Biomedical subjects

Chuanyi Ji

Publications and source records attributed to Chuanyi Ji.

4 recordsLinked to original sources

PubMed2003

Expression and in vitro activation of Manduca sexta prophenoloxidase-activating proteinase-2 precursor (proPAP-2) from baculovirus-infected insect cells.

Prophenoloxidase activation is a component of the immune system in insects and crustaceans. We recently purified and cloned a new prophenoloxidase-activating proteinase (PAP-2) from hemolymph of the tobacco hornworm Manduca sexta [J. Biol. Chem. 278, 3552-3561]. As the terminal component of a putative serine proteinase cascade, this enzyme activates prophenoloxidase (proPO) via limited proteolysis. To purify and study the activating proteinase for PAP-2 from this insect, we expressed the zymogen of PAP-2 (proPAP-2) in insect cells infected by a recombinant baculovirus that harbors the cDNA. To facilitate the purification of proPAP-2, we modified a commercial vector (pFastBac1) by inserting a synthetic DNA fragment encoding a hexahistidine sequence, allowing fusion of the affinity tag to the carboxyl terminus of a protein. After Spodoptera frugiperda Sf21 cells were infected by the virus, recombinant proPAP-2 was efficiently secreted into the media at a concentration of 5.9 microg/ml under the optimal conditions. After ammonium sulfate precipitation, the proenzyme was purified to near homogeneity by affinity chromatography on Ni(2+)-NTA agarose. Western blot analysis indicated that the recombinant proPAP-2 has a mobility slightly lower than that of the zymogen from M. sexta hemolymph. The molecular mass and isoelectric point of proPAP-2 were determined to be 47,573+/-11Da and 6.6, respectively. After the purified proenzyme was added to hemolymph from induced M. sexta larvae, it was rapidly activated by an unknown proteinase in the presence of peptidoglycan.

Amidohydrolases↗

PubMed2002

A serpin mutant links Toll activation to melanization in the host defence of Drosophila.

A prominent response during the Drosophila host defence is the induction of proteolytic cascades, some of which lead to localized melanization of pathogen surfaces, while others activate one of the major players in the systemic antimicrobial response, the Toll pathway. Despite the fact that gain-of-function mutations in the Toll receptor gene result in melanization, a clear link between Toll activation and the melanization reaction has not been firmly established. Here, we present evidence for the coordination of hemolymph-borne melanization with activation of the Toll pathway in the Drosophila host defence. The melanization reaction requires Toll pathway activation and depends on the removal of the Drosophila serine protease inhibitor Serpin27A. Flies deficient for this serpin exhibit spontaneous melanization in larvae and adults. Microbial challenge induces its removal from the hemolymph through Toll-dependent transcription of an acute phase immune reaction component.

Animals↗

PubMed1997

An Efficient EM-based Training Algorithm for Feedforward Neural Networks.

A fast training algorithm is developed for two-layer feedforward neural networks based on a probabilistic model for hidden representations and the EM algorithm. The algorithm decomposes training the original two-layer networks into training a set of single neurons. The individual neurons are then trained via a linear weighted regression algorithm. Significant improvement on training speed has been made using this algorithm for several bench-mark problems. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Journal Article↗

PubMed1997

Network Synthesis through Data-Driven Growth and Decay.

AN ALGORITHM FOR ADDITION AND DELETION (ADDEL) OF RESOURCES DURING LEARNING IS DEVELOPED TO ACHIEVE TWO GOALS: (1) to find feed-forward multilayer networks that are as small as possible, (2) to find an appropriate structure for such small networks. These goals are accomplished by operating alternately between an adding phase and a deleting phase while learning the given input-output associations. The adding phase develops a crude structure by filling in resources (connections, units and layers) at a virtual multilayer network with a maximum of L possible layers. The deleting phase then removes any unnecessary connections to obtain a refined structure. The additions and deletions are done based on a sensitivity measure and a corresponding probability rule so that only the synapses which are most effective in reducing the output error are preserved. A generalization error estimated from a validation set is used to control the alternation between the two learning phases and the termination of learning. Simulations, including handwritten digit recognition, demonstrate that the algorithm is effective in finding an appropriate network structure for a small network which can generalize well. The algorithm is used to investigate when the size of a network is important for generalization. Copyright 1997 Elsevier Science Ltd.

Journal Article↗