Nanostructural materials increase mineralization in bone cells and affect gene expression through miRNA regulation.
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ABSTRACT:
SUBMITTER: Mahmood M
PROVIDER: S-EPMC3822941 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Bone loss occurs as a consequence of a variety of diseases as well as from traumatic injuries, and often requires therapeutic intervention. Strategies for repairing and replacing damaged and/or lost bone tissue include the use of biomaterials and medical implant devices with and without osteoinductive coatings. The soluble growth factor angiopoietin-1 (Ang-1) has been found to promote cell adhesion and survival in a range of cell types including cardiac myocytes, endothelial cells and fibroblasts through an integrin-dependent mechanism. Furthermore, the short sequence QHREDGS has been identified as the integrin-binding sequence of Ang-1 and as a synthetic peptide has been found to possess similar integrin-dependent effects as Ang-1 in the aforementioned cell types. Integrins have been implicated in osteoblast differentiation and bone mineralization, processes critical to bone regeneration. By binding integrins on the osteoblast surface, QHREDGS could promote cell survival and adhesion, as well as conceivably osteoblast differentiation and bone mineralization. Here we immobilized QHREDGS onto polyacrylate (PA)-coated titanium (Ti) plates and polyethylene glycol (PEG) hydrogels. The osteoblast differentiation marker, alkaline phosphatase, peaked in activity 4-12 days earlier on the QHREDGS-immobilized PA-coated Ti plates than on the unimmobilized, DGQESHR (scrambled)- and RGDS-immobilized surfaces. Significantly more bone matrix was deposited on the QHREDGS-immobilized Ti surface than on the other surfaces as determined by atomic force microscopy. The QHREDGS-immobilized hydrogels also had a significantly higher mineral-to-matrix (M/M) ratio determined by Fourier transform infrared spectroscopy. Alizarin Red S and von Kossa staining and quantification, and environmental scanning electron microscopy showed that while both the QHREDGS- and RGDS-immobilized surfaces had extensive mineralization relative to the unimmobilized and DGQESHR-immobilized surfaces, the mineralization was more considerable on the QHREDGS-immobilized surface, both with and without the induction of osteoblast differentiation. Finally, treatment of cell monolayers with soluble QHREDGS was demonstrated to upregulate osteogenic gene expression. Taken together, these results demonstrate that the QHREDGS peptide is osteoinductive, inducing osteoblast differentiation, bone matrix deposition and mineralization.
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Project description:Gastrectomy (GX) is thought to result in osteomalacia due to deficiencies in Vitamin D and Ca. Using a GX rat model, we showed that GX induced high turnover of bone with hyperosteoidosis, prominent increase of mineralization and increased mRNA expression of both osteoclast-derived tartrate-resistant acid phosphatase 5b and osteocalcin. The increased 1, 25(OH)2D3 level and unchanged PTH and calcitonin levels suggested that conventional bone and Ca metabolic pathways were not involved or changed in compensation. Thus, GX-induced bone pathology was different from a typical osteomalacia. Gene expression profiles through microarray analysis and data mining using Ingenuity Pathway Analysis indicated that 612 genes were up-regulated and 1,097 genes were down-regulated in the GX bone. These genes were related functionally to connective tissue development, skeletal and muscular system development and function, Ca signaling and the role of osteoblasts, osteoclasts and chondrocytes. Network analysis indicated 9 genes (Aldehyde dehydrogenase 1 family, member A1; Aquaporin 9; Interleukin 1 receptor accessory protein; Very low density lipoprotein receptor; Periostin, osteoblast specific factor; Aggrecan; Gremlin 1; Angiopoietin-like 4; Wingless-type MMTV integration site family, member 10B) were hubs connected with tissue development and immunological diseases. These results suggest that chronic systemic inflammation might underlie the GX-induced pathological changes in bone.
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Project description:BackgroundCurrent microRNA (miRNA) research in progress has engendered rapid accumulation of expression data evolving from microarray experiments. Such experiments are generally performed over different tissues belonging to a specific species of metazoan. For disease diagnosis, microarray probes are also prepared with tissues taken from similar organs of different candidates of an organism. Expression data of miRNAs are frequently mapped to co-expression networks to study the functions of miRNAs, their regulation on genes and to explore the complex regulatory network that might exist between Transcription Factors (TFs), genes and miRNAs. These directions of research relating miRNAs are still not fully explored, and therefore, construction of reliable and compatible methods for mining miRNA co-expression networks has become an emerging area. This paper introduces a novel method for mining the miRNA co-expression networks in order to obtain co-expressed miRNAs under the hypothesis that these might be regulated by common TFs.ResultsThree co-expression networks, configured from one patient-specific, one tissue-specific and a stem cell-based miRNA expression data, are studied for analyzing the proposed methodology. A novel compactness measure is introduced. The results establish the statistical significance of the sets of miRNAs evolved and the efficacy of the self-pruning phase employed by the proposed method. All these datasets yield similar network patterns and produce coherent groups of miRNAs. The existence of common TFs, regulating these groups of miRNAs, is empirically tested. The results found are very promising. A novel visual validation method is also proposed that reflects the homogeneity as well as statistical properties of the grouped miRNAs. This visual validation method provides a promising and statistically significant graphical tool for expression analysis.ConclusionA heuristic mining methodology that resembles a clustering motivation is proposed in this paper. However, there remains a basic difference between the mining method and a clustering approach. The heuristic approach can produce priority modules (PM) from an miRNA co-expression network, by employing a self-pruning phase, which are analyzed for statistical and biological significance. The mining algorithm minimizes the space/time complexity of the analysis, and also handles noise in the data. In addition, the mining method reveals promising results in the unsupervised analysis of TF-miRNA regulation.
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Project description:Defective bone mineralization has serious clinical manifestations, including deformities and fractures, but the regulation of this extracellular process is not fully understood. We have developed a mathematical model consisting of ordinary differential equations that describe collagen maturation, production and degradation of inhibitors, and mineral nucleation and growth. We examined the roles of individual processes in generating normal and abnormal mineralization patterns characterized using two outcome measures: mineralization lag time and degree of mineralization. Model parameters describing the formation of hydroxyapatite mineral on the nucleating centers most potently affected the degree of mineralization, while the parameters describing inhibitor homeostasis most effectively changed the mineralization lag time. Of interest, a parameter describing the rate of matrix maturation emerged as being capable of counter-intuitively increasing both the mineralization lag time and the degree of mineralization. We validated the accuracy of model predictions using known diseases of bone mineralization such as osteogenesis imperfecta and X-linked hypophosphatemia. The model successfully describes the highly nonlinear mineralization dynamics, which includes an initial lag phase when osteoid is present but no mineralization is evident, then fast primary mineralization, followed by secondary mineralization characterized by a continuous slow increase in bone mineral content. The developed model can potentially predict the function for a mutated protein based on the histology of pathologic bone samples from mineralization disorders of unknown etiology.
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