Construction and Characterization of a CTLA-4-Targeted scFv–Melittin Fusion Protein as a Potential Immunosuppressive Agent for Organ Transplant

Immunotoxins with selective cytotoxicity are frequently used as therapeutic immunosuppressive agents in solid-organ transplantation because of their efficiency and high specificity. In this study, we present a new recombinant immunotoxin termed anti-CTLA-4-scFv–melittin prepared from Escherichia coli aimed at clearing activated T cells at the same time avoiding all-round decline in systematic immunity. This fusion protein is composed of anti-CTLA-4-scFv unit and melittin analog unit with properties of low immunogenicity and selective cytotoxicity to CTLA-4-positive T cells. In preliminary biological activity assays, our results confirmed the feasibility of activated T cell clearance strategy and there were significant differences in cell survival rates between CTLA-4-positive group and control group at all experimental concentrations of the immunotoxin. The selective cytotoxicity, low immunogenicity, and low production cost make it an attractive alternate to traditional immunosuppressants.     

A Melanesian α-thalassemia mutation suggests a novel mechanism for regulating gene expression

A Melanesian variant of the genetic disease α-thalassemia has recently been shown to be due to a single-nucleotide polymorphism located between the adult α-globin genes and their enhancers. The finding that this mutation creates a novel promoter provides support for a mechanism of gene regulation by facilitated chromatin looping.     

Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.