Mutation in Osteoactivin Decreases Bone Formation in Vivo and Osteoblast Differentiation in Vitro

We have previously identified osteoactivin (OA), encoded by Gpnmb, as an osteogenic factor that stimulates osteoblast differentiation in vitro. To elucidate the importance of OA in osteogenesis, we characterized the skeletal phenotype of a mouse model, DBA/2J (D2J) with a loss-of-function mutation in Gpnmb. Microtomography of D2J mice showed decreased trabecular mass, compared to that in wild-type mice [DBA/2J-Gpnmb⁺/SjJ (D2J/Gpnmb⁺)]. Serum analysis showed decreases in OA and the bone-formation markers alkaline phosphatase and osteocalcin in D2J mice. Although D2J mice showed decreased osteoid and mineralization surfaces, their osteoblasts were increased in number, compared to D2J/Gpnmb⁺ mice. We then examined the ability of D2J osteoblasts to differentiate in culture, where their differentiation and function were decreased, as evidenced by low alkaline phosphatase activity and matrix mineralization. Quantitative RT-PCR analyses confirmed the decreased expression of differentiation markers in D2J osteoblasts. In vitro, D2J osteoblasts proliferated and survived significantly less, compared to D2J/Gpnmb⁺ osteoblasts. Next, we investigated whether mutant OA protein induces endoplasmic reticulum stress in D2J osteoblasts. Neither endoplasmic reticulum stress markers nor endoplasmic reticulum ultrastructure were altered in D2J osteoblasts. Finally, we assessed underlying mechanisms that might alter proliferation of D2J osteoblasts. Interestingly, TGF-β receptors and Smad-2/3 phosphorylation were up-regulated in D2J osteoblasts, suggesting that OA contributes to TGF-β signaling. These data confirm the anabolic role of OA in postnatal bone formation.Osteoporosis is a growing public health problem, in part because of the increasing numbers of people living beyond the age of 65 years.¹ It is characterized by low bone mass due to increased bone resorption by osteoclasts and decreased bone formation by osteoblasts, with significant deterioration in the bone microarchitecture leading to high bone fragility and increased fracture risk.^1,2 The net effect of osteoporosis is low bone mass.¹ There is an increasing demand for identifying novel bone anabolic factors with potential therapeutic benefits in treating generalized bone loss, such as osteoporosis and/or major skeletal fracture.Osteoactivin is a novel glycoprotein first identified in natural mutant osteopetrotic rats.³ The same protein has been identified and named separately in several other species: as dendritic cell heparan sulfate proteoglycan integrin dependent ligand (DCHIL) in mouse dendritic cells,⁴ as transmembrane glycoprotein NMB (GPNMB) in human melanoma cell lines and melanocytes,⁵ and as hematopoietic growth factor inducible neurokinin (HGFIN) in human tumor cells.⁶ The current recommended name for the protein encoded by Gpnmb in mouse is transmembrane glycoprotein NMB (http://www.ncbi.nlm.nih.gov/protein/Q99P91.2); here, we continue to use osteoactivin (OA) for the protein and Gpnmb for the gene. OA is a type I transmembrane protein that consists of multiple domains, including an extracellular domain, transmembrane domain, and protein sorting signal sequence.⁷ Within the C-terminal domain, OA has an RGD motif, predicting an integrin attachment site.^3,7–9Our research group initially reported on the novel role of OA in osteoblast differentiation and function.^7–10 We demonstrated that OA expression has a temporal pattern during osteoblast differentiation, being highest during matrix maturation and culture mineralization in vitro.^7–11 Using loss-of–function and gain-of–function approaches in osteoblasts, we reported that OA overexpression increases osteoblast differentiation and function and that OA down-regulation decreases nodule formation, alkaline phosphatase (ALP) activity, osteocalcin (OC) production, and matrix mineralization in vitro.⁷ We also reported on the positive role of OA in mesenchymal stem cell (MSCs) differentiation into osteoblasts in vitro.¹² In another study, we showed that recombinant OA protein induces higher osteogenic potential of fetal-derived MSCs, compared with bone marrow–derived MSCs¹³ and its osteogenic effects in the mouse C3H10T1/2 MSC cell line were similar to those of recombinant BMP-2.¹² We also localized OA protein as associated predominately with osteoblasts lining trabecular bones in vivo,¹¹ and showed that local injection of recombinant OA increased bone mass in a rat model.¹⁴ Moreover, in a fracture repair model OA expression increased over time, reaching a maximum 2 weeks after fracture.¹¹ In a parallel study, recombinant OA supported bone regeneration and formation in a rat critical-size calvarial defect model.¹⁵ Others have shown that OA is highly expressed by osteoclasts in vitro, suggesting that it may regulate osteoclast formation and activity.¹⁶There is urgent need for an animal model to fully examine the role of OA in osteogenesis. Interestingly, a natural mutation of the Gpnmb gene has been identified in the DBA/2J (D2J) mouse strain.¹⁷ These mice exhibit high-frequency hearing loss, which begins at the time of weaning and becomes severe by 2 to 3 months of age.^18,19 Aged D2J mice also develop progressive eye abnormalities that closely mimic human hereditary glaucoma. The onset of disease symptoms falls roughly between 3 and 4 months of age, and disease becomes severe by 6 months of age.^5,20 D2J mice are homozygous for a nonsense mutation in the Gpnmb gene sequence that induces an early stop codon, generating a truncated protein sequence of 150 amino acids (aa) instead of the full-length 562-aa OA protein.⁵ The control for the D2J mouse is the wild-type DBA/2J-Gpnmb⁺/SjJ mouse (D2J/Gpnmb⁺), homozygous for the wild-type Gpnmb gene.²¹ These Gpnmb wild-type mice do not develop glaucoma, as D2J mice do, although they exhibit mild iris stromal atrophy.²¹In the present study, we used Gpnmb mutant (D2J) and Gpnmb wild-type (D2J/Gpnmb⁺) mice to gain insight into the role of OA in osteogenesis and in osteoblast differentiation and function. Here, we report that loss-of–function mutation of Gpnmb suppresses bone formation by directly affecting osteoblast proliferation and survival, leading to a decreased number of differentiated osteoblasts with suppressed activity in bone mineralization. Thus, our data point to OA as a novel and positive regulator of postnatal bone formation.     

Vancomycin-Variable Enterococcus faecium: In Vivo Emergence of Vancomycin Resistance in a Vancomycin-Susceptible Isolate

We report the emergence of vancomycin resistance in a patient colonized with a vanA-containing, vanRS-negative isolate of Enterococcus faecium which was initially vancomycin susceptible. This is a previously undescribed mechanism of drug resistance with diagnostic and therapeutic implications.     

Matrix Structure Evolution and Nanoreinforcement Distribution in Mechanically Milled and Spark Plasma Sintered Al-SiC Nanocomposites

Development of homogenous metal matrix nanocomposites with uniform distribution of nanoreinforcement, preserved matrix nanostructure features, and improved properties, was possible by means of innovative processing techniques. In this work, Al-SiC nanocomposites were synthesized by mechanical milling and consolidated through spark plasma sintering. Field Emission Scanning Electron Microscope (FE-SEM) with Energy Dispersive X-ray Spectroscopy (EDS) facility was used for the characterization of the extent of SiC particles’ distribution in the mechanically milled powders and spark plasma sintered samples. The change of the matrix crystallite size and lattice strain during milling and sintering was followed through X-ray diffraction (XRD). The density and hardness of the developed materials were evaluated as function of SiC content at fixed sintering conditions using a densimeter and a digital microhardness tester, respectively. It was found that milling for 24 h led to uniform distribution of SiC nanoreinforcement, reduced particle size and crystallite size of the aluminum matrix, and increased lattice strain. The presence and amount of SiC reinforcement enhanced the milling effect. The uniform distribution of SiC achieved by mechanical milling was maintained in sintered samples. Sintering led to the increase in the crystallite size of the aluminum matrix; however, it remained less than 100 nm in the composite containing 10 wt.% SiC. Density and hardness of sintered nanocomposites were reported and compared with those published in the literature.     

Modeling putative therapeutic implications of exosome exchange between tumor and immune cells

Development of effective strategies to mobilize the immune system as a therapeutic modality in cancer necessitates a better understanding of the contribution of the tumor microenvironment to the complex interplay between cancer cells and the immune response. Recently, effort has been directed at unraveling the functional role of exosomes and their cargo of messengers in this interplay. Exosomes are small vesicles (30–200 nm) that mediate local and long-range communication through the horizontal transfer of information, such as combinations of proteins, mRNAs and microRNAs. Here, we develop a tractable theoretical framework to study the putative role of exosome-mediated cell–cell communication in the cancer–immunity interplay. We reduce the complex interplay into a generic model whose three components are cancer cells, dendritic cells (consisting of precursor, immature, and mature types), and killer cells (consisting of cytotoxic T cells, helper T cells, effector B cells, and natural killer cells). The framework also incorporates the effects of exosome exchange on enhancement/reduction of cell maturation, proliferation, apoptosis, immune recognition, and activation/inhibition. We reveal tristability—possible existence of three cancer states: a low cancer load with intermediate immune level state, an intermediate cancer load with high immune level state, and a high cancer load with low immune-level state, and establish the corresponding effective landscape for the cancer–immunity network. We illustrate how the framework can contribute to the design and assessments of combination therapies.Immunotherapeutic approaches have recently emerged as effective therapeutic modalities (1) exemplified by immune checkpoint blockade with anti–CTLA-4 to activate T-cells and induce tumor cell killing, which has been shown to be effective for some cancers but not others (2). A better understanding of the intricate interplay between cancer and the immune system, and of mechanisms of immune evasion and of hijacking of the host response by cancer cells, is relevant to the development of effective immunotherapeutic approaches (3–6).The immune-based suppression of tumor development and progression is mediated through nonspecific innate immunity and antigen-specific adaptive immunity (7). However, cancer cells can inhibit the immune response, thus evading suppression in multiple ways (8) (see below for details), and additionally hijack the immune system to their advantage (3, 4). The challenge to understand the tumor–immune interplay stems from the dynamic nature, and complexity and heterogeneity of both the cancer cells and the immune system and their interactions through the tumor microenvironment (9).Here we consider immune cells as consisting of macrophages (10), natural killer cells (11), cytotoxic T cells (12), helper T cells (13), and regulatory T cells (3). These various immune cells are produced, activated, and perform their functions separated by space and time, which contributes to additional complexity (14). Among the immune cells, dendritic cells (DCs) are the most efficient antigen-presenting cells to bridge innate immunity with adaptive immunity (15). DCs also secrete cytokines that promote the antitumor functions of both natural killer cells and macrophages (16, 17). We consider the tumor microenvironment as comprised of a heterogeneous population of cancer cells (18), stromal cells (19), and tumor-infiltrating immune cells (20). The interactions among these cell types contribute to tumor development and progression. Tumor-associated macrophages and cancer-associated fibroblasts regulate tumor metabolism and engender an immune-suppressive environment by secreting TGF-β and other cytokines (21). Fluctuations in energy sources and oxygen within a tumor contribute to malignant progression and cell phenotypic diversity (22, 23).Though secreted factors play critical roles in cell–cell communications, here we focus on the additional role of cell–cell communication mediated by the exchange of special extracellular lipid vesicles called exosomes (24). These nanovesicles of ∼30–200 nm are formed in the multivesicular bodies and then released from the cell into the extracellular space (25). The exosomes carry a broad range of cargo, including proteins, microRNAs, mRNAs, and DNA fragments, to specific target cells at a remote location (26). Membrane markers assign the exosomes to specific targeted cells. Notably, upon entering the target cell, the exosomes induce modulation of cell function and even identity switch (phenotypic, epigenetic, and even genetic) (27). Exosomes have recently emerged as playing an important role in the immune system interaction with tumors (28, 29). Tumor-derived exosomes can promote metastatic niche formation by influencing bone marrow-derived cells toward a prometastatic phenotype through upregulation of c-Met (29). DCs have been shown to induce tumor cell killing through release of exosomes that contain potent tumor-suppressive factors such as TNF and through activation of natural killer cells, cytotoxic T cells, and helper T cells (24, 30–32). However, tumor-derived exosomes (Tex) can directly inhibit the differentiation of DCs in bone marrow (33), which strongly inhibits the dendritic cell-mediated immune response to the tumor. In addition, Tex can also directly inhibit natural killer cells (34).Mathematical models have been devised to study the complex interactions of cancer and immune system, including those that consider spatial heterogeneity (as reviewed in ref. 35) and those that consider spatially homogeneous populations (as reviewed in ref. 36). Cancer–immunity models have been constructed to investigate the effects of therapy (37–39), cancer dormancy (40), and interactions with time delay (41). Other types of modeling methods have also been applied. For example, tumor growth has also been fitted to experimental data by artificial neural networks (42); a detailed network of cancer immune system has been modeled by multiple subset models (43).In this study, we have developed an exosome exchange-based cancer–immunity interplay (ECI) model, to incorporate the special role of DCs and exosome-mediated communications. Distinct from the previous approaches, our modeling strategy is adapted from methodology used in studies of gene regulatory circuits, allowing us to check the multistability features of the system (44). We find that, by including exosome exchange, the cancer–immunity interplay can give rise to three quasi-stable cancer states, which may be associated with the elimination/equilibrium/escape phases proposed in the immunoediting theory (45). The ECI model is also capable of explaining tumorigenesis by considering the time evolution of immune responses. Guided by the treatment simulations, we assess the effectiveness of various therapeutic protocols with and without time delay and noise.