In-Silico Patterning of Vascular Mesenchymal Cells in Three Dimensions

Cells organize in complex three-dimensional patterns by interacting with proteins along with the surrounding extracellular matrix. This organization provides the mechanical and chemical cues that ultimately influence a cell's differentiation and function. Here, we computationally investigate the pattern formation process of vascular mesenchymal cells arising from their interaction with Bone Morphogenic Protein-2 (BMP-2) and its inhibitor, Matrix Gla Protein (MGP). Using a first-principles approach, we derive a reaction-diffusion model based on the biochemical interactions of BMP-2, MGP and cells. Simulations of the model exhibit a wide variety of three-dimensional patterns not observed in a two-dimensional analysis. We demonstrate the emergence of three types of patterns: spheres, tubes, and sheets, and show that the patterns can be tuned by modifying parameters in the model such as the degradation rates of proteins and chemotactic coefficient of cells. Our model may be useful for improved engineering of three-dimensional tissue structures as well as for understanding three dimensional microenvironments in developmental processes.National Institutes of Health (U.S.) (GM69811)United States. Dept. of Energy (DOE CSGF fellowship

Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

Background: Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions. Methods: This study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 β (IL-1β). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified. Results: BioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1β, carprofen, and IL-1β and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein. Conclusions: Using this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation

Overexpression of Akt1 Enhances Adipogenesis and Leads to Lipoma Formation in Zebrafish

<div><h3>Background</h3><p>Obesity is a complex, multifactorial disorder influenced by the interaction of genetic, epigenetic, and environmental factors. Obesity increases the risk of contracting many chronic diseases or metabolic syndrome. Researchers have established several mammalian models of obesity to study its underlying mechanism. However, a lower vertebrate model for conveniently performing drug screening against obesity remains elusive. The specific aim of this study was to create a zebrafish obesity model by over expressing the insulin signaling hub of the <em>Akt1</em> gene.</p> <h3>Methodology/Principal Findings</h3><p><em>Skin oncogenic transformation screening shows that a stable zebrafish transgenic of Tg(krt4Hsa.myrAkt1</em>)^cy18 displays severely obese phenotypes at the adult stage. In Tg(<em>krt4:Hsa.myrAkt1</em>)^cy18, the expression of exogenous human constitutively active Akt1 (myrAkt1) can activate endogenous downstream targets of mTOR, GSK-3α/β, and 70S6K. During the embryonic to larval transitory phase, the specific over expression of myrAkt1 in skin can promote hypertrophic and hyperplastic growth. From 21 hour post-fertilization (hpf) onwards, myrAkt1 transgene was ectopically expressed in several mesenchymal derived tissues. This may be the result of the integration position effect. Tg(<em>krt4:Hsa.myrAkt1</em>)^cy18 caused a rapid increase of body weight, hyperplastic growth of adipocytes, abnormal accumulation of fat tissues, and blood glucose intolerance at the adult stage. Real-time RT-PCR analysis showed the majority of key genes on regulating adipogenesis, adipocytokine, and inflammation are highly upregulated in Tg(<em>krt4:Hsa.myrAkt1</em>)^cy18. In contrast, the myogenesis- and skeletogenesis-related gene transcripts are significantly downregulated in Tg(<em>krt4:Hsa.myrAkt1</em>)^cy18, suggesting that excess adipocyte differentiation occurs at the expense of other mesenchymal derived tissues.</p> <h3>Conclusion/Significance</h3><p>Collectively, the findings of this study provide direct evidence that Akt1 signaling plays an important role in balancing normal levels of fat tissue in vivo. The obese zebrafish examined in this study could be a new powerful model to screen novel drugs for the treatment of human obesity.</p> </div

Matrix Gla protein regulates differentiation of endothelial cells derived from mouse embryonic stem cells

Matrix Gla protein (MGP) is an antagonist of bone morphogenetic proteins (BMPs) and expressed in vascular endothelial cells. Lack of MGP causes vascular abnormalities in multiple organs in mice. The objective of this study is to define the role of MGP in early endothelial differentiation. We find that expression of endothelial markers is highly induced in Mgp null organs, which, in wild type, contain high MGP expression. Furthermore, Mgp null embryonic stem cells express higher levels of endothelial markers than wild type controls and an abnormal temporal pattern of expression. We also find that the Mgp-deficient endothelial cells adopt characteristics of mesenchymal stem cells. We conclude that loss of MGP causes dysregulation of early endothelial differentiation