Original Article
Five Freely Circulating miRNAs and Bone Tissue miRNAs are Associated with Osteoporotic Fractures† C Seeliger1*, K Karpinski1, AT Haug1, H Vester2, A Schmitt2, JS Bauer3, M van Griensven1 1
Dept. of Experimental Trauma Surgery, Klinikum rechts der Isar, Technical University Munich, Munich,
Germany 2
Dept. of Trauma Surgery, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
3
Dept. of Radiology, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
* Address correspondence and request for reprints to: Claudine Seeliger, PhD Department of Experimental Trauma Surgery, Klinikum rechts der Isar, Technical University Munich, Ismaninger Strasse 22, D-81675 Munich, Germany Tel: +49-89-4140-7527 Fax: +49-89-4140-7526 Email: [email protected]
†
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: [10.1002/jbmr.2175]
Additional Supporting Information may be found in the online version of this article.
Initial Date Submitted September 12, 2013; Date Revision Submitted December 22, 2013; Date Final Disposition Set January 9, 2014
Journal of Bone and Mineral Research © 2014 American Society for Bone and Mineral Research DOI 10.1002/jbmr.2175
Abstract Osteoporosis as a systemic skeletal disorder is characterized by increased bone fragility and the risk of fractures. According to the World Health Organization, osteoporosis is one of the 10 most common diseases and affects approximately 75 million people in Europe, the U.S. and Japan. In this context, the identification of specific microRNA (miRNA) signatures is an important step for new diagnostic and therapeutic approaches. The focus of interest on miRNAs as biomarkers came with new publications identifying free circulating extracellular miRNAs associated with various types of cancer. This study aimed to identify specific miRNAs in patients with osteoporotic fractures compared to non-osteoporotic fractures. For the array analysis, miRNAs were isolated from the serum of 20 patients with hip fractures, transcribed and the samples were pooled into 10 osteoporotic and 10 non-osteoporotic specimens. With each pool of samples, human serum & plasma miRNA PCR Arrays were performed, which are able to identify 83 different miRNAs. Subsequently, a separate validation analysis of each miRNA found to be regulated in the array followed with miRNA samples isolated from the serum of 30 osteoporotic and 30 non-osteoporotic patients and miRNA samples isolated from the bone tissue of 20 osteoporotic and 20 non-osteoporotic patients. With the validation analysis of the regulated miRNAs we identified 9 miRNAs, namely miR-21, miR-23a, miR24, miR-93, miR-100, miR-122a, miR-124a, miR-125b and miR-148a, which were significantly up-regulated in the serum of patients with osteoporosis. In the bone tissue of osteoporotic patients, we identified that miR-21, miR-23a, miR-24, miR-25, miR-100 and miR-125b displayed a significantly higher expression. A total of 5 miRNAs display an up-regulation both in serum and bone tissue. This study reveals an important role for several miRNAs in osteoporotic patients and suggested that they may be used as biomarkers for diagnostic purposes and may be a target for treating bone loss and optimizing fracture healing in osteoporotic patients.
KEY WORDS: miRNA, osteoporosis, fracture, bone healing, biomarkers
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Introduction Osteoporosis is a systemic skeletal disorder characterized by a reduction in bone mass and a change in the microstructure of bone tissue, which increases bone fragility and the risk of fractures (1,2). Skeletal fragility arises from inefficacy to produce the optimal bone mass, excessive bone resorption, and inadequate formation response during bone remodeling (1). An increasing number of studies indicate that osteoporosis is caused by extensive interactions among local and systemic regulators of bone cell function (3). In the view of the main cell types involved in bone homeostasis, Torricelli et al. showed, that human osteoblasts derived from osteoporotic patients have a lower degree of proliferation capacity but show no differences under differentiation conditions compared to cells from non-osteoporotic patients (4). Monocytes, the progenitors of osteoclasts, from osteoporotic patients on the other hand display an increased migration to the area of bone resorption (5). Also Gruber et al., who investigated osteoblast and osteoclast cell number and activity, detected a patientdependent osteoclast activity of the cells from postmenopausal osteoporotic patients (6). These findings underline that the bimolecular aetiopathogenetic mechanisms of osteoporosis are not sufficiently understood up to now. Therefore, the analysis of microRNA (miRNA) signatures will provide a better understanding of the regulating effects involved in the bone homeostasis in this disease. With the identification of specific miRNA signatures in osteoporosis, not only important cell-based information will be revealed, furthermore a possible biomarker could be established. Until now, the “gold standard” for measuring bone mineral density (BMD) is dual x-ray absorptiometry (DXA). However, there is a huge overlap in BMD in patients with and without insufficiency fractures (7). Minimum follow-up times to obtain knowledge on the disease’s progression have to be long due to small annual changes compared to the precision of the measurements (8). Serum markers would be more feasible in this respect. However, specific markers in osteoporotic patients are not well-established. Only a few biomarkers concerning bone turnover exist. For some diseases (mainly in the cancer and cardiac area), it is already known that the signatures of plasma/serum miRNAs can reflect associations to physiological or disease conditions (9). In this context, the present study focused on the analysis of specific miRNA signatures in order to identify potential diagnostic biomarkers, which in turn may represent new targets in optimizing fracture healing in osteoporotic patients. 3
Expression profiling of circulating miRNAs as biomarkers revealed both diagnostic and prognostic potential in 13 types of cancer so far (10). The focus of interest on miRNAs as biomarkers came with new publications on circulating extracellular miRNAs (11). miRNAs are non-coding, short RNA segments of approximately 22 nucleotides. They are involved in crucial cellular biological functions that include proliferation, differentiation and apoptosis (12). They modulate the gene expression of mRNA genes by formation of a RNA-induced silencing complex (RISC), which either leads to mRNA target cleavage or to mRNA degradation and/or translation repression (13), respectively. In many diseases, including many types of cancer, myocardial damage, endometriosis, gastrointestinal diseases or diabetes mellitus, typical signatures of local or systemically circulating miRNAs have already been described (11,14-18). Also, regulative miRNAs could be identified in skeletal disorders. For example, Wang et al. detected the up-regulation of miR-133a in circulating monocytes isolated from postmenopausal patients with low BMD due to unbalanced bone resorption by osteoclasts and bone formation by osteoblasts (19). Another study showed the important regulation of miR-483 in the pathogenesis of osteoarthritis (20). miRNAs also play an important role in the case of osteogenic differentiation in different cell types. E.g. miR-370 was recently identified as being relevant for viability and differentiation of murine osteoblasts (21-24). These studies indicate that miRNAs may play a role in the function, differentiation and/or development of bone and thus possibly also in the associated diseases. However, the role or possible involvement of circulating miRNA is not known for osteoporosis in human patients. As osteoporosis is a musculoskeletal disease with a high incidence of hip fractures, we investigated whether miRNAs are regulated in patients that suffered from a femoral neck or pertrochanteric fracture due to osteoporosis (Type of fracture: AO 31-A/B). Our findings may be used as biomarkers for diagnostic purposes and may be of great interest in developing innovative approaches concerning the treatment of bone loss and of osteoporotic fractures of elderly patients.
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Material and Methods Primary human samples The recruited patients were admitted to our clinic with hip fractures (Fracture types: AO 31-A/B). Subsequently, blood and - if available - bone tissue were collected during the implantation of a total endoprosthesis or a gamma nail in the proximal femur. This study was approved by the local ethical review committee of the Faculty of Medicine of the Technical University of Munich (project number 2413/09a). The study was performed according to the declaration of Helsinki in its newest version. The patients provided informed written consent. Patients were recruited if the following criteria were met: femoral neck or pertrochanteric fracture, indication of a surgical treatment and age over 50. Exclusion criteria were malignancy, benign ovarian cysts except endometrioma, inflammation, known chronic, systemic, metabolic, endocrine diseases including polycystic ovarian syndrome, hormone therapy in the previous 3 months and any medical history or signs of other inflammatory disease. The classification of the patients in the osteoporotic and the nonosteoporotic group was based on clinical, radiographic and DXA evaluation. Bone density was additionally evaluated via q-CT (Philips iCT, Best, Netherlands and Mindways calibration phantom and software, Austin, TX, USA) of the femoral head. The demographic data of the included patients are presented in Tables 1, 2 and 3 available in the supplemental material. Sample processing and miRNA extraction miRNA from serum was extracted using the miRNeasy Serum/Plasma Kit, according to the manufacturer’s recommendations (Qiagen, Hilden, Germany). For miRNA isolation of bone tissue, the extraction with TriReagent (Carl Roth, Karlsruhe, Germany) was performed after milling of the tissue (Retsch, Haan, Germany) using liquid nitrogen. RNA extraction was performed by chlorophorm phenol extraction as described before (25). The amount and purity of RNA was estimated by photometry (Eppendorf, Hamburg, Germany). Subsequently, miRNA was transcribed to cDNA using the miScript II RT Kit (Qiagen, Hilden, Germany) and one part of the RNA was transcribed to first-strand cDNA using the First Strand cDNA Synthesis Kit (Fermentas, St. Leon-Rot, Germany) for gene expression analysis in the bone tissue samples.
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miRNAs array analysis First of all, we profiled miRNA spectra from two pooled serum groups including a pool of 10 osteoporotic samples (7 female and 3 male patients) and a pool of 10 non-osteoporotic (10 female patients) samples in order to identify the regulated miRNAs. In total, 83 different miRNAs were profiled by the Human Serum & Plasma miRNA PCR Array MIHS-106Z (Qiagen). The expression level of the miRNAs from the array was determined by the cycle number via q-PCR, with their levels normalized to the average of the two small RNA molecules SNORD96a and RNU6 using the 2-∆∆CT method (2). Afterwards, the factor of change of the pool of osteoporotic patient samples compared to the pool of non-osteoporotic patient samples regarding the expression of specific miRNAs was calculated. miRNA validation analysis For the validation of the array results, the regulated miRNAs were analyzed in duplicates using the miScript SYBR Green PCR Kit with a CFX 96 Touch Real-Time PCR System (Bio-Rad, Munich, Germany) in 60 serum samples (osteoporotic patients group: all 7 samples from female patients of the array + 23 additionally collected samples from female patients; non-osteoporotic patients group: all 10 samples from female patients of the array + 20 additionally collected samples from female patients). Furthermore, we analyzed miR-93 and miR-637, which are known to be associated with bone development and were not included in the array. The relative expression level of each miRNA was determined by the cycle number via q-PCR, with their levels normalized to the average of two small RNA molecules SNORD96a and RNU6 using the 2-∆∆CT method (26). Of the 40 bone tissues samples (20 female osteoporotic patients, 11 male and 9 female non-osteoporotic patients) of osteoporotic and non-osteoporotic patients the in the array regulated miRNAs, as well as miR-93 and miR-637 were analyzed. We used Receiver Operating Characteristic (ROC) curves to determine the diagnostic potential of serum miRNAs, which were analyzed after logarithmic transformation of all samples included in the validation. Target generation and bioinformatic analysis of miRNA data To estimate possible targets for miRNA action that have been validated in our study, potential targets were predicted by using putative targets generated from TargetScan Human V5.1. This software program predicts putative targets based on factors that include sequences homology, predicted biological function and verified 6
targets. The predicted targets were ranked in order of conserved sequences and the prospect of regulation of gene expression via miRNA activity by using a context score. Previous studies have shown that a context score less than -0.3 is biologically relevant (27). Predicted gene targets were analyzed with Ingenuity Pathway Assessment (IPA) and the Ingenuity Knowledgebase (Version 8.7, Ingenuity Systems, Redwood City, CA). Quantitative real-time PCR of possibly involved genes EVA Green was used to analyze the miRNA influenced genes BMPR2 (bone morphogenetic protein receptor type II), c-Fos, OSX (osterix), PDCD4 (programmed cell death 4), RANKL (receptor activator of NF-κB Ligand), RUNX2 (runt-related transcription factor) and VCAN (versican) in the transcribed cDNA from the 40 bone tissue samples with a CFX 96 Touch Real-Time PCR System. The sequences of both the forward and reverse primers are listed in Table 4 available in the supplemental material. The relative expression levels of each gene were determined by the cycle number via q-PCR, with their levels normalized to the β-tubulin cycle number using the 2-∆∆CT method. Statistical analyses Results are given (after logarithmic transformation) as box plots showing 25th, 50th and 75th percentiles (horizontal bars), and minimum to maximum ranges (error bars). The band inside the box marks the median. Two-tailed Mann-Whitney U test was used to determine the significance of differential results from samples of the osteoporotic patients compared to the non-osteoporotic patient samples using GraphPad Prism version 5.01 (GraphPad Software, San Diego, USA). To determine the diagnostic utility of serum miRNAs, we used ROC curves, analyzed after logarithmic transformation of the data. The p-value tests the null hypothesis that the area under the curve equals 0.50. The cutoff points with the highest sensitivity and specificity were determined. P < 0.05 was taken as a minimum level of significance.
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Results Identification of differentially expressed miRNAs in the serum of osteoporotic patients using a miRNA array We initially profiled the miRNA spectra via the Human Serum & Plasma miRNA PCR Arrays from a pool of 10 miRNA samples from osteoporotic patients and from another pool of 10 miRNA samples from nonosteoporotic patients to identify regulated miRNAs in the serum. A total of 83 different miRNAs could be captured using the mentioned array (Table 1). Of the 83 screened miRNAs, 51 were detectable in the serum of the osteoporotic patients with hip fractures. Among the most abundant miRNAs in the serum from osteoporosis patients, 11 miRNAs, including miR-21-5p, miR-23-3p, miR-24-3p, miR-25-3p, miR-27a-3p, miR-100-5p, miR-122a-5p, miR-124-3p, miR-125b-5p, miR-148a-3p and miR-223-3p were significantly up-regulated in comparison to the non-osteoporotic patients’ samples. Interestingly, none of the studied miRNAs were downregulated in osteoporosis. MiR-122-5p was the most abundant miRNA in the pooled sample of osteoporotic patients with a 15.79-fold change in comparison to the pooled sample of the non-osteoporotic patients. Raw data of the array are shown in Table 5 available in the supplemental material.
Specific circulating miRNAs to identify osteoporosis To assess the potential of using miRNAs as biomarkers in osteoporosis, we chose the 11 up-regulated miRNAs of the array, plus in addition miR-93 and mir-637 for validation experiments. Here, we used q-PCR on an independent validation sample set, which consisted of serum samples from 30 osteoporotic patients and 30 nonosteoporotic patients. Among the selected miRNAs, nine could significantly distinguish osteoporosis samples from samples of non-osteoporotic patients: miR-21, miR-23a, miR-24, miR-93, miR-100, miR-122a, miR124a, miR-125b and miR-148a. The overall distribution of the levels of these miRNAs between samples of osteoporotic patients and samples of non-osteoporotic patients are shown in Figures 1 A–J.
Identification of miRNAs isolated from bone tissue of the osteoporotic patients From the analyzed regulated miRNAs, six could be identified bone tissue of osteoporotic patients, showing a significant up-regulation, including miR-21, miR-23a, miR-24, miR-25, miR-100 and miR-125b (Figures 2A8
J). No down-regulation was detectable among the miRNAs tested. MiR-23a and miR-100 were the most upregulated miRNA species in the osteoporosis tissue samples compared to the tissue from non-osteoporotic bone samples. Comparing to the results of the serum and bone analyses, an up-regulation of miR-21, miR-23a, miR-24, miR100 and miR-125b was detectable in both the serum and the bone tissue of osteoporotic patients. These results indicate that most of the miRNA deregulation observed in the serum of osteoporotic patients appears to be associated with miRNA deregulations observed in bone tissue of osteoporotic patients.
Assessing the diagnostic value In order to assess the potential diagnostic value of the significantly regulated miRNAs, a ROC curve analysis was performed after logarithmic transformation of the relative expression of the nine miRNAs identified in the patients’ serum. The associated area under the curve (AUC) was used to confirm the diagnostic value of each miRNA (Figures 3A-I). As shown in Figure 3F, the AUC of miR-122a was the highest, reaching 0.77 (95% confidence interval (CI) = 0.69–0.86, P < 0.0001. The AUC of the remaining eight miRNAs was 0.63 for miR21 (95% CI = 0.53–0.73, P = 0.013), 0.63 for miR-23a (95% CI = 0.53–0.73, P = 0.015), 0.63 for miR-24 (95% CI = 0.53–0.74, P = 0.013), 0.68 for miR-93 (95% CI = 0.58–0.78, P = 0.001) 0.69 for miR-100 (95% CI = 0.60–0.78, P = 0.0003), 0.69 for miR-124a (95% CI = 0.59–0.78, P = 0.0005), 0.76 for miR-125b (95% CI = 0.67–0.85, P < 0.0001) and 0.61 for miR-148a (95% CI = 0.51–0.72, P = 0.038). The sensitivity and specificity associated with the optimal cutoff points are shown in table 2. MiR-122a showed the highest sensitivity of 74.14% and a specificity of 72.14%.
Identification of related genes A search of the TargetScan Human V5.1 Database revealed several genes predicted as targets for each of the nine identified miRNAs in the serum from osteoporotic patients. The analysis of these genes via Ingenuity Pathway Assessment (IPA) shows different biological involvement of these miRNAs in cell fate (Figure 4). Furthermore, the possible impact of the miRNAs identified in this study with regard to the bone cell development as described in the literature is shown in Figure 5. Up to now, for the miRNAs miR-21, miR-23a, miR-24, miR-93, miR-100 and miR-148a an osteogenic or osteoclastic involvement is known. 9
Analysis of related genes triggered by the identified miRNA To evaluate the effect of the up-regulated miRNAs on genes involved in bone homeostasis genes, bone-specific mRNA transcripts in bone tissue samples of the patients were analyzed (Figure 6 A-G). Thereby, a downregulation of PDCD4 and an up-regulation of c-FOS, both influenced by miRNA-21, could be detected in samples from osteoporotic patients. The expression of RUNX2 - possibly affected by the miRNA-23a/miRNA24-2/miRNA-27a complex - was down-regulated in samples form osteoporotic patients compared to samples from non-osteoporotic patients. miRNA-93 repressing the gene expression of osterix was slightly downregulated in osteoporotic patients. The gene expression of BMPR2 was also down-regulated in samples of osteoporotic patients under the influence of miRNA-100. VCAN may be regulated by miRNA-124a showed a lower expression pattern in the samples from osteoporotic patients in comparison to samples from nonosteoporotic patients. Additionally, RANKL was also slightly up-regulated in samples from osteoporotic patients - possibly under the influence of miRNA-148a.
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Discussion An imbalance of the ratio of osteoblasts to osteoclasts causes alterations of bone mass and structure in osteoporosis. For a better understanding of the underlying pathogenetic mechanisms of osteoporosis-related hip fractures and for the improvement of diagnostic tools, we investigated a specific miRNA signature for osteoporosis. In many physiological and pathophysiological processes, miRNAs have been demonstrated to play crucial roles (28). For several types of solid tumors and leukemia as well as several other diseases such as Alzheimer’s disease and autism (29), coronary heart disease (30), B-cell leukemia (31), and type 2 diabetes (32), the presence of altered miRNA profiles in the plasma has been reported. This is the first report of altered serum miRNA signatures for osteoporosis in human patients. Because the analysis of serum markers is convenient in the clinical setting, changes in the levels of specific circulating miRNAs in the serum may represent a great potential as diagnostic predictors for this disease. The use of serum miRNA is associated with two advantages in the clinical diagnosis. Firstly, a serum-based biomarker would enable a relatively comprehensive analysis of the disease without requiring DXA and therefore reducing exposure to radiation. Secondly, a serum miRNAbased test is more cost-efficient and sample management is easy, including sample collection and processing. In this work, we investigated specific spectra of miRNAs in the sera of osteoporosis patients and found that specific circulating miRNAs could be used to detect cases of osteoporosis. Furthermore, five of them were also regulated locally in bone and may therefore be the best option for developing an innovative diagnostic tool for osteoporosis. Until now, only a few studies have indicated that specific miRNAs may be involved in osteoporosis (11,33). However, these studies focused on the identification of miRNAs in one cell type, such as blood mononuclear cells or mice mesenchymal stem cells. Also, a regulatory effect of certain miRNAs in osteoporotic animal models was described. For example He et al. could identify miR-10b, miR-22, miR-31 and miR-210 which were regulated during the development of osteoporosis in the bone tissue of mice (35). Our results did not reveal a regulative effect of these miRNAs in the serum of osteoporotic patients in comparison to the controls, due to the different species and age of the individuals. Nevertheless, we could demonstrate an up-regulation of different miRNAs in serum and bone tissue of human osteoporotic patients for the first time. 11
The functional analysis in silico showed that the identified miRNAs could target different genes involved in the organ system development and in basic biological reactions such as cell-cell signaling, DNA replication, cell death and survival. For all miRNAs identified in the serum, except for miR-124a and miR-122a, an influence in the context of osteoblast and osteoclast development has been described in the literature. For example, the induction of miR21 is known to inhibit PDCD4, which in turn inhibits the repression of c-Fos, thus allowing an increase in osteoclastogenesis (36). We could confirm these findings with our analysis of the expressions of these genes; this underlines the regulative effect of miR-21 on osteoclastogenesis in osteoporotic patients. Each miRNA of the miRNA cluster miR-23a-27a~24-2 has an inhibitory effect on the osteoblast differentiation via the down-regulation of RUNX2 (37). We could detect an up-regulation of miR-23 and miR-24 both in the serum and the bone tissue of osteoporotic patients. These up-regulations may lead to the down-regulation of RUNX2 gene expression, resulting in a lower amount of mature osteoblasts in osteoporotic patients, which could explain the well-known poorer microstructure of their bones. Additionally, miR-93 inhibits osteoblast differentiation by reducing the expression of the transcription factor OSX, which is one of the important regulators of osteoblast differentiation and bone mineralization (38). Our investigation in serum and bone tissue shows a strong up-regulation of miR-93 and the analysis of the OSX expression was down-regulated in the bone tissue samples of osteoporotic patients. Incisive changes in miRNA expression presumably cause an impaired mineralization in osteoporotic patients, which also suggests that serum testing of only one miRNA alone might not be sufficient to assess osteoporosis or the severity of this disease. The study by Zeng et al. on miR-100 showed its potential regulating effect on BMPR2 which is involved in the osteogenesis of human mesenchymal stem cells (39). MiR-100 exhibited an inhibitory effect on the osteogenic differentiation capacity of hMSCs. In our study, we could also detect a significant up-regulation of miR-100 as well as in serum and in bone tissue of osteoporotic patients. The analysis of BMPR2 in the bone tissue samples of osteoporotic patients reveals a down-regulation of this gene. Beside the higher osteoclastic activity, our results suggest that the osteoblastic differentiation capacity seems to be deregulated by the identified miRNAs in the osteoporotic patients. This may lead to the before-mentioned imbalance of bone resorption and bone formation. 12
MiR-124 was first described by Laine et al. They could show the down-regulation of aggrecan in cartilage development by this type of miRNA. This observation prompted us to investigate other bone-related proteoglycans in this study (40). As a matter of fact, we could detect a lower expression of VCAN in bone tissue samples from osteoporotic patients compared to non-osteoporotic patients. This may be associated with the inhibition of the early stages of the osteoid development in osteoporosis (41). MiR-125b, which was significantly up-regulated in the analyzed serum and bone tissue of osteoporotic patients, inhibits osteoblastic differentiation by down-regulating cell proliferation (42). The exact mechanism of the regulation effect of miR-125b is not known until now. Overexpression of miR-148a, slightly up-regulated in our analyzed serum and bone tissue samples of osteoporotic patients, is known to promote osteoclastogenesis in CD14+ PBMCs by targeting V-maf musculoaponeurotic fibrosarcoma oncogene homolog B (MAFB) that negatively regulates RANKL. Our analysis of RANKL gene expression in bone tissue revealed a slight up-regulation of this gene in osteoporotic patients, underlining the known deregulated osteoclastogenesis associated with osteoporosis (43). These findings raise interesting questions about the roles of the circulating miRNAs in view of osteoporosis and underline the need to understand the biological origins and functions of these circulating miRNAs in this context. Moreover, the contribution of the different up-regulated miRNAs identified in our experiments to the down-regulation of specific transcripts needs further investigation, which will be the subject of further experiments. Altogether, we could show that miRNAs are associated with the pathogenesis of osteoporosis. Our identified miRNAs showed significant sensitivity and specificity in distinguishing osteoporotic patients. However, it should be pointed out that more studies are necessary to further investigate the diagnostic value of the circulating miRNAs in a larger cohort of samples. One limitation of the study is, that the recruited samples of the bone tissue samples were from male and female patients; therefore we were not able to make a genderspecific analysis of the data. Nevertheless, our study clearly demonstrates that the miRNA profiling of patients with osteoporosis may play important roles in the detection, classification of osteoporotic diseases. In conclusion, this study profiled serum miRNAs in osteoporosis and identified nine miRNAs highly associated with osteoporosis in blood serum. In bone tissue six differentially expressed miRNAs were detectable. Five of these were simultaneously up-regulated in the serum samples. Given that osteoporosis is currently diagnosed 13
mainly by dual x-ray absorptiometry (DXA), a method that goes hand in hand with exposure to radiation and the minimum follow-up times to obtain knowledge on disease’s progression have to be long due to small annual changes compared to the precision of these measurement, the diagnosis of and intervention in the disease are often delayed due to the lack of symptoms and sensitive biomarkers in the early stages. Thus, our five identified, significantly up-regulated circulating miRNAs may provide novel biomarkers for osteoporosis and may play an important role in the pathogenesis of osteoporosis-related hip fractures. The findings of this study furthermore present innovative approaches in the development of pharmacological treatment of osteoporosis.
Competing interests The authors of this manuscript have no competing of interest to disclose as described by the “Journal of Bone and Mineral Research”.
Acknowledgements This work was partly funded by the Elsbeth Bonhoff foundation (funding number 94). We would also like to thank Fritz Seidl, M.A. translating and interpreting for proofreading this paper and Marina Unger for her technical support. The authors like to thank Servier for providing the graphics used for Figure 5. C.S and M.vG. designed the research. C.S and K.K. performed the experiments. H.V., A.S. and A.T.H. collected and prepared the patient samples. J.B.S. measured the bone density. H.V. classified the patients. M.vG., K.K. and A.T.H. assisted with data analysis and revised the manuscript. C.S. prepared the figures and wrote the manuscript. C.S. and M.vG. take responsibility for the integrity of the data analysis.
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Figure Legends Fig. 1. miRNAs are differently expressed in serum of osteoporotic patients. Box plots show miRNA expression levels in serum of female osteoporosis patients (N=30) compared to female non-osteoporosis patients (N=30). The expression of miRNA-21 (A), miRNA-23a (B), miRNA-24 (C), miRNA-25 (D), miRNA93 (E), miRNA-100 (F), miRNA-122a (G), miRNA-124a (H), miRNA-125b (I) and miRNA-148a (J) was upregulated in serum from osteoporotic patients. * p < 0.05, ** p< 0.01, *** p< 0.001, **** p< 0.0001 for the comparison indicated by Mann-Whitney U test. Box plots showing 25th, 50th and 75th percentiles (horizontal bars), and minimum to maximum ranges (error bars). Fig. 2. miRNAs are expressed differently on the intracellular level in osteoporotic patients. Box plots show miRNA expression levels in tissue of osteoporotic patients (N=20) compared to non-osteoporotic patients (N=20). The expressions of miRNA-21 (A), miRNA-23a (B), miRNA-24 (C), miRNA-25 (D), miRNA-93 (E), miRNA-100 (F), miRNA-122a (G), miRNA-124a (H), miRNA-125b (I) and miRNA-148a (J) were upregulated in bone tissue from osteoporotic patients. * p < 0.05, ** p< 0.01, *** p< 0.001 for the comparison indicated by Mann-Whitney U test. Box plots showing 25th, 50th and 75th percentiles (horizontal bars), and minimum to maximum ranges (error bars). Fig. 3. Diagnostic value of miRNAs for osteoporosis. AUC for serum miRNAs: miRNA-21 (A), miRNA-23a (B), miRNA-24 (C), miRNA-93 (D), miRNA-100 (E), miRNA-122a (F), miRNA-124a (G), miRNA-125b (H) and miRNA-148a (I). The relative expressions of each miRNA were log-transformed. AUC = area under the curve, CI = confidence interval Fig. 4. In silico functional analysis of the regulated miRNAs. Analyzed biological involvement of the triggered genes of miRNA-21, miRNA-23, miRNA-24, miRNA-96, miRNA-100, miRNA-122, miRNA-124, miRNA-125 and miRNA-148 revealed by TargetScan and Ingenuity Pathway Assessment. Fig. 5: The role of the regulated miRNAs in osteoblast and osteoclast development. Literature research could identify the influence on the cell development of osteoblasts and osteoclasts of the miRNAs miRNA-21, miRNA-23a, miR-24-2, miR-27a, miR- 93, miRNA-100, miRNA-125 and miRNA-148a. BMPR2 = bone morphogenetic protein receptor type II, MAFB = V-maf musculoaponeurotic fibrosarcoma oncogene homolog
17
B, OSX = osterix, PDCD4 = programmed cell death4, RANKL = receptor activator of NF-κB Ligand, RUNX2 = runt-related transcription factor 2. Fig. 6: miRNA influenced genes are deregulated in tissue samples of osteoporotic patients. Box plots showing gene expression levels in bone tissue of osteoporotic patients (N=20) compared to non-osteoporotic patients (N=20). Gene expression of PDCD4 (A) and c-Fos (B) triggered by miRNA-21, of RUNX2 (C) affected by the miRNA-23a/miRNA-24-2/miRNA-27a complex, gene expression of osterix triggered by miRNA-93 (D), miRNA- 100 triggering BMPR2 (E), VCAN may be regulated by miRNA-124a (F) and RANKL (G) influenced by miRNA-148a are shown. Box plots showing 25th, 50th and 75th percentiles (horizontal bars), and minimum to maximum ranges (error bars). BMPR2 = bone morphogenetic protein receptor type II, OSX = osterix, PDCD4 = programmed cell death4, RANKL = receptor activator of NF-κB Ligand, RUNX2 = runt-related transcription factor 2, VCAN = versican.
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Tables: Table 1: Regulated miRNAs in osteoporotic patients compared to non-osteoporotic patients using the human Serum & Plasma miRNA PCR Array. Two arrays were performed. For the first arrays, a pool of 10 isolated miRNA samples from ostoporotic patients and for the second array a pool of 10 isolated miRNA samples from non-ostoporotic patients were used. Afterwards, the factor of change between the pool of osteoporotic patient samples and the pool of non-osteoporotic patient samples regarding the expression of specific miRNAs was calculated. NS = gene’s average threshold cycle is either not determined or greater than the defined cut-off value in both samples meaning that its expression was undetected, making this factor of change erroneous and un-interpretable Fold Fold miRNA regulation change miRNA regulation change hsa-let-7a-5p hsa-miR-27a-3p up-regulated 4.60 NS hsa-miR-1 hsa-miR-296-5p undetermined undetermined hsa-miR-100-5p hsa-miR-29a-3p up-regulated 8.26 NS hsa-miR-106b-5p hsa-miR-30d-5p NS NS hsa-miR-10b-5p hsa-miR-34a-5p undetermined NS hsa-miR-122-5p hsa-miR-375 undetermined up-regulated 15.79 hsa-miR-124-3p hsa-miR-423-5p up-regulated 11.35 NS hsa-miR-125b-5p hsa-miR-499a-5p undetermined up-regulated 12.57 hsa-miR-126-3p hsa-miR-574-3p NS NS hsa-miR-133a undetermined hsa-miR-885-5p NS hsa-miR-133b undetermined hsa-miR-9-5p undetermined hsa-miR-134 undetermined hsa-miR-92a-3p NS hsa-miR-141-3p undetermined hsa-let-7c NS hsa-miR-143-3p NS hsa-miR-107 undetermined hsa-miR-146a-5p hsa-miR-10a-5p NS undetermined hsa-miR-150-5p hsa-miR-128 NS NS hsa-miR-155-5p hsa-miR-130b-3p undetermined NS hsa-miR-17-5p/106a-5p NS hsa-miR-145-5p NS hsa-miR-17-3p hsa-miR-148a-3p undetermined up-regulated 9.12 hsa-miR-18a-5p NS hsa-miR-15a-5p NS hsa-miR-192-5p NS hsa-miR-184 undetermined hsa-miR-195-5p NS hsa-miR-193a-5p NS hsa-miR-196a-5p undetermined hsa-miR-204-5p undetermined hsa-miR-19a-3p NS hsa-miR-206 undetermined hsa-miR-19b-3p hsa-miR-211-5p NS undetermined hsa-miR-200a-3p undetermined hsa-miR-26b-5p NS hsa-miR-200b-3p undetermined hsa-miR-30e-5p NS hsa-miR-200c-3p undetermined hsa-miR-372 undetermined hsa-miR-203a undetermined hsa-miR-373-3p undetermined hsa-miR-205-5p undetermined hsa-miR-374a-5p NS hsa-miR-208a undetermined hsa-miR-376c-3p undetermined hsa-miR-20a-5p hsa-miR-7-5p NS NS hsa-miR-21-5p hsa-miR-96-5p up-regulated 7.88 undetermined hsa-miR-210 hsa-miR-103a-3p NS NS hsa-miR-214-3p undetermined hsa-miR-15b-5p NS hsa-miR-215 undetermined hsa-miR-16-5p NS hsa-miR-221-3p hsa-miR-191-5p NS NS hsa-miR-222-3p hsa-miR-22-3p NS NS hsa-miR-223-3p hsa-miR-24-3p up-regulated 4.25 up-regulated 5.34 hsa-miR-224-5p hsa-miR-26a-5p NS NS hsa-miR-23a-3p hsa-miR-31-5p up-regulated 4.23 undetermined hsa-miR-25-3p up-regulated 4.25
19
Table 2: Sensitivity and specificity of the regulated miRNAs in the serum of osteoporotic and non-osteoporotic patients. Data were evaluated via a cut-off point.
miRNA miR-21 miR-23a miR-24 miR-96 miR-100 miR-122a miR-124a miR-125b miR-148a
Sensitivity (%) 61.29 57.38 60.34 68.97 62.90 74.14 61.40 76.36 62.50
Specificity (%) 61.67 56.67 60.35 68.34 61.67 72.14 61.02 75.00 62.30
20
Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Five Freely Circulating miRNAs and Bone Tissue miRNAs are Associated with Osteoporotic Fractures† C Seeliger1*, K Karpinski1, AT Haug1, H Vester2, A Schmitt2, JS Bauer3, M van Griensven1 1
Dept. of Experimental Trauma Surgery, Klinikum rechts der Isar, Technical University Munich, Munich,
Germany 2
Dept. of Trauma Surgery, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
3
Dept. of Radiology, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
* Address correspondence and request for reprints to: Claudine Seeliger, PhD Department of Experimental Trauma Surgery, Klinikum rechts der Isar, Technical University Munich, Ismaninger Strasse 22, D-81675 Munich, Germany Tel: +49-89-4140-7527 Fax: +49-89-4140-7526 Email: [email protected]
†
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: [10.1002/jbmr.2175]
Additional Supporting Information may be found in the online version of this article.
Initial Date Submitted September 12, 2013; Date Revision Submitted December 22, 2013; Date Final Disposition Set January 9, 2014
Journal of Bone and Mineral Research © 2014 American Society for Bone and Mineral Research DOI 10.1002/jbmr.2175
Abstract Osteoporosis as a systemic skeletal disorder is characterized by increased bone fragility and the risk of fractures. According to the World Health Organization, osteoporosis is one of the 10 most common diseases and affects approximately 75 million people in Europe, the U.S. and Japan. In this context, the identification of specific microRNA (miRNA) signatures is an important step for new diagnostic and therapeutic approaches. The focus of interest on miRNAs as biomarkers came with new publications identifying free circulating extracellular miRNAs associated with various types of cancer. This study aimed to identify specific miRNAs in patients with osteoporotic fractures compared to non-osteoporotic fractures. For the array analysis, miRNAs were isolated from the serum of 20 patients with hip fractures, transcribed and the samples were pooled into 10 osteoporotic and 10 non-osteoporotic specimens. With each pool of samples, human serum & plasma miRNA PCR Arrays were performed, which are able to identify 83 different miRNAs. Subsequently, a separate validation analysis of each miRNA found to be regulated in the array followed with miRNA samples isolated from the serum of 30 osteoporotic and 30 non-osteoporotic patients and miRNA samples isolated from the bone tissue of 20 osteoporotic and 20 non-osteoporotic patients. With the validation analysis of the regulated miRNAs we identified 9 miRNAs, namely miR-21, miR-23a, miR24, miR-93, miR-100, miR-122a, miR-124a, miR-125b and miR-148a, which were significantly up-regulated in the serum of patients with osteoporosis. In the bone tissue of osteoporotic patients, we identified that miR-21, miR-23a, miR-24, miR-25, miR-100 and miR-125b displayed a significantly higher expression. A total of 5 miRNAs display an up-regulation both in serum and bone tissue. This study reveals an important role for several miRNAs in osteoporotic patients and suggested that they may be used as biomarkers for diagnostic purposes and may be a target for treating bone loss and optimizing fracture healing in osteoporotic patients.
KEY WORDS: miRNA, osteoporosis, fracture, bone healing, biomarkers
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Introduction Osteoporosis is a systemic skeletal disorder characterized by a reduction in bone mass and a change in the microstructure of bone tissue, which increases bone fragility and the risk of fractures (1,2). Skeletal fragility arises from inefficacy to produce the optimal bone mass, excessive bone resorption, and inadequate formation response during bone remodeling (1). An increasing number of studies indicate that osteoporosis is caused by extensive interactions among local and systemic regulators of bone cell function (3). In the view of the main cell types involved in bone homeostasis, Torricelli et al. showed, that human osteoblasts derived from osteoporotic patients have a lower degree of proliferation capacity but show no differences under differentiation conditions compared to cells from non-osteoporotic patients (4). Monocytes, the progenitors of osteoclasts, from osteoporotic patients on the other hand display an increased migration to the area of bone resorption (5). Also Gruber et al., who investigated osteoblast and osteoclast cell number and activity, detected a patientdependent osteoclast activity of the cells from postmenopausal osteoporotic patients (6). These findings underline that the bimolecular aetiopathogenetic mechanisms of osteoporosis are not sufficiently understood up to now. Therefore, the analysis of microRNA (miRNA) signatures will provide a better understanding of the regulating effects involved in the bone homeostasis in this disease. With the identification of specific miRNA signatures in osteoporosis, not only important cell-based information will be revealed, furthermore a possible biomarker could be established. Until now, the “gold standard” for measuring bone mineral density (BMD) is dual x-ray absorptiometry (DXA). However, there is a huge overlap in BMD in patients with and without insufficiency fractures (7). Minimum follow-up times to obtain knowledge on the disease’s progression have to be long due to small annual changes compared to the precision of the measurements (8). Serum markers would be more feasible in this respect. However, specific markers in osteoporotic patients are not well-established. Only a few biomarkers concerning bone turnover exist. For some diseases (mainly in the cancer and cardiac area), it is already known that the signatures of plasma/serum miRNAs can reflect associations to physiological or disease conditions (9). In this context, the present study focused on the analysis of specific miRNA signatures in order to identify potential diagnostic biomarkers, which in turn may represent new targets in optimizing fracture healing in osteoporotic patients. 3
Expression profiling of circulating miRNAs as biomarkers revealed both diagnostic and prognostic potential in 13 types of cancer so far (10). The focus of interest on miRNAs as biomarkers came with new publications on circulating extracellular miRNAs (11). miRNAs are non-coding, short RNA segments of approximately 22 nucleotides. They are involved in crucial cellular biological functions that include proliferation, differentiation and apoptosis (12). They modulate the gene expression of mRNA genes by formation of a RNA-induced silencing complex (RISC), which either leads to mRNA target cleavage or to mRNA degradation and/or translation repression (13), respectively. In many diseases, including many types of cancer, myocardial damage, endometriosis, gastrointestinal diseases or diabetes mellitus, typical signatures of local or systemically circulating miRNAs have already been described (11,14-18). Also, regulative miRNAs could be identified in skeletal disorders. For example, Wang et al. detected the up-regulation of miR-133a in circulating monocytes isolated from postmenopausal patients with low BMD due to unbalanced bone resorption by osteoclasts and bone formation by osteoblasts (19). Another study showed the important regulation of miR-483 in the pathogenesis of osteoarthritis (20). miRNAs also play an important role in the case of osteogenic differentiation in different cell types. E.g. miR-370 was recently identified as being relevant for viability and differentiation of murine osteoblasts (21-24). These studies indicate that miRNAs may play a role in the function, differentiation and/or development of bone and thus possibly also in the associated diseases. However, the role or possible involvement of circulating miRNA is not known for osteoporosis in human patients. As osteoporosis is a musculoskeletal disease with a high incidence of hip fractures, we investigated whether miRNAs are regulated in patients that suffered from a femoral neck or pertrochanteric fracture due to osteoporosis (Type of fracture: AO 31-A/B). Our findings may be used as biomarkers for diagnostic purposes and may be of great interest in developing innovative approaches concerning the treatment of bone loss and of osteoporotic fractures of elderly patients.
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Material and Methods Primary human samples The recruited patients were admitted to our clinic with hip fractures (Fracture types: AO 31-A/B). Subsequently, blood and - if available - bone tissue were collected during the implantation of a total endoprosthesis or a gamma nail in the proximal femur. This study was approved by the local ethical review committee of the Faculty of Medicine of the Technical University of Munich (project number 2413/09a). The study was performed according to the declaration of Helsinki in its newest version. The patients provided informed written consent. Patients were recruited if the following criteria were met: femoral neck or pertrochanteric fracture, indication of a surgical treatment and age over 50. Exclusion criteria were malignancy, benign ovarian cysts except endometrioma, inflammation, known chronic, systemic, metabolic, endocrine diseases including polycystic ovarian syndrome, hormone therapy in the previous 3 months and any medical history or signs of other inflammatory disease. The classification of the patients in the osteoporotic and the nonosteoporotic group was based on clinical, radiographic and DXA evaluation. Bone density was additionally evaluated via q-CT (Philips iCT, Best, Netherlands and Mindways calibration phantom and software, Austin, TX, USA) of the femoral head. The demographic data of the included patients are presented in Tables 1, 2 and 3 available in the supplemental material. Sample processing and miRNA extraction miRNA from serum was extracted using the miRNeasy Serum/Plasma Kit, according to the manufacturer’s recommendations (Qiagen, Hilden, Germany). For miRNA isolation of bone tissue, the extraction with TriReagent (Carl Roth, Karlsruhe, Germany) was performed after milling of the tissue (Retsch, Haan, Germany) using liquid nitrogen. RNA extraction was performed by chlorophorm phenol extraction as described before (25). The amount and purity of RNA was estimated by photometry (Eppendorf, Hamburg, Germany). Subsequently, miRNA was transcribed to cDNA using the miScript II RT Kit (Qiagen, Hilden, Germany) and one part of the RNA was transcribed to first-strand cDNA using the First Strand cDNA Synthesis Kit (Fermentas, St. Leon-Rot, Germany) for gene expression analysis in the bone tissue samples.
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miRNAs array analysis First of all, we profiled miRNA spectra from two pooled serum groups including a pool of 10 osteoporotic samples (7 female and 3 male patients) and a pool of 10 non-osteoporotic (10 female patients) samples in order to identify the regulated miRNAs. In total, 83 different miRNAs were profiled by the Human Serum & Plasma miRNA PCR Array MIHS-106Z (Qiagen). The expression level of the miRNAs from the array was determined by the cycle number via q-PCR, with their levels normalized to the average of the two small RNA molecules SNORD96a and RNU6 using the 2-∆∆CT method (2). Afterwards, the factor of change of the pool of osteoporotic patient samples compared to the pool of non-osteoporotic patient samples regarding the expression of specific miRNAs was calculated. miRNA validation analysis For the validation of the array results, the regulated miRNAs were analyzed in duplicates using the miScript SYBR Green PCR Kit with a CFX 96 Touch Real-Time PCR System (Bio-Rad, Munich, Germany) in 60 serum samples (osteoporotic patients group: all 7 samples from female patients of the array + 23 additionally collected samples from female patients; non-osteoporotic patients group: all 10 samples from female patients of the array + 20 additionally collected samples from female patients). Furthermore, we analyzed miR-93 and miR-637, which are known to be associated with bone development and were not included in the array. The relative expression level of each miRNA was determined by the cycle number via q-PCR, with their levels normalized to the average of two small RNA molecules SNORD96a and RNU6 using the 2-∆∆CT method (26). Of the 40 bone tissues samples (20 female osteoporotic patients, 11 male and 9 female non-osteoporotic patients) of osteoporotic and non-osteoporotic patients the in the array regulated miRNAs, as well as miR-93 and miR-637 were analyzed. We used Receiver Operating Characteristic (ROC) curves to determine the diagnostic potential of serum miRNAs, which were analyzed after logarithmic transformation of all samples included in the validation. Target generation and bioinformatic analysis of miRNA data To estimate possible targets for miRNA action that have been validated in our study, potential targets were predicted by using putative targets generated from TargetScan Human V5.1. This software program predicts putative targets based on factors that include sequences homology, predicted biological function and verified 6
targets. The predicted targets were ranked in order of conserved sequences and the prospect of regulation of gene expression via miRNA activity by using a context score. Previous studies have shown that a context score less than -0.3 is biologically relevant (27). Predicted gene targets were analyzed with Ingenuity Pathway Assessment (IPA) and the Ingenuity Knowledgebase (Version 8.7, Ingenuity Systems, Redwood City, CA). Quantitative real-time PCR of possibly involved genes EVA Green was used to analyze the miRNA influenced genes BMPR2 (bone morphogenetic protein receptor type II), c-Fos, OSX (osterix), PDCD4 (programmed cell death 4), RANKL (receptor activator of NF-κB Ligand), RUNX2 (runt-related transcription factor) and VCAN (versican) in the transcribed cDNA from the 40 bone tissue samples with a CFX 96 Touch Real-Time PCR System. The sequences of both the forward and reverse primers are listed in Table 4 available in the supplemental material. The relative expression levels of each gene were determined by the cycle number via q-PCR, with their levels normalized to the β-tubulin cycle number using the 2-∆∆CT method. Statistical analyses Results are given (after logarithmic transformation) as box plots showing 25th, 50th and 75th percentiles (horizontal bars), and minimum to maximum ranges (error bars). The band inside the box marks the median. Two-tailed Mann-Whitney U test was used to determine the significance of differential results from samples of the osteoporotic patients compared to the non-osteoporotic patient samples using GraphPad Prism version 5.01 (GraphPad Software, San Diego, USA). To determine the diagnostic utility of serum miRNAs, we used ROC curves, analyzed after logarithmic transformation of the data. The p-value tests the null hypothesis that the area under the curve equals 0.50. The cutoff points with the highest sensitivity and specificity were determined. P < 0.05 was taken as a minimum level of significance.
7
Results Identification of differentially expressed miRNAs in the serum of osteoporotic patients using a miRNA array We initially profiled the miRNA spectra via the Human Serum & Plasma miRNA PCR Arrays from a pool of 10 miRNA samples from osteoporotic patients and from another pool of 10 miRNA samples from nonosteoporotic patients to identify regulated miRNAs in the serum. A total of 83 different miRNAs could be captured using the mentioned array (Table 1). Of the 83 screened miRNAs, 51 were detectable in the serum of the osteoporotic patients with hip fractures. Among the most abundant miRNAs in the serum from osteoporosis patients, 11 miRNAs, including miR-21-5p, miR-23-3p, miR-24-3p, miR-25-3p, miR-27a-3p, miR-100-5p, miR-122a-5p, miR-124-3p, miR-125b-5p, miR-148a-3p and miR-223-3p were significantly up-regulated in comparison to the non-osteoporotic patients’ samples. Interestingly, none of the studied miRNAs were downregulated in osteoporosis. MiR-122-5p was the most abundant miRNA in the pooled sample of osteoporotic patients with a 15.79-fold change in comparison to the pooled sample of the non-osteoporotic patients. Raw data of the array are shown in Table 5 available in the supplemental material.
Specific circulating miRNAs to identify osteoporosis To assess the potential of using miRNAs as biomarkers in osteoporosis, we chose the 11 up-regulated miRNAs of the array, plus in addition miR-93 and mir-637 for validation experiments. Here, we used q-PCR on an independent validation sample set, which consisted of serum samples from 30 osteoporotic patients and 30 nonosteoporotic patients. Among the selected miRNAs, nine could significantly distinguish osteoporosis samples from samples of non-osteoporotic patients: miR-21, miR-23a, miR-24, miR-93, miR-100, miR-122a, miR124a, miR-125b and miR-148a. The overall distribution of the levels of these miRNAs between samples of osteoporotic patients and samples of non-osteoporotic patients are shown in Figures 1 A–J.
Identification of miRNAs isolated from bone tissue of the osteoporotic patients From the analyzed regulated miRNAs, six could be identified bone tissue of osteoporotic patients, showing a significant up-regulation, including miR-21, miR-23a, miR-24, miR-25, miR-100 and miR-125b (Figures 2A8
J). No down-regulation was detectable among the miRNAs tested. MiR-23a and miR-100 were the most upregulated miRNA species in the osteoporosis tissue samples compared to the tissue from non-osteoporotic bone samples. Comparing to the results of the serum and bone analyses, an up-regulation of miR-21, miR-23a, miR-24, miR100 and miR-125b was detectable in both the serum and the bone tissue of osteoporotic patients. These results indicate that most of the miRNA deregulation observed in the serum of osteoporotic patients appears to be associated with miRNA deregulations observed in bone tissue of osteoporotic patients.
Assessing the diagnostic value In order to assess the potential diagnostic value of the significantly regulated miRNAs, a ROC curve analysis was performed after logarithmic transformation of the relative expression of the nine miRNAs identified in the patients’ serum. The associated area under the curve (AUC) was used to confirm the diagnostic value of each miRNA (Figures 3A-I). As shown in Figure 3F, the AUC of miR-122a was the highest, reaching 0.77 (95% confidence interval (CI) = 0.69–0.86, P < 0.0001. The AUC of the remaining eight miRNAs was 0.63 for miR21 (95% CI = 0.53–0.73, P = 0.013), 0.63 for miR-23a (95% CI = 0.53–0.73, P = 0.015), 0.63 for miR-24 (95% CI = 0.53–0.74, P = 0.013), 0.68 for miR-93 (95% CI = 0.58–0.78, P = 0.001) 0.69 for miR-100 (95% CI = 0.60–0.78, P = 0.0003), 0.69 for miR-124a (95% CI = 0.59–0.78, P = 0.0005), 0.76 for miR-125b (95% CI = 0.67–0.85, P < 0.0001) and 0.61 for miR-148a (95% CI = 0.51–0.72, P = 0.038). The sensitivity and specificity associated with the optimal cutoff points are shown in table 2. MiR-122a showed the highest sensitivity of 74.14% and a specificity of 72.14%.
Identification of related genes A search of the TargetScan Human V5.1 Database revealed several genes predicted as targets for each of the nine identified miRNAs in the serum from osteoporotic patients. The analysis of these genes via Ingenuity Pathway Assessment (IPA) shows different biological involvement of these miRNAs in cell fate (Figure 4). Furthermore, the possible impact of the miRNAs identified in this study with regard to the bone cell development as described in the literature is shown in Figure 5. Up to now, for the miRNAs miR-21, miR-23a, miR-24, miR-93, miR-100 and miR-148a an osteogenic or osteoclastic involvement is known. 9
Analysis of related genes triggered by the identified miRNA To evaluate the effect of the up-regulated miRNAs on genes involved in bone homeostasis genes, bone-specific mRNA transcripts in bone tissue samples of the patients were analyzed (Figure 6 A-G). Thereby, a downregulation of PDCD4 and an up-regulation of c-FOS, both influenced by miRNA-21, could be detected in samples from osteoporotic patients. The expression of RUNX2 - possibly affected by the miRNA-23a/miRNA24-2/miRNA-27a complex - was down-regulated in samples form osteoporotic patients compared to samples from non-osteoporotic patients. miRNA-93 repressing the gene expression of osterix was slightly downregulated in osteoporotic patients. The gene expression of BMPR2 was also down-regulated in samples of osteoporotic patients under the influence of miRNA-100. VCAN may be regulated by miRNA-124a showed a lower expression pattern in the samples from osteoporotic patients in comparison to samples from nonosteoporotic patients. Additionally, RANKL was also slightly up-regulated in samples from osteoporotic patients - possibly under the influence of miRNA-148a.
10
Discussion An imbalance of the ratio of osteoblasts to osteoclasts causes alterations of bone mass and structure in osteoporosis. For a better understanding of the underlying pathogenetic mechanisms of osteoporosis-related hip fractures and for the improvement of diagnostic tools, we investigated a specific miRNA signature for osteoporosis. In many physiological and pathophysiological processes, miRNAs have been demonstrated to play crucial roles (28). For several types of solid tumors and leukemia as well as several other diseases such as Alzheimer’s disease and autism (29), coronary heart disease (30), B-cell leukemia (31), and type 2 diabetes (32), the presence of altered miRNA profiles in the plasma has been reported. This is the first report of altered serum miRNA signatures for osteoporosis in human patients. Because the analysis of serum markers is convenient in the clinical setting, changes in the levels of specific circulating miRNAs in the serum may represent a great potential as diagnostic predictors for this disease. The use of serum miRNA is associated with two advantages in the clinical diagnosis. Firstly, a serum-based biomarker would enable a relatively comprehensive analysis of the disease without requiring DXA and therefore reducing exposure to radiation. Secondly, a serum miRNAbased test is more cost-efficient and sample management is easy, including sample collection and processing. In this work, we investigated specific spectra of miRNAs in the sera of osteoporosis patients and found that specific circulating miRNAs could be used to detect cases of osteoporosis. Furthermore, five of them were also regulated locally in bone and may therefore be the best option for developing an innovative diagnostic tool for osteoporosis. Until now, only a few studies have indicated that specific miRNAs may be involved in osteoporosis (11,33). However, these studies focused on the identification of miRNAs in one cell type, such as blood mononuclear cells or mice mesenchymal stem cells. Also, a regulatory effect of certain miRNAs in osteoporotic animal models was described. For example He et al. could identify miR-10b, miR-22, miR-31 and miR-210 which were regulated during the development of osteoporosis in the bone tissue of mice (35). Our results did not reveal a regulative effect of these miRNAs in the serum of osteoporotic patients in comparison to the controls, due to the different species and age of the individuals. Nevertheless, we could demonstrate an up-regulation of different miRNAs in serum and bone tissue of human osteoporotic patients for the first time. 11
The functional analysis in silico showed that the identified miRNAs could target different genes involved in the organ system development and in basic biological reactions such as cell-cell signaling, DNA replication, cell death and survival. For all miRNAs identified in the serum, except for miR-124a and miR-122a, an influence in the context of osteoblast and osteoclast development has been described in the literature. For example, the induction of miR21 is known to inhibit PDCD4, which in turn inhibits the repression of c-Fos, thus allowing an increase in osteoclastogenesis (36). We could confirm these findings with our analysis of the expressions of these genes; this underlines the regulative effect of miR-21 on osteoclastogenesis in osteoporotic patients. Each miRNA of the miRNA cluster miR-23a-27a~24-2 has an inhibitory effect on the osteoblast differentiation via the down-regulation of RUNX2 (37). We could detect an up-regulation of miR-23 and miR-24 both in the serum and the bone tissue of osteoporotic patients. These up-regulations may lead to the down-regulation of RUNX2 gene expression, resulting in a lower amount of mature osteoblasts in osteoporotic patients, which could explain the well-known poorer microstructure of their bones. Additionally, miR-93 inhibits osteoblast differentiation by reducing the expression of the transcription factor OSX, which is one of the important regulators of osteoblast differentiation and bone mineralization (38). Our investigation in serum and bone tissue shows a strong up-regulation of miR-93 and the analysis of the OSX expression was down-regulated in the bone tissue samples of osteoporotic patients. Incisive changes in miRNA expression presumably cause an impaired mineralization in osteoporotic patients, which also suggests that serum testing of only one miRNA alone might not be sufficient to assess osteoporosis or the severity of this disease. The study by Zeng et al. on miR-100 showed its potential regulating effect on BMPR2 which is involved in the osteogenesis of human mesenchymal stem cells (39). MiR-100 exhibited an inhibitory effect on the osteogenic differentiation capacity of hMSCs. In our study, we could also detect a significant up-regulation of miR-100 as well as in serum and in bone tissue of osteoporotic patients. The analysis of BMPR2 in the bone tissue samples of osteoporotic patients reveals a down-regulation of this gene. Beside the higher osteoclastic activity, our results suggest that the osteoblastic differentiation capacity seems to be deregulated by the identified miRNAs in the osteoporotic patients. This may lead to the before-mentioned imbalance of bone resorption and bone formation. 12
MiR-124 was first described by Laine et al. They could show the down-regulation of aggrecan in cartilage development by this type of miRNA. This observation prompted us to investigate other bone-related proteoglycans in this study (40). As a matter of fact, we could detect a lower expression of VCAN in bone tissue samples from osteoporotic patients compared to non-osteoporotic patients. This may be associated with the inhibition of the early stages of the osteoid development in osteoporosis (41). MiR-125b, which was significantly up-regulated in the analyzed serum and bone tissue of osteoporotic patients, inhibits osteoblastic differentiation by down-regulating cell proliferation (42). The exact mechanism of the regulation effect of miR-125b is not known until now. Overexpression of miR-148a, slightly up-regulated in our analyzed serum and bone tissue samples of osteoporotic patients, is known to promote osteoclastogenesis in CD14+ PBMCs by targeting V-maf musculoaponeurotic fibrosarcoma oncogene homolog B (MAFB) that negatively regulates RANKL. Our analysis of RANKL gene expression in bone tissue revealed a slight up-regulation of this gene in osteoporotic patients, underlining the known deregulated osteoclastogenesis associated with osteoporosis (43). These findings raise interesting questions about the roles of the circulating miRNAs in view of osteoporosis and underline the need to understand the biological origins and functions of these circulating miRNAs in this context. Moreover, the contribution of the different up-regulated miRNAs identified in our experiments to the down-regulation of specific transcripts needs further investigation, which will be the subject of further experiments. Altogether, we could show that miRNAs are associated with the pathogenesis of osteoporosis. Our identified miRNAs showed significant sensitivity and specificity in distinguishing osteoporotic patients. However, it should be pointed out that more studies are necessary to further investigate the diagnostic value of the circulating miRNAs in a larger cohort of samples. One limitation of the study is, that the recruited samples of the bone tissue samples were from male and female patients; therefore we were not able to make a genderspecific analysis of the data. Nevertheless, our study clearly demonstrates that the miRNA profiling of patients with osteoporosis may play important roles in the detection, classification of osteoporotic diseases. In conclusion, this study profiled serum miRNAs in osteoporosis and identified nine miRNAs highly associated with osteoporosis in blood serum. In bone tissue six differentially expressed miRNAs were detectable. Five of these were simultaneously up-regulated in the serum samples. Given that osteoporosis is currently diagnosed 13
mainly by dual x-ray absorptiometry (DXA), a method that goes hand in hand with exposure to radiation and the minimum follow-up times to obtain knowledge on disease’s progression have to be long due to small annual changes compared to the precision of these measurement, the diagnosis of and intervention in the disease are often delayed due to the lack of symptoms and sensitive biomarkers in the early stages. Thus, our five identified, significantly up-regulated circulating miRNAs may provide novel biomarkers for osteoporosis and may play an important role in the pathogenesis of osteoporosis-related hip fractures. The findings of this study furthermore present innovative approaches in the development of pharmacological treatment of osteoporosis.
Competing interests The authors of this manuscript have no competing of interest to disclose as described by the “Journal of Bone and Mineral Research”.
Acknowledgements This work was partly funded by the Elsbeth Bonhoff foundation (funding number 94). We would also like to thank Fritz Seidl, M.A. translating and interpreting for proofreading this paper and Marina Unger for her technical support. The authors like to thank Servier for providing the graphics used for Figure 5. C.S and M.vG. designed the research. C.S and K.K. performed the experiments. H.V., A.S. and A.T.H. collected and prepared the patient samples. J.B.S. measured the bone density. H.V. classified the patients. M.vG., K.K. and A.T.H. assisted with data analysis and revised the manuscript. C.S. prepared the figures and wrote the manuscript. C.S. and M.vG. take responsibility for the integrity of the data analysis.
14
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Figure Legends Fig. 1. miRNAs are differently expressed in serum of osteoporotic patients. Box plots show miRNA expression levels in serum of female osteoporosis patients (N=30) compared to female non-osteoporosis patients (N=30). The expression of miRNA-21 (A), miRNA-23a (B), miRNA-24 (C), miRNA-25 (D), miRNA93 (E), miRNA-100 (F), miRNA-122a (G), miRNA-124a (H), miRNA-125b (I) and miRNA-148a (J) was upregulated in serum from osteoporotic patients. * p < 0.05, ** p< 0.01, *** p< 0.001, **** p< 0.0001 for the comparison indicated by Mann-Whitney U test. Box plots showing 25th, 50th and 75th percentiles (horizontal bars), and minimum to maximum ranges (error bars). Fig. 2. miRNAs are expressed differently on the intracellular level in osteoporotic patients. Box plots show miRNA expression levels in tissue of osteoporotic patients (N=20) compared to non-osteoporotic patients (N=20). The expressions of miRNA-21 (A), miRNA-23a (B), miRNA-24 (C), miRNA-25 (D), miRNA-93 (E), miRNA-100 (F), miRNA-122a (G), miRNA-124a (H), miRNA-125b (I) and miRNA-148a (J) were upregulated in bone tissue from osteoporotic patients. * p < 0.05, ** p< 0.01, *** p< 0.001 for the comparison indicated by Mann-Whitney U test. Box plots showing 25th, 50th and 75th percentiles (horizontal bars), and minimum to maximum ranges (error bars). Fig. 3. Diagnostic value of miRNAs for osteoporosis. AUC for serum miRNAs: miRNA-21 (A), miRNA-23a (B), miRNA-24 (C), miRNA-93 (D), miRNA-100 (E), miRNA-122a (F), miRNA-124a (G), miRNA-125b (H) and miRNA-148a (I). The relative expressions of each miRNA were log-transformed. AUC = area under the curve, CI = confidence interval Fig. 4. In silico functional analysis of the regulated miRNAs. Analyzed biological involvement of the triggered genes of miRNA-21, miRNA-23, miRNA-24, miRNA-96, miRNA-100, miRNA-122, miRNA-124, miRNA-125 and miRNA-148 revealed by TargetScan and Ingenuity Pathway Assessment. Fig. 5: The role of the regulated miRNAs in osteoblast and osteoclast development. Literature research could identify the influence on the cell development of osteoblasts and osteoclasts of the miRNAs miRNA-21, miRNA-23a, miR-24-2, miR-27a, miR- 93, miRNA-100, miRNA-125 and miRNA-148a. BMPR2 = bone morphogenetic protein receptor type II, MAFB = V-maf musculoaponeurotic fibrosarcoma oncogene homolog
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B, OSX = osterix, PDCD4 = programmed cell death4, RANKL = receptor activator of NF-κB Ligand, RUNX2 = runt-related transcription factor 2. Fig. 6: miRNA influenced genes are deregulated in tissue samples of osteoporotic patients. Box plots showing gene expression levels in bone tissue of osteoporotic patients (N=20) compared to non-osteoporotic patients (N=20). Gene expression of PDCD4 (A) and c-Fos (B) triggered by miRNA-21, of RUNX2 (C) affected by the miRNA-23a/miRNA-24-2/miRNA-27a complex, gene expression of osterix triggered by miRNA-93 (D), miRNA- 100 triggering BMPR2 (E), VCAN may be regulated by miRNA-124a (F) and RANKL (G) influenced by miRNA-148a are shown. Box plots showing 25th, 50th and 75th percentiles (horizontal bars), and minimum to maximum ranges (error bars). BMPR2 = bone morphogenetic protein receptor type II, OSX = osterix, PDCD4 = programmed cell death4, RANKL = receptor activator of NF-κB Ligand, RUNX2 = runt-related transcription factor 2, VCAN = versican.
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Tables: Table 1: Regulated miRNAs in osteoporotic patients compared to non-osteoporotic patients using the human Serum & Plasma miRNA PCR Array. Two arrays were performed. For the first arrays, a pool of 10 isolated miRNA samples from ostoporotic patients and for the second array a pool of 10 isolated miRNA samples from non-ostoporotic patients were used. Afterwards, the factor of change between the pool of osteoporotic patient samples and the pool of non-osteoporotic patient samples regarding the expression of specific miRNAs was calculated. NS = gene’s average threshold cycle is either not determined or greater than the defined cut-off value in both samples meaning that its expression was undetected, making this factor of change erroneous and un-interpretable Fold Fold miRNA regulation change miRNA regulation change hsa-let-7a-5p hsa-miR-27a-3p up-regulated 4.60 NS hsa-miR-1 hsa-miR-296-5p undetermined undetermined hsa-miR-100-5p hsa-miR-29a-3p up-regulated 8.26 NS hsa-miR-106b-5p hsa-miR-30d-5p NS NS hsa-miR-10b-5p hsa-miR-34a-5p undetermined NS hsa-miR-122-5p hsa-miR-375 undetermined up-regulated 15.79 hsa-miR-124-3p hsa-miR-423-5p up-regulated 11.35 NS hsa-miR-125b-5p hsa-miR-499a-5p undetermined up-regulated 12.57 hsa-miR-126-3p hsa-miR-574-3p NS NS hsa-miR-133a undetermined hsa-miR-885-5p NS hsa-miR-133b undetermined hsa-miR-9-5p undetermined hsa-miR-134 undetermined hsa-miR-92a-3p NS hsa-miR-141-3p undetermined hsa-let-7c NS hsa-miR-143-3p NS hsa-miR-107 undetermined hsa-miR-146a-5p hsa-miR-10a-5p NS undetermined hsa-miR-150-5p hsa-miR-128 NS NS hsa-miR-155-5p hsa-miR-130b-3p undetermined NS hsa-miR-17-5p/106a-5p NS hsa-miR-145-5p NS hsa-miR-17-3p hsa-miR-148a-3p undetermined up-regulated 9.12 hsa-miR-18a-5p NS hsa-miR-15a-5p NS hsa-miR-192-5p NS hsa-miR-184 undetermined hsa-miR-195-5p NS hsa-miR-193a-5p NS hsa-miR-196a-5p undetermined hsa-miR-204-5p undetermined hsa-miR-19a-3p NS hsa-miR-206 undetermined hsa-miR-19b-3p hsa-miR-211-5p NS undetermined hsa-miR-200a-3p undetermined hsa-miR-26b-5p NS hsa-miR-200b-3p undetermined hsa-miR-30e-5p NS hsa-miR-200c-3p undetermined hsa-miR-372 undetermined hsa-miR-203a undetermined hsa-miR-373-3p undetermined hsa-miR-205-5p undetermined hsa-miR-374a-5p NS hsa-miR-208a undetermined hsa-miR-376c-3p undetermined hsa-miR-20a-5p hsa-miR-7-5p NS NS hsa-miR-21-5p hsa-miR-96-5p up-regulated 7.88 undetermined hsa-miR-210 hsa-miR-103a-3p NS NS hsa-miR-214-3p undetermined hsa-miR-15b-5p NS hsa-miR-215 undetermined hsa-miR-16-5p NS hsa-miR-221-3p hsa-miR-191-5p NS NS hsa-miR-222-3p hsa-miR-22-3p NS NS hsa-miR-223-3p hsa-miR-24-3p up-regulated 4.25 up-regulated 5.34 hsa-miR-224-5p hsa-miR-26a-5p NS NS hsa-miR-23a-3p hsa-miR-31-5p up-regulated 4.23 undetermined hsa-miR-25-3p up-regulated 4.25
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Table 2: Sensitivity and specificity of the regulated miRNAs in the serum of osteoporotic and non-osteoporotic patients. Data were evaluated via a cut-off point.
miRNA miR-21 miR-23a miR-24 miR-96 miR-100 miR-122a miR-124a miR-125b miR-148a
Sensitivity (%) 61.29 57.38 60.34 68.97 62.90 74.14 61.40 76.36 62.50
Specificity (%) 61.67 56.67 60.35 68.34 61.67 72.14 61.02 75.00 62.30
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