Shiga toxin B-subunit as a tool to study retrograde transport.

Abstract

AbstractShiga toxin, produced by Shigella dysenteriae and Shiga toxin-producing Escherichia coli, is a bacterial protein toxin composed of two subunits, A and B (1). The A-subunit is the actual toxin whose ribosomal RNA N-glycosidase activity leads to the inhibition of protein biosynthesis in target cells. For binding to the cellular Shiga toxin receptor, the glycosphingolipid globotriaosylceramide (Gb3 or CD77), the A-subunit depends on its noncovalent interaction with the B-subunit, termed here StxB, composed of a homopentamer of B-fragments. In a number of cell types, Shiga toxin is transported from the plasma membrane to the endoplasmic reticulum (ER), via early endosomes and the Golgi apparatus (1–3), bypassing the late endocytic pathway (4). It is generally admitted that this toxin, like other members of the Shiga family (5), translocates from the lumen of the compartments to the cytosol at the level of the ER (6). Apart from its importance for the entry of Shiga toxin into target cells, the study of the retrograde transport route also plays a fundamental role in our understanding of intracellular membrane homeostasis and the steady-state localization of cellular proteins. The fine dissection of the molecular mechanisms underlying the fundamental steps of the retrograde route requires experimental systems that allow the quantitative analysis of these steps. In this chapter, we summarize what we call the Shiga toolbox: mutant StxB and experimental setups that allow measuring endocytosis, arrival in the trans-Golgi network (TGN), and the ER. The basic element in the Shiga toolbox is StxB, which conserves the intracellular transport characteristics of the holotoxin, but which, in most cell types, does not have adverse effects.KeywordsEarly EndosomeShiga ToxinPolyacrylamide SolutionSulfotransferase ActivityBlotting BufferThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.