Interaction of the GM2 Activator Protein with Sulfated and Sialylated Glycosphingolipids

Membrane lipids and their degradation compounds control GM2 catabolism at intralysosomal luminal vesicles

The catabolism of ganglioside GM2 is dependent on three gene products. Mutations in any of these genes result in a different type of GM2 gangliosidosis (Tay-Sachs disease, Sandhoff disease, and the B1 and AB variants of GM2 gangliosidosis), with GM2 as the major lysosomal storage compound. GM2 is also a secondary storage compound in lysosomal storage diseases such as Niemann-Pick disease types A-C, with primary storage of SM in type A and cholesterol in types B and C, respectively. The reconstitution of GM2 catabolism at liposomal surfaces carrying GM2 revealed that incorporating lipids into the GM2-carrying membrane such as cholesterol, SM, sphingosine, and sphinganine inhibits GM2 hydrolysis by β-hexosaminidase A assisted by GM2 activator protein, while anionic lipids, ceramide, fatty acids, lysophosphatidylcholine, and diacylglycerol stimulate GM2 catabolism. In contrast, the hydrolysis of the synthetic, water-soluble substrate 4-methylumbelliferyl-6-sulfo-2-acetamido-2-deoxy-β-d-glucopyranoside was neither significantly affected by membrane lipids such as ceramide or SM nor stimulated by anionic lipids such as bis(monoacylglycero)phosphate added as liposomes, detergent micelles, or lipid aggregates. Moreover, hydrolysis-inhibiting lipids also had an inhibiting effect on the solubilization and mobilization of membrane-bound lipids by the GM2 activator protein, while the stimulating lipids enhanced lipid mobilization.

Survey of the year 2003 commercial optical biosensor literature

In the year 2003 there was a 17% increase in the number of publications citing work performed using optical biosensor technology compared with the previous year. We collated the 962 total papers for 2003, identified the geographical regions where the work was performed, highlighted the instrument types on which it was carried out, and segregated the papers by biological system. In this overview, we spotlight 13 papers that should be on everyone's 'must read' list for 2003 and provide examples of how to identify and interpret high-quality biosensor data. Although we still find that the literature is replete with poorly performed experiments, over-interpreted results and a general lack of understanding of data analysis, we are optimistic that these shortcomings will be addressed as biosensor technology continues to mature.