The HDAC Inhibitor FK228 Enhances Adenoviral Transgene Expression by a Transduction-Independent Mechanism but Does Not Increase Adenovirus Replication

The histone deacetylase inhibitor FK228 has previously been shown to enhance adenoviral transgene expression when cells are pre-incubated with the drug. Upregulation of the coxsackie adenovirus receptor (CAR), leading to increased viral transduction, has been proposed as the main mechanism. In the present study, we found that the highest increase in transgene expression was achieved when non-toxic concentrations of FK228 were added immediately after transduction, demonstrating that the main effect by which FK228 enhances transgene expression is transduction-independent. FK228 had positive effects both on Ad5 and Ad5/f35 vectors with a variety of transgenes and promoters, indicating that FK228 works mainly by increasing transgene expression at the transcriptional level. In some cases, the effects were dramatic, as demonstrated by an increase in CD40L expression by FK228 from 0.3% to 62% when the murine prostate cancer cell line TRAMP-C2 was transduced with Ad[CD40L]. One unexpected finding was that FK228 decreased the transgene expression of an adenoviral vector with the prostate cell-specific PPT promoter in the human prostate adenocarcinoma cell lines LNCaP and PC-346C. This is probably a consequence of alteration of the adenocarcinoma cell lines towards a neuroendocrine differentiation after FK228 treatment. The observations in this study indicate that FK228 enhances adenoviral therapy by a transduction-independent mechanism. Furthermore, since histone deacetylase inhibitors may affect the differentiation of cells, it is important to keep in mind that the activity and specificity of tissue- and tumor-specific promoters may also be affected

Soluble THSD7A Is an N-Glycoprotein That Promotes Endothelial Cell Migration and Tube Formation in Angiogenesis

BACKGROUND: Thrombospondin type I domain containing 7A (THSD7A) is a novel neural protein that is known to affect endothelial migration and vascular patterning during development. To further understand the role of THSD7A in angiogenesis, we investigated the post-translational modification scheme of THS7DA and to reveal the underlying mechanisms by which this protein regulates blood vessel growth. METHODOLOGY/PRINCIPAL FINDINGS: Full-length THSD7A was overexpressed in human embryonic kidney 293T (HEK293T) cells and was found to be membrane associated and N-glycosylated. The soluble form of THSD7A, which is released into the cultured medium, was harvested for further angiogenic assays. We found that soluble THSD7A promotes human umbilical vein endothelial cell (HUVEC) migration and tube formation. HUVEC sprouts and zebrafish subintestinal vessel (SIV) angiogenic assays further revealed that soluble THSD7A increases the number of branching points of new vessels. Interestingly, we found that soluble THSD7A increased the formation of filopodia in HUVEC. The distribution patterns of vinculin and phosphorylated focal adhesion kinase (FAK) were also affected, which implies a role for THSD7A in focal adhesion assembly. Moreover, soluble THSD7A increased FAK phosphorylation in HUVEC, suggesting that THSD7A is involved in regulating cytoskeleton reorganization. CONCLUSIONS/SIGNIFICANCE: Taken together, our results indicate that THSD7A is a membrane-associated N-glycoprotein with a soluble form. Soluble THSD7A promotes endothelial cell migration during angiogenesis via a FAK-dependent mechanism and thus may be a novel neuroangiogenic factor

A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm

This paper presents a novel conflict-resolving neural network classifier that combines the ordering algorithm, fuzzy ARTMAP (FAM), and the dynamic decay adjustment (DDA) algorithm, into a unified framework. The hybrid classifier, known as Ordered FAMDDA, applies the DDA algorithm to overcome the limitations of FAM and ordered FAM in achieving a good generalization/performance. Prior to network learning, the ordering algorithm is first used to identify a fixed order of training patterns. The main aim is to reduce and/or avoid the formation of overlapping prototypes of different classes in FAM during learning. However, the effectiveness of the ordering algorithm in resolving overlapping prototypes of different classes is compromised when dealing with complex datasets. Ordered FAMDDA not only is able to determine a fixed order of training patterns for yielding good generalization, but also is able to reduce/resolve overlapping regions of different classes in the feature space for minimizing misclassification during the network learning phase. To illustrate the effectiveness of Ordered FAMDDA, a total of ten benchmark datasets are experimented. The results are analyzed and compared with those from FAM and Ordered FAM. The outcomes demonstrate that Ordered FAMDDA, in general, outperforms FAM and Ordered FAM in tackling pattern classification problems.<br /