Nucleotide sequence of a cDNA encoding a common precursor of disintegrin flavostatin and hemorrhagic factor HR2a from the venom of Trimeresurus flavoviridis

AbstractThe venom of Trimeresurus flavoviridis has three disintegrins that act as platelet aggregation inhibitors by binding to integrin αIIbβ3 on platelets through its Arg-Gly-Asp sequence. We isolated the cDNA encoding the flavostatin precursor that is one of the disintegrins in T. flavoviridis venom. The open reading frame consisted of four regions, a pre-peptide region, a metalloprotease region, a spacer region and a disintegrin region, indicating that the flavostatin precursor belongs to the metalloprotease/disintegrin family. Surprisingly, the deduced amino acid sequence of the metalloprotease region was completely consistent with that of hemorrhagic metalloprotease HR2a, which indicated that this metalloprotease released from the flavostatin precursor functions as a hemorrhagic factor. These observations indicated that a disintegrin and a hemorrhagic metalloprotease were synthesized as a common precursor. Thus, our results support the hypothesis that a disintegrin is synthesized as a metalloprotease/disintegrin precursor and matures by cleavage from the precursor molecule

Basement membrane-like structures containing NTH α1(IV) are formed around the endothelial cell network in a novel in vitro angiogenesis model

Angiogenesis is a process through which new blood vessels are formed by sprouting and elongating from existing blood vessels. Several methods have been used to replicate angiogenesis in vitro, including culturing vascular endothelial cells on Matrigel and coculturing with endothelial cells and fibroblasts. However, the angiogenesis elongation process has not been completely clarified in these models. We therefore propose a new in vitro model of angiogenesis, suitable for observing vascular elongation, by seeding a spheroid cocultured from endothelial cells and fibroblasts into a culture dish. In this model, endothelial cells formed tubular networks elongated from the spheroid with a lumen structure and were connected with tight junctions. A basement membrane (BM)-like structure was observed around the tubular network, similarly to blood vessels in vivo. These results suggested that blood vessel-like structure could be reconstituted in our model. Laminin and type IV collagen, main BM components, were highly localized around the network, along with nontriple helical form of type IV collagen α1-chain [NTH α1(IV)]. In an ascorbic acid-depleted condition, laminin and NTH α1(IV) were observed around the network but not the triple-helical form of type IV collagen and the network was unstable. These results suggest that laminin and NTH α1(IV) are involved in the formation of tubular network and type IV collagen is necessary to stabilize the network

Microarray optimizations: increasing spot accuracy and automated identification of true microarray signals

In this paper, fluorescent microarray images and various analysis techniques are described to improve the microarray data acquisition processes. Signal intensities produced by rarely expressed genes are initially correctly detected, but they are often lost in corrections for background, log or ratio. Our analyses indicate that a simple correlation between the mean and median signal intensities may be the best way to eliminate inaccurate microarray signals. Unlike traditional quality control methods, the low intensity signals are retained and inaccurate signals are eliminated in this mean and median correlation. With larger amounts of microarray data being generated, it becomes increasingly more difficult to analyze data on a visual basis. Our method allows for the automatic quantitative determination of accurate and reliable signals, which can then be used for normalization. We found that a mean to median correlation of 85% or higher not only retains more data than current methods, but the retained data is more accurate than traditional thresholds or common spot flagging algorithms. We have also found that by using pin microtapping and microvibrations, we can control spot quality independent from initial PCR volume

Microarray optimizations: increasing spot accuracy and automated identification of true microarray signals

In this paper, fluorescent microarray images and various analysis techniques are described to improve the microarray data acquisition processes. Signal intensities produced by rarely expressed genes are initially correctly detected, but they are often lost in corrections for background, log or ratio. Our analyses indicate that a simple correlation between the mean and median signal intensities may be the best way to eliminate inaccurate microarray signals. Unlike traditional quality control methods, the low intensity signals are retained and inaccurate signals are eliminated in this mean and median correlation. With larger amounts of microarray data being generated, it becomes increasingly more difficult to analyze data on a visual basis. Our method allows for the automatic quantitative determination of accurate and reliable signals, which can then be used for normalization. We found that a mean to median correlation of 85% or higher not only retains more data than current methods, but the retained data is more accurate than traditional thresholds or common spot flagging algorithms. We have also found that by using pin microtapping and microvibrations, we can control spot quality independent from initial PCR volume

Grainyhead-like 3, a transcription factor identified in a microarray screen, promotes the specification of the superficial layer of the embryonic epidermis

The Xenopus ectoderm consists of two populations of cells, superficial polarised epithelial cells and deep, non-epithelial cells. These two cell types differ in their developmental fate. In the neural ectoderm, primary neurons are derived only from the deep cells. In the epidermal ectoderm, superficial cells express high levels of differentiation markers, while most of the deep cells do not differentiate until later when they produce the stratified adult epidermis. However, few molecular differences are known between the deep and superficial cells. Here, we have undertaken a systematic approach to identify genes that show layer-restricted expression by microarray analysis of deep and superficial cells at the gastrula stage, followed by wholemount in situ hybridisation. We have identified 32 differentially expressed genes, of which 26 show higher expression in the superficial layer and 6 in the deep layer and describe their expression at the gastrula and neurula stage. One of the identified genes is the transcription factor Grh13, which we found to be expressed in the superficial layer of the gastrula ectoderm and the neurula epidermis. By using markers identified in this work.. we show that Grlh3 promotes superficial gene expression in the deep layer of the epidermis. Concomitantly, deep layer specific genes are switched off, showing that Grlh3 can promote deep cells to take on a superficial cell identity in the embryonic epidermis. (c) 2006 Elsevier Ireland Ltd. All rights reserved