A switch-on mechanism to activate maize ribosome-inactivating protein for targeting HIV-infected cells

Maize ribosome-inactivating protein (RIP) is a plant toxin that inactivates eukaryotic ribosomes by depurinating a specific adenine residue at the a-sarcin/ricin loop of 28S rRNA. Maize RIP is first produced as a proenzyme with a 25-amino acid internal inactivation region on the protein surface. During germination, proteolytic removal of this internal inactivation region generates the active heterodimeric maize RIP with full N-glycosidase activity. This naturally occurring switch-on mechanism provides an opportunity for targeting the cytotoxin to pathogen-infected cells. Here, we report the addition of HIV-1 protease recognition sequences to the internal inactivation region and the activation of the maize RIP variants by HIV-1 protease in vitro and in HIV-infected cells. Among the variants generated, two were cleaved efficiently by HIV-1 protease. The HIV-1 protease-activated variants showed enhanced N-glycosidase activity in vivo as compared to their un-activated counterparts. They also possessed potent inhibitory effect on p24 antigen production in human T cells infected by two HIV-1 strains. This switch-on strategy for activating the enzymatic activity of maize RIP in target cells provides a platform for combating pathogens with a specific protease.

Effects of the “all-fish” GH (growth hormone) transgene expression on resistance to Ichthyophthirius multifiliis infections in common carp, Cyprinus carpio L.

A study was undertaken on the susceptibility of the F-4 generation of "all-fish" growth hormone transgenic carp, Cyprinus carpio L, against Ichthyophthirius multifiliis infections. When 1-year old, transgenic carp, with non-transgenic carp and non-manipulated carp (controls) were split into three batches, and experimental infections were performed throughout the 3-month period. All 72 fish were successfully infected. It was shown that there was a significant difference (P<0.01) on infection level between transgenics and non-transgenics, and transgenics and controls. It possibly resulted from transgenics that had stronger non-specific immune functions. In addition, fish surface area affected significantly infection level (P<0.001). Carp with larger surface area harboured more parasites for each type of fish, but transgenic with larger surface area than non-transgenics and controls (P<0.01), loaded fewer parasites than others. Besides, the time of infection also greatly influenced (P<0.001) infection level. Results showed that there was a significant decline in parasite infectivity through October to November (P<0.001). It was likely to suggest that there existed senescence resulted in failure of any I. multifiliis isolate maintenance. Significant difference in infectivity between isolate G from grass carp and isolate H from gold fish suggested that different parasite strains may exist. (C) 2009 Elsevier B.V. All rights reserved.

Algorithm Studies on How to Obtain a Conditional Nonlinear Optimal Perturbation (CNOP)

The conditional nonlinear optimal perturbation (CNOP), which is a nonlinear generalization of the linear singular vector (LSV), is applied in important problems of atmospheric and oceanic sciences, including ENSO predictability, targeted observations, and ensemble forecast. In this study, we investigate the computational cost of obtaining the CNOP by several methods. Differences and similarities, in terms of the computational error and cost in obtaining the CNOP, are compared among the sequential quadratic programming (SQP) algorithm, the limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, and the spectral projected gradients (SPG2) algorithm. A theoretical grassland ecosystem model and the classical Lorenz model are used as examples. Numerical results demonstrate that the computational error is acceptable with all three algorithms. The computational cost to obtain the CNOP is reduced by using the SQP algorithm. The experimental results also reveal that the L-BFGS algorithm is the most effective algorithm among the three optimization algorithms for obtaining the CNOP. The numerical results suggest a new approach and algorithm for obtaining the CNOP for a large-scale optimization problem.