MicroRNA variants as genetic determinants of bone mass

Dole, Neha S; Delany, Anne M.

doi:10.1016/j.bone.2015.12.016

Cited by 30 publications

(28 citation statements)

References 142 publications

(180 reference statements)

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“…Hence, SNPs within or proximal to the loci of miRNA (miR‐SNPs) or mRNA (PolymiRTS) could potentially disrupt miRNA's targeting on mRNA. The potential effects of miR‐SNPs on skeletal traits have previously been discussed . Genetic variants in 7 miRNA families were reported to potentially affect skeletal traits, including miR‐27a, miR‐124, miR‐125, miR‐146a, miR‐149, miR‐196a2, and miR‐2861.…”

Section: Effects Of Mirna Polymorphisms On Skeletal Traitsmentioning

confidence: 99%

MicroRNA and Human Bone Health

Cheng

Tan

et al. 2018

JBMR Plus

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The small non‐coding microRNAs (miRNAs) are post‐transcription regulators that modulate diverse cellular process in bone cells. Because optimal miRNA targeting is essential for their function, single‐nucleotide polymorphisms (SNPs) within or proximal to the loci of miRNA (miR‐SNPs) or mRNA (PolymiRTS) could potentially disrupt the miRNA‐mRNA interaction, leading to changes in bone metabolism and osteoporosis. Recent human studies of skeletal traits using miRNA profiling, genomewide association studies, and functional studies started to decipher the complex miRNA regulatory network. These studies have indicated that miRNAs may be a promising bone marker. This review focuses on human miRNA studies on bone traits and discusses how genetic variants affect bone metabolic pathways. Major ex vivo investigations using human samples supported with animal and in vitro models have shed light on the mechanistic role of miRNAs. Furthermore, studying the miRNAs’ signatures in secondary osteoporosis and osteoporotic medications such as teriparatide (TPTD) and denosumab (DMab) have provided valuable insight into clinical management of the disease. © 2018 The Authors. JBMR Plus Published by Wiley Periodicals, Inc. on behalf of the American Society for Bone and Mineral Research

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Section: Effects Of Mirna Polymorphisms On Skeletal Traitsmentioning

confidence: 99%

MicroRNA and Human Bone Health

Cheng

Tan

et al. 2018

JBMR Plus

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“…As therapeutic targets, miRNAs are of priority due to not only its potent function but also the feasibility for drug delivery and targeting. 26 Previously, multiple miRNAs are found to be closely related to bone development and metabolism 27 and play an important role in promoting new bone regeneration, 28 such as miR218, miR27, miR204, and miR223. 20,[29][30][31] These miRNAs serve as regulators of mineral deposition through acting as drivers or inhibitors of bone-related signaling pathways.…”

Section: Mir204 Acts As An Inhibitor Of Osteogenesis In Dmmentioning

confidence: 99%

Delivery of antagomiR204-conjugated gold nanoparticles from PLGA sheets and its implication in promoting osseointegration of titanium implant in type 2 diabetes mellitus

Liu¹,

Tan²,

Zhou³

et al. 2017

IJN

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Impaired osseointegration of the implant remains the big hurdle for dental implant therapy in diabetic patients. In this study, the authors first identified that miR204 was strikingly highly expressed in the bone mesenchymal stem cells (BMSCs) of diabetic rats. Forced expression of miR204 repressed the osteogenic potential of BMSCs, while inhibition of miR204 significantly increased the osteogenic capacity. Moreover, the miR204 inhibitor was conjugated with gold nanoparticles (AuNP-antagomiR204) and dispersed them in the poly(lactic-co-glycolic acid) (PLGA) solution. The AuNP-antagomiR204 containing PLGA solution was applied for coating the surface of titanium implant. Electron microscope revealed that an ultrathin sheet was formed on the surface of the implant, and the AuNPs were evenly dispersed in the coated PLGA sheet. Cellular experiments revealed that these encapsulated AuNP-antagomiR204 were able to be released from the PLGA sheet and uptaken by adherent BMSCs. In vivo animal study further confirmed that the AuNP-antagomiR204 released from PLGA sheet promoted osseointegration, as revealed by microcomputerized tomography (microCT) reconstruction and histological assay. Taken together, this study established that miR204 misexpression accounted for the deficient osseointegation in diabetes mellitus, while PLGA sheets aided the release of AuNP-antagomiR204, which would be a promising strategy for titanium implant surface functionalization toward better osseointegration.

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“…From a mechanistic point of view, these sequence variations may have profound influence on the transcription of DNA sequences, the structure of RNA transcripts, and the interaction between RNA transcripts and other molecules. Consequently, these sequence variations may influence the biogenesis and function of miRNAs [21, 22]. Therefore, to characterize the subtle influences of RNA sequences on the biogenesis of miRNAs, high-accuracy predictor is required.…”

Section: Introductionmentioning

confidence: 99%

Integrated Strategy Improves the Prediction Accuracy of miRNA in Large Dataset

2016

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MiRNAs are short non-coding RNAs of about 22 nucleotides, which play critical roles in gene expression regulation. The biogenesis of miRNAs is largely determined by the sequence and structural features of their parental RNA molecules. Based on these features, multiple computational tools have been developed to predict if RNA transcripts contain miRNAs or not. Although being very successful, these predictors started to face multiple challenges in recent years. Many predictors were optimized using datasets of hundreds of miRNA samples. The sizes of these datasets are much smaller than the number of known miRNAs. Consequently, the prediction accuracy of these predictors in large dataset becomes unknown and needs to be re-tested. In addition, many predictors were optimized for either high sensitivity or high specificity. These optimization strategies may bring in serious limitations in applications. Moreover, to meet continuously raised expectations on these computational tools, improving the prediction accuracy becomes extremely important. In this study, a meta-predictor mirMeta was developed by integrating a set of non-linear transformations with meta-strategy. More specifically, the outputs of five individual predictors were first preprocessed using non-linear transformations, and then fed into an artificial neural network to make the meta-prediction. The prediction accuracy of meta-predictor was validated using both multi-fold cross-validation and independent dataset. The final accuracy of meta-predictor in newly-designed large dataset is improved by 7% to 93%. The meta-predictor is also proved to be less dependent on datasets, as well as has refined balance between sensitivity and specificity. This study has two folds of importance: First, it shows that the combination of non-linear transformations and artificial neural networks improves the prediction accuracy of individual predictors. Second, a new miRNA predictor with significantly improved prediction accuracy is developed for the community for identifying novel miRNAs and the complete set of miRNAs. Source code is available at: https://github.com/xueLab/mirMeta

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MicroRNA variants as genetic determinants of bone mass

Cited by 30 publications

References 142 publications

MicroRNA and Human Bone Health

MicroRNA and Human Bone Health

Delivery of antagomiR204-conjugated gold nanoparticles from PLGA sheets and its implication in promoting osseointegration of titanium implant in type 2 diabetes mellitus

Integrated Strategy Improves the Prediction Accuracy of miRNA in Large Dataset

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