No abstract

MicroRNAs (miRNAs) regulate activities in living organisms through various signaling pathways and play important roles in the development and progression of osteoporosis.The balance between osteogenic and adipogenic differentiation of rBMSCs is closely related to the occurrence of osteoporosis. ERα regulates bone metabolism in various tissues. However, the correlation among ERα, miRNAs, and the differentiation of rBMSCs is still unclear. In this study, we used lentivirus transfection into rBMSCs to construct an ERα-deficient model, analyzed the differences in expressed miRNAs between control and ERα-deficient rBMSCs. The results revealed that the expression of 25 miRNAs were upregulated, 164 miRNAs were downregulated, and some of the regulated miRNAs such as miR-210-3p and miR-214-3p were related to osteogenic or adipogenic differentiation, as well as to particular signaling pathways. Next, we overexpressed miR-210-3p to evaluate its effects on the osteogenic and adipogenic differentiation of rBMSCs, and identified the relationship among miR-210-3p, Wnt signaling pathway, and the differentiation of rBMSCs. The results indicated that ERα-deficient inhibited osteogenic differentiation, promoted adipogenic differentiation, and regulated the expression of some miRNAs. Meanwhile, overexpression of miR-210-3p promoted osteogenic differentiation and inhibited adipogenic differentiation of rBMSCs, processes likely to be related to the Wnt signaling pathway. In conclusion, we identified a group of upregulated and downregulated miRNAs in ERα-deficient rBMSCs that might play a vital role in regulating osteogenic or adipogenic differentiation. One of these, miR-210-3p, inhibited osteogenic differentiation and promoted adipogenic differentiation correlated with the Wnt signaling pathway in ERα-deficient rBMSCs, providing new insight into the regulation of bone metabolism.

Icariin (ICA) is a major active ingredient in Herba epimedii, which is commonly used as a Chinese herbal medicine for the treatment of osteoporosis. Previous studies have revealed that ICA exerted a protective effect against bone loss and increased bone regeneration; however, the association between ICA and estrogen receptor (ER) signaling remains unclear. The aim of the present study was to determine the effect of ICA on rat bone marrow stromal cells (rBMSCs). Cell Counting Kit‑8 assays were conducted to measure proliferation, alkaline phosphatase (ALP) activity was evaluated to assess osteoblast differentiation, and reverse transcription‑quantitative polymerase chain reaction as well as western blotting were performed to detect the expression of cellular and molecular markers of osteogenic or adipogenic differentiation. The results demonstrated that treatment of rBMSCs with 10‑6 M ICA stimulated rBMSC proliferation and ALP activity. Furthermore, ICA treatment increased the expression of the osteogenic markers runt‑related transcription factor 2, collagen type 1 and bone morphogenetic Protein 2; however, it also decreased the expression of the adipogenic differentiation markers peroxisome proliferator‑activated receptor gamma and CCAAT/enhancer‑binding protein α. Treatment of rBMSCs with ICI182780, an ER antagonist, blocked the effects of ICA. Taken together, these findings indicated that ICA may stimulate osteoblast differentiation and inhibit adipogenic differentiation via the activation of the ER signaling pathway. Therefore, ICA has the potential to serve as a therapeutic alternative for the prevention and treatment of osteoporosis.

No abstract

Investigating the proteome can add a significant layer of information to manifold existing methylation, mutation, and transcriptome data on brain tumors as proteins represent the pharmacologically addressable phenotype of a disease. Small cohorts limit the usability and validity of statistical methods, and variable technical setups and high numbers of missing values make data integration from public sources challenging. Using a newly developed framework being able to reduce batch effects without the need for data reduction or missing value imputation, we show –based on in-house and publicly available datasets- successful integration of proteomic data across different tissue types, quantification platforms, and technical setups. Exemplarily, data of a Sonic hedgehog (Shh) medulloblastoma mouse model were analyzed, showing efficient data integration independent of tissue preservation strategy or batch. We further integrated batches of publicly available data of human brain tumors, confirming proposed proteomic cancer subtypes correlating with clinical features. We show that, missing value tolerant reduction of technical variances may be helpful to identify biomarkers, proteomic signatures, and altered pathways characteristic for molecular brain cancer subtypes.