 0.5% Triton X-100. Enzyme activity assay was performed in assay buffer (10 mM MgCl2 and 0.15 M alkaline buffer, pH 10.3) with 10 mM p-nitrophenyl phosphate as substrate. Absorbance was read by an ELISA reader (ThermoMax, Scientific Surplus, Hillsborough, NJ, USA) at OD405. Relative ALP activity is defined as mmol of p-nitrophenol phosphate hydrolyzed per minute per mg of total protein.

Tartrate-resistant acid phosphatase (TRAP) staining

The degree of osteoclastic differentiation of BMM was measured by TRAP staining. Cells in 96-well plates were washed with PBS and were fixed and stained for TRAP, a marker of osteoclast differentiation, according to the manufacturer's instructions (Sigma-Aldrich, St. Louis, MO, USA). TRAP-positive cells with more than three nuclei were counted as multinucleated cells (MNCs).

MTT assay

The MLO-Y4 cells, seeded on a 96-well plate (104 cells per well), were treated with 10−6 M dexamethasone to induce cellular apoptosis for 24 hours. After 24 hours, 30 µL stock solution of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) was added to each well and incubated for 4 hours at 37°C. After incubation for 4 hours, 30 µL of DMSO was added to dissolve the dark blue crystals for 5 minutes. The absorbance was read by an ELISA reader (ThermoMax, Scientific Surplus) at a wavelength of 540 nm.

Transmission electron microscopy (TEM) and scanning electron microscopy (SEM)

To examine the ultrastructural changes of MLO-Y4 cells, the cells were visualized using an electron microscope after fixation with 2.5% glutaraldehyde in 0.1 M phosphate buffer (pH 7.4) followed by processing for transmission and scanning electron microscopy (TEM and SEM) as described previously.[33] Briefly, after fixation and rinsing in the same buffer, the cells were fixed at 1% OsO4 in 0.04 M phosphate buffer with 0.14 M sucrose for 10 minutes at 4°C. Subsequently, dehydration with standard ethanol series was done. For TEM analysis, the sample was infiltrated with epoxy resin and transferred to beam capsules for polymerization in the oven, then the capsules were separated from the polymerized resin with a razor blade. Embedded cells in hardened blocks were viewed with an optical microscope to choose the appropriate area, and ultrathin sections of the chosen area were obtained using an ultramicrotome (Sorvall MT-6000; DuPont, Wilmington, DE, USA). After heavy metal staining of the section with lead citrate and 4% uranyl acetate, the samples were examined through the transmission electron microscope (JEM-1400; JEOL Ltd., Tokyo, Japan) at 50 kV. For SEM, dehydrated samples were freeze-embedded in t-butyl alcohol and freeze-dried, then coated with osmium and observed with the scanning electron microscope (JEM-7401F; JEOL Ltd.)

Statistical analysis

The analyses of gene expression data for microarray and GSEA were described in each section. For the other analysis, statistical significance was analyzed by the nonparametric Mann-Whitney test or Wilcoxon matched-pairs test for group comparison, and p < 0.05 was considered significant. Data are expressed as means ± SD (or SEM). Statistical analyses were performed using SPSS software (version 17.0, SPSS Inc., Chicago, IL, USA).

Results

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

Establishment of estrogen deficiency–induced osteoporosis mice model

We first examined the BMD and BMC of C3H mice before (aged 8 weeks) and 4 weeks after (aged 12 weeks) OVX or Sham-op. Because the mice at these ages are in growing phase, the BMD and BMC of the animals were actually increased. However, OVX mice exhibited significantly less BMD and BMC gain compared with Sham-op mice (BMD 0.0479 ± 0.0005 versus 0.0516 ± 0.0007 mg/cm2; BMC 0.491 ± 0.019 versus 0.542 ± 0.011 mg; all p < 0.001, Fig. 1A, B). In addition, μCT analysis of femurs also showed a corresponding reduction in trabecular bone area as shown in Fig. 1C.

image

Figure 1. Establishment of estrogen deficiency–induced bone loss model. Eight-week-old female C3H/He mice were sham operated (Sham, n = 3) or bilateral ovariectomized (OVX, n = 3) and whole-body bone mineral density (BMD, mg/cm2) and bone mineral content (BMC, mg) were measured before and 4 weeks after operation, using PIXImus II densitometer. Representative µCT images of the distal femur at 4 weeks after sham operation or ovariectomy. Data are expressed as means ± SD. *p < 0.001 versus Sham-op.

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Identification of differentially expressed miRNAs in bone tissue after OVX

To identify differentially expressed miRNAs in bone tissue induced by estrogen deficiency, we carried out miRNA microarray profiling of bone tissue 4 weeks after OVX or Sham-op. Among 567 miRNAs investigated, the expression levels of nine miRNAs were significantly different between OVX and Sham-op mice (cutoff; p < 0.05, fold-change > 1.5); miR-127, -133a, -133a*, -133b, -136, -206, -378, and -378* were significantly upregulated and miR-204 was downregulated in OVX mice compared with Sham-op mice (Fig. 2A, B). The heat map of 188 miRNAs with a p value less than 0.1 is shown in Supplemental Fig. S1. Validation analysis using qRT-PCR data also showed compatible changes (Fig. 2C). The absolute expression level of these miRNAs in each group of animals varied with the miR-127 and miR-378 having the highest expression levels (Fig. 2D).

image

Figure 2. Differentially expressed miRNAs in bone tissue after OVX. To identify differentially expressed miRNAs in bone tissue involved in the estrogen deficiency–induced bone loss, we carried out miRNA microarray profiling of bone tissue 4 weeks after OVX or Sham-op. Among 567 miRNAs investigated, expression levels of eight miRNAs (miR-127, -133a, -133a*, -133b, -136, -206, -378, and -378*) were significantly upregulated, and one miRNA (miR-204) was downregulated after OVX (cutoff; p < 0.05, fold-change > 1.5). (A) Heat map of differentially expressed miRNAs between Sham and OVX mice in miRNA array. The color scale bar represents the relative miRNA expression changes normalized by the standard deviation. Red denotes high expression and green denotes low expression relative to the median. (B) Relative miRNAs' expression of differentially expressed miRNAs in OVX mice (all p < 0.001). (C) Of eight miRNAs that were differentially expressed in the miRNA array, six miRNAs were validated by qRT-PCR (all p < 0.001). (D) Absolute expression levels of the six validated miRNAs relative to each other.

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Identification of differentially expressed mRNAs in bone tissue after OVX

We concurrently investigated the change of mRNA expression using mRNA microarray in bone tissue after OVX. We systematically analyzed the microarray data using two approaches. First, we identified differentially expressed mRNAs between OVX and Sham-op mice by overrepresentation analysis (ORA). Among 28,785 mRNAs investigated, 658 genes were significantly changed in OVX mice (cutoff; p < 0.05, fold-change > 1.5), 503 genes (76.4%) were upregulated, and 155 genes (23.6%) were downregulated in OVX mice (Fig. 3). Functional annotations for those differentially expressed genes were performed by the DAVID database, and we have identified several major functional clusters and pathways of genes. On medium stringency (default DAVID software setting), 109 clusters were identified in upregulated genes and 11 clusters in downregulated genes after OVX. Of these, 10 and two clusters from upregulated and downregulated genes, respectively, reached an enrichment score 1.5 or greater (Table 1). Of the 10 clusters of upregulated genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway members were consolidated into four common cellular biological processes. Functional gene cluster related with mitochondrial oxidative phosphorylation (OXPHOS) and electron transport chain comprised the largest number of genes (95 genes), with protein catabolic process,[41] nucleotide catabolic process,[19] and lysosome regulation[15] being present in decreasing frequency. Two clusters of downregulated genes after OVX were enzyme inhibitor activity (11 genes) and immunoglobulin component.[4]

image

Figure 3. Differentially expressed mRNAs in bone tissue after OVX identified by overrepresentation analysis (ORA). To identify differentially expressed mRNAs in bone tissue involved in the estrogen deficiency–induced bone loss, we carried out mRNA microarray profiling of bone tissue 4 weeks after OVX or Sham-op. Among 28,785 mRNAs investigated, 658 genes were significantly changed in OVX mice (cutoff; p < 0.05, fold-change > 1.5); 503 genes (76.4%) were upregulated and 155 genes (23.6%) were downregulated in OVX mice. Heat map and hierarchical clustering of differentially expressed 658 mRNAs between Sham and OVX mice. The color scale bar represents the relative mRNA expression changes normalized by the standard deviation. Red denotes high expression and green denotes low expression relative to the median.

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Table 1. Functional Annotations for Differentially Expressed Genes Performed by the DAVID Database
 Annotation clusters (enrichment score)Related categoriesGO and KEGG terms (functional cluster members)Unique membersp Value of clusters
Up (n = 503)1 (6.68)MitochondriaGO:0005739 (68), GO:0044429 (34), GO:0005740 (27), GO:0019866 (23), GO:0031966 (25), GO:0005743 (22), GO:0031967 (29), GO:0031975 (29), GO:003109 (37), GO:0006119 (10), GO:0045333 (10), GO:0015980 (12), GO:0022900 (11), GO:0042773 (5), GO:0070469 (7), GO:0042775 (4), mmu00190 (15), mmu05012 (12), mmu05016 (14), mmu05010 (11)95<0.001
2 (3.67)OXPHOS
5 (2.08)
6 (2.04)
7 (1.98)
3 (2.40)Nucleotide catabolic processGO:0016779 (7), GO:0009166 (5), GO:0009264 (4), GO:0034655 (5), GO:0034656 (5), GO:0044270 (5), GO:0009262 (4), GO:0046700 (4), GO:0009123 (4), mmu00230 (9), mmu00240 (5)19<0.01
4 (2.15)
8 (1.88)
9 (1.52)Lysosome regulationGO:0005773 (9), GO:0005764 (7), GO:0000323 (7), mmu04142 (11)15<0.05
10 (1.50)Protein catabolic processGO:0006508 (30), GO:0044265 (20), GO:0051603 (18), GO:0044257 (18), GO:0043632 (17), GO:0019941 (17), GO:0030163 (18), GO:0009057 (20), GO:0006511 (7)41<0.05
Down (n = 155)1 (3.03)Immunoglobulin componentGO:0003823 (4)4<0.001
2 (2.79)Enzyme inhibitor activityGO:0030414 (7), GO:0004857 (7), GO:0004866 (6), GO:0004869 (3), GO:0004867 (3)11<0.001

Next, we compared mRNA expression levels of metabolic pathways between OVX and Sham-op groups using GSEA, which is a cutoff-free enrichment analysis method that reveals coordinated transcriptomic alterations of gene sets. Several metabolic pathways showed differential expression in OVX mice with significant activation of networks related with mitochondrial OXPHOS mediated by PPARγ pathway, stress response mediated by CREB pathway, and pathways of apoptosis (Fig. 4). Pathways of immune system process including complement activation and leukocyte migration were downregulated after OVX and had a connection with upregulated stress response and cellular apoptosis networks. Because the individual nodes in Fig. 4 do not actually represent individual genes per se, we have constructed another heat map using 108 genes that correspond to three upregulated (mitochondria and OXPHOS, stress response and apoptosis) and one downregulated (immune process) pathway and annotated each gene on the right axis of the heat map (Fig. 4B).

image

Figure 4. Differentially expressed metabolic pathways in bone tissue after OVX identified by gene set enrichment analysis (GSEA). Difference in group average of log-transformed intensity was used as a metric for sorting probes in each data set. After sorting genes according to the metric, enrichment scores were calculated for 143 different gene sets that are curated in the mSigDB database (http://www.broadinstitute.org/gsea/msigdb/). (A) The significant gene sets with an absolute enrichment score more than 0.35, p value less than 0.05, and more than 15 component genes were visualized as a network with nodes representing gene sets and edges connecting two nodes if the two gene sets share a significant number of genes with hypergeometric test. Red nodes are upregulated pathways; green nodes are downregulated pathways; nodes with thick blue borders are anticorrelated target pathways of differentially expressed miRNA in OVX; diamonds are gene sets with adjacent chromosomal position (C1); circles are curated gene sets from public pathway databases (C2); rectangles are gene sets regulated by the specified transcription factor (C3); triangles are computational gene sets (C4); hexagons gene ontology gene sets (C5). (B) Heap map showing the 108 genes that correspond to 3 upregulated (mitochondria and OXPHOS, stress response and apoptosis) and 1 downregulated (immune process) pathway identified by functional clustering analysis.

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Integrative analysis of miRNA/mRNA expression and reconstruction of a regulatory network in OVX-induced bone loss

The integrative analysis of miRNA/mRNA expression profiles allows reconstructing a network of functional interactions occurring in estrogen deficiency–induced bone loss. Our integrative approach assumes an anticorrelated expression profile between an miRNA and its predicted mRNA targets. The network consists of nine differentially expressed miRNAs and 75 anticorrelated targets with the number of targets per miRNA ranging from 2 to 30 and 9.3% of the genes being targeted by at least 2 miRNAs (Fig. 5). The names of unique genes that are potential targets of miRNAs are shown in Table 2. Functional clustering analysis of anticorrelated target genes revealed various overrepresented biological processes including mitochondrial OXPHOS, calcium ion binding, cellular homeostasis, response to hormone stimuli, regulation of apoptosis, and reproductive structure development (Table 3). In pathway-pathway network, analyzed by GSEA of differentially expressed mRNAs in OVX, these anticorrelated miRNA target genes occupied important nodes including the PPARγ and CREB pathways (Fig. 4, nodes with thick blue borders).

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Figure 5. Integrative analysis of miRNA/mRNA expression and reconstruction of a regulatory network in OVX-induced bone loss. The network has been reconstructed using the subset of differentially expressed miRNAs and mRNAs characterized by (1) a regulatory relationship according to miRNA target prediction programs Target scan and Pictar prediction and (2) expression profiles strongly anticorrelated. The network consists of nine differentially expressed miRNAs and 75 anticorrelated targets, with the number of targets per miRNA ranging from 2 to 30, and 9.3% of the genes being targeted by at least two miRNAs.

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Table 2. Differentially Expressed Candidate Target Genes of miRNAs
Down (n = 56)ADCYAP1, ATP6V0D1, CAMK1, CTTNBP2NL, DCBLD1, DCUN1D3, DNAJB1, EDEM1, EFEMP2, FAM120A, FAM134B, FRMD8, FST, FTL1, FTL2, GRAMD1A, GRM7, KLF3, LFNG, LOXL3, M6PR, MFSD11, MMAB, MTSS1L, MXD4, OSBPL7, P2RY2, PDRG1, PEX5, PGAM1, PHC2, PIGV, PIM1, PIP4K2B, PPTC7, PTPN1, RAB34, SH3BGRL3, SLC27A4, SLC8A1, SMARCB1, SMEK1, TKT, TPM3, TSPAN4, UBE2Q1, UBTD2, WDR6, WNT7A, ZCCHC14, ZFP282, 1110038D17RIK, 1110049F12RIK, 2310002L09RIK, 4930420K17RIK, 6330409N04RIK
Up (n = 19)ABCA2, AK3L1, BAG4, BBC3, CALM2, CECR6, HMCN1, LETM1, MAB21L1, NCAM1, PHEX, RNASE6, RYBP, SGK1, SLC25A22, SLC25A25, SOCS6, TCF3, THBS1
Table 3. Functional Clustering Analysis of Candidate Target Genes of miRNAs Performed by the DAVID Database
Annotation clusters (enrichment score)Related categoriesGO and KEGG terms (functional cluster members)Unique membersp Value of clusters
1 (1.83)MitochondriaGO:0044429 (8), GO:0005739 (8), GO:0005740 (4), GO:0005743 (3), GO:00031966 (4)LETM1, SLC25A25, BBC3, SLC25A22, 2310002L09RIK, AK3L1, MMAB, PPTC7<0.01
2 (1.73)Calcium ion bindingGO:0005509 (8)SLC8A1, LETM1, SLC25A25, EFEMP2, TKT, THBS1, EDEM1, CALM2<0.01
3 (1.65)Cellular homeostasisGO:00019725 (5), GO:0048878 (5), GO:0042592 (6), GO:0055082 (4), GO:003003 (3), GO:0055080 (3), GO:0055081 (3)SGK1, SLC8A1, FTL1, FTL2, SOCS6, SH3BGRL3, ABCA2<0.05
4 (1.55)Response to hormone stimuliGO:0010033 (5), GO:0009725 (3), GO:0009719 (3)SGK1, ABCA2, PTPN1, EDEM1, TCF3<0.05
5 (1.50)ApoptosisGO:0006915 (5), GO:00012501 (5), GO:0008219 (5), GO:0016265 (3)BAG4, BBC3, GRM7, PIM1, DCUN1D3, SGK1, RYBP<0.05

Effect of miR-127 and -136 on bone cells in vitro

To validate the potential involvement of differentially expressed miRNAs in bone metabolism, we further studied the role of miR-127 in vitro, which exhibited the greatest changes after OVX. We also studied the effects of miR-136, which has not been studied in the context of bone mass regulation. First, we investigated the change of miRNA-127 expression in UAMS-32 cells, a mouse osteoblastic cell line, in varying concentration (approximately 10−10 to 10−7 M) of estradiol (E2). As shown in Fig. 6A, the expression of endogenous miRNA-127 was significantly upregulated at low concentration from 1 × 10−11 to 10−9 M, whereas it became downregulated at concentration of 1 × 10−7 M. The expression of miRNA-136 showed a similar pattern, although the expression level at 1 × 10−7 M was similar to the vehicle treatment.

image

Figure 6. Effect of miR-127 and -136 on bone cells in vitro. (A) UAMS-32 cells were treated with various concentrations of estradiol (E2) for 48 hours, and the expression of miR-127 and -136 was evaluated by real-time quantitative PCR. Data are expressed as means ± SD. *p < 0.01 versus vehicle, #p < 0.05 versus vehicle. (B) UAMS-32 cells were transfected with miR-127 or -136 inhibitor (50 nM), precursor (10 nM), or respective controls, and osteogenic differentiation was induced by BMP-2 (300 ng/mL). Alkaline phosphatase (ALP) activities were measured after 3 days. Data are expressed as means ± SD. *p < 0.05 versus control. (C) Total RNA was extracted from UAMS-32 cells grown for 3 days in the presence of indicated miRNA inhibitor, precursor, or respective controls. The amount of mRNA for osteoblast-specific products was determined by real-time quantitative PCR and expressed as mRNA abundance relative to control. Data are expressed as means ± SD. *p < 0.05 versus control. (D) Bone marrow macrophage (BMM) isolated from C3H/H3 female mice were cultured in the presence of M-CSF (30 ng/mL) and RANKL (50 ng/mL) to induce osteoclastic differentiation after transfection with miR-127 or -136 inhibitor, precursor, or respective controls. Tartrate-resistant acid phosphatase (TRAP) stain was done after 4 to 5 days after culture, and the number of TRAP-positive multinucleated cells (MNCs) was counted. Data are expressed as means ± SD. *p < 0.01 versus control. (E) MLO-Y4 cells were observed under the scanning or transmission electron microscopy (SEM or TEN) after transfection with miR-127 or -136 inhibitor or control. Scale bars: SEM = 50 µm, TEM = 1 µm. White arrow = mitochondria; black arrow = autophagosome. (F) After transfection with miR-127 or -136 inhibitor, precursor, or respective controls, MLO-Y4 cells were treated with 10−6 M dexamethasone to induce cellular apoptosis for 24 hours. Cell viability was quantified by MTT assay. Data are expressed as means ± SD. *p < 0.01 versus control. #p < 0.01 versus Dexa (−).

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Next, we examined the effects of miRNA-127 and -136 on osteoblastic differentiation. When UAMS-32 cells were transfected with miR-127 inhibitor and induced osteoblastic differentiation with BMP-2 for 3 days, ALP activities were significantly increased by 40% compared with negative control, whereas transfection with miR-127 precursor did not result in any discernable changes (Fig. 6B). In contrast, transfection of miR-136 inhibitor did not have positive effects, whereas miR-136 precursor has significantly downregulated the ALP activities by 50% in UAMS-32 cells. Consistent with ALP activity results, mRNA expressions of osteoblast-specific genes, collagen type 1 (Col1), ALP, Runx2, osteocalcin (OC) measured by quantitative real-time PCR were significantly increased by transfection of miR-127 inhibitor, whereas the levels were significantly reduced by transfection of miR-136 precursor in the presence of BMP-2 (Fig. 6C), indicating that both miR-127 and -136 are negatively regulating osteoblast differentiation.

We also studied the effects of miR-127 and -136 on osteoclastic differentiation using BMM isolated from C3H/He female mice. When we cultured BMM in the presence of M-CSF and RANKL to induce osteoclastic differentiation, transfection of miR-127 inhibitor has resulted in a significant reduction in the number of TRAP-positive MNCs at 4 to 5 days after culture, whereas miR-127 precursor has significantly increased osteoclast formation (Fig. 6D). The same pattern of inhibition and stimulation of osteoclastogenesis was observed with transfection of miR-136 inhibitor and precursor, respectively (Fig. 6D).

We next evaluated the effects of miRNA-127 and -136 on cell morphology and survival of MLO-Y4 cells, which exhibited characteristics of osteocyte. The SEM examination showed that the transfection of MLO-Y4 cells with miR-127 or -136 inhibitors for 48 hours enhanced the extended dendritic processes (Fig. 6E). The TEM examination showed that the transfection with miR-127 or -136 inhibitors increased the mitochondria numbers (white arrow) and decreased autophagosomes (black arrow; Fig. 6E). Moreover, when cellular apoptosis was induced by treatment with dexamethasone, transfection of miR-127 inhibitor has significantly enhanced cell survival, whereas transfection of miR-127 precursor has aggravated cell death (Fig. 6F). Notably, transfection of miR-127 precursor alone has also promoted cell death compared with negative control even in the absence of dexamethasone. Essentially the same pattern of enhanced survival or aggravated cell death was observed with transfection of miR-136 inhibitor or precursor, respectively (Fig. 6F).

Taken together, these results suggest that both miR-127 and miR-136 may be involved in the suppression of osteoblastic differentiation and osteocyte function and survival, while they promote osteoclast differentiation, thereby contributing to the loss of bone mass in the context of estrogen-deficiency state.

Discussion

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

In this study, we have identified the distinct miRNA expression profiles and global miRNA/mRNA regulatory networks involved in estrogen deficiency–induced bone loss through integrative analysis using microarray in bone tissue of OVX mice. We found that these networks consist of several overrepresented biological processes, including mitochondrial OXPHOS, cellular homeostasis, response to stimuli, and regulation of apoptosis, which were mediated by PPARγ and CREB pathways. In addition, we were able to validate the role of miRNA-127 and -136, which were the most significantly increased after OVX, in the regulation of differentiation or function of osteoblast, osteocyte, and osteoclast in vitro. To our knowledge, this is the first study on the global expression profile and the regulatory network of miRNAs in the estrogen-deficiency model.

We have identified eight miRNAs (miR-127, -133a, -133a*, -133b, -136 -206, -378, -378*) whose expression is upregulated and 1 miRNA (miR-204), which was downregulated in the bone tissue of OVX mice, and these results were validated by quantitative real-time PCR. Of note, miR-133, -206, -378, and -204 have previously been reported to be involved in osteoblastic differentiation from their precursors. The expression of miR-133[15, 16] and miR-206[16, 34] was downregulated during BMP-2–induced osteoblastic differentiation of the mesenchymal C2C12 cells, and these miRNAs inhibit osteoblastic differentiation by directly attenuating Runx2 (miR-133),[15, 35] or by regulating other genes such as connexin 43 (miR-206).[16] miR-378 was also shown to inhibit osteoblastic differentiation by modulating nephronectin expression.[36] Given that both BMD and BMC have significantly decreased by OVX, the significantly increased expression of miR-133, -206, and -378 in this study is consistent with these previous studies. However, miR-204, which was also found to inhibit osteoblastic differentiation and reciprocally enhance adipogenesis by direct targeting Runx2,[17, 35] was significantly downregulated by OVX in this study. The reason for this discrepancy is not clear at present. It may be postulated that the decreased bone mass in the OVX state could lead to suppression of miRNA-204 expression as a compensation mechanism. Alternatively, given that the main source of miRNAs in this study should originate from osteocytes, which comprise approximately 90% of bone tissue in vivo, the miRNAs in mature osteocytes may play a different role compared with that in osteoblasts. Studies addressing the role of each differentially expressed miRNA in osteocytes and osteoblasts should be further elucidated.

Notably, we found that two miRNAs, miR-127 and miR-136, are upregulated by OVX. These miRNAs have not been implicated in the context of bone mass regulation previously. Both of them are located on mouse distal chromosome 12 (and conserved at human 14q32) and are imprinted: They are expressed exclusively from the maternal chromosome, and retroviral transposon-like gene on the complementary strand to miR-127 and miR-136 is expressed only from the paternal chromosome. This arrangement suggests that miR-127 and miR-136 could regulate the transposon by means of an RNAi pathway.[37, 38] However, the function of this distal 12 cluster of miRNAs, including miR-127 and miR-136, is unclear at present, and especially their roles in bone metabolism is yet to be investigated.

The computational prediction of miRNA targets currently presents several significant challenges because most of the tools (miRanda, TargetScan, PicTar, PITA, and RNAhybrid) are characterized by a significant proportion of false-positive interactions. On the basis of the hypothesis that miRNAs can act through target degradation, we performed target prediction analysis to select functional miRNA/mRNA relationships. We have demonstrated that 75 (11.4%) of 658 differentially expressed mRNAs after OVX were directly targeted by differentially expressed 9 miRNAs and constructed functional clusters of cellular homeostasis, response to hormone stimuli, calcium ion binding, apoptosis, mitochondria, and reproductive structure development. Interestingly, gene clusters of cellular homeostasis (SGK1, SLC8A1, FTL1, FTL2, SOCS6, ABCA2, and SH3BGRL3) and response to hormone stimuli (SGK1, ABCA2, PTPN1, EDEM1, and TCF3) are regulated by differentially expressed miRNAs, suggesting that compensatory response for deprivation of estrogen stimuli is coordinately regulated by the miRNA/mRNA network. Of note, the gene cluster of cell apoptosis (BAG4, BBC3, GRM7, PIM1, DCUN1D3, SGK1, and RYBP) is also regulated by these networks, and this result is supported by recent studies that demonstrated the role of miRNAs on programmed cell death.[39, 40] Indeed, miR-204 regulates apoptosis of human trabecular meshwork cell[39] and autophage during myocardial ischemia-reperfusion injury.[40] miR-206 is also involved in high glucose-mediated apoptosis in cardiomyocytes[41] and myocardial infarction.[42] In view of the fact that estrogen deficiency accelerates apoptosis of osteocytes and osteoblasts, which leads to bone loss,[43] our finding suggests that the miRNA/mRNA regulatory network could play a substantial role in this process.

We also identified that differentially expressed miRNAs after OVX regulate a set of mitochondria OXPHOS-related genes (LETM1, SLC25A25, BBC3, SLC25A22, 2310002L09RIK, AK3L1, MMAB, and PPTC7). It has been shown that estrogen receptor is expressed in mitochondria, and estrogen exerts direct and indirect effects on mitochondrial function in a variety of tissues,[44, 45] including mitochondrial biogenesis and respiration.[46] In this regard, our observation can be interpreted as a compensatory response for decreased estrogen stimuli, resulting in the regulation of mitochondrial OXPHOS-related genes after OVX.

Using the pathway analysis with GSEA, we also identified several differentially expressed gene clusters in the bone of OVX mice, which is in good agreement with the results from ORA: an increased expression of the mitochondria OXPHOS, stress response, and apoptosis–related genes and a decreased expression of the immune response–related genes. Of note, our model identified two major signaling pathways, PPARγ and CREB, that appear to be a hub linking these clusters of genes. Indeed, PPARγ has been known to play a critical role in adipocyte differentiation[47, 48] and also acts as a molecular switch between osteogenic and adipogenic lineage commitment.[49] We have demonstrated that overexpression of PPARγ in osteoblast using collagen type 1 promoter has reduced bone mass gain in male and attenuated bone loss after ovariectomy in female mice.[32] Moreover, PPARγ and CREB expression is increased in marrow stromal cells from aging individuals as well as those exposed to glucocorticoids.[50, 51] These results suggest that miRNAs' regulation of the PPARγ and CREB pathway may represent an important mechanism in the coordinated control of miRNA and mRNA expression in estrogen deficiency–induced bone loss.

Our computational analysis was further validated by in vitro studies on miR-127 and -136. Inhibitor of miR-127 or precursor of miR-136 has significantly enhanced or suppressed osteoblastic differentiation in vitro, respectively. Precursors of both miR-127 and -136 were shown to enhance osteoclastic differentiation of BMMs. Furthermore, transfection of precursors of both miR-127 and -136 aggravated cell death of MLO-Y4 osteocytes in the absence or presence of dexamethasone, whereas inhibition of these miRNAs enhanced osteocyte-like morphological changes. Collectively, both miR-127 and -136 regulate the differentiation and function of osteoblasts, osteocytes, and osteoclasts, rendering all parameters of the bone microenvironment in favor of losing bone mass.

One limitation of our study is that there was a considerable variation of miRNA expression in the study animals, whereas the mRNA expression levels were relatively consistent. However, we assume that the appropriate statistical method must have filtered the false identification of differentially expressed miRNAs. Identification of miR-133, -206, -378, and -204, which have previously been reported to be involved in osteoblastic differentiation, also supports the validity of this study. Furthermore, we have demonstrated the functional relevance of miR-127 and -136 through in vitro analysis of their inhibitor or precursors using osteoblast, osteocyte, and osteoclast cell models.

In conclusion, our study provides novel insights into the miRNA action in estrogen deficiency–induced osteoporosis. We obtained a comprehensive view of the coordinated regulatory network of miRNAs and their target genes in bone tissue per se by comparing miRNA and mRNA expression data and computational analyses. A small number of differentially expressed miRNAs targets a set of key regulatory genes that may eventually play a role in the pathogenesis of estrogen deficiency–induced osteoporosis. Based on this integrated approach, our data may make an important contribution to future investigations aimed at characterizing the role of specific miRNAs in the osteoporosis pathogenesis and therapy.

Acknowledgments

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

This study was supported by a grant from Seoul National University Hospital (grant number 03-2011-0070) and a grant from the Ministry of Health and Welfare of Korea (grant number A121445).

Authors' roles: Study design: JHA, JHO, CSS. Study conduct: JHA, JHO, JAS, JYY, HP, WYP. Data interpretation: JHA, JHO, HP, HJC, SWK, SYK, CSS. Drafting manuscript: JHA, CSS. Revising manuscript content: JHA, HP, CSS. Approving final version of manuscript: JHA, JHO, JAS, JYY, HP, HJC, SWK, SYK, WYP, CSS. CSS takes responsibility for the integrity of the data analysis.

References

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

Supporting Information

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

Additional Supporting Information may be found in the online version of this article.

FilenameFormatSizeDescription
jbmr2060-sm-0001-SupFig-S1.tif2702KSupplementary Figure S1.
jbmr2060-sm-0002-SupTabs-S1-S2.doc46KSupplementary Tables S1-S2.
jbmr2060-sm-0003-SupFigLegend-S1.doc38KSupplementary Figure Legend.

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