Publications

The small GTPase accelerators regulator of G protein signalling (RGS) proteins are important regulators of proximal signalling from G protein coupled receptors. Although natural killer (NK) cells express a number of G-protein coupled receptors, expression of RGS proteins has not been investigated. We analysed the expression of RGS proteins in rat NK cells, and detected mRNA for RGS1, RGS2, RGS5, RGS8, RGS16, and RGS18. Interestingly, when we included a panel of different leucocyte subsets, we found that RGS8 was selectively expressed by NK cells. NK cells are under control of both activating and inhibitory receptors and, utilizing a xenogeneic system where the mouse activating Ly49D or inhibitory Ly49A receptors were transfected into the rat RNK-16 cell line, the potential regulation of RGS proteins by single NK cell receptors was studied. We found that ligation of Ly49D led to a rapid and transient increase in message for RGS2, while Ly49A ligation up-regulated RGS2, RGS16, and RGS18 mRNA. Both receptors also induced a prolonged increase in RGS2 endogenous protein levels. These findings suggest that RGS proteins may be influenced by or involved in NK cell receptor events, suggesting a crosstalk between G-protein coupled receptors and NK cell receptors.

Determining phylogenetic relationships between species is a difficult problem, and many phylogenetic relationships remain unresolved, even among eutherian mammals. Repetitive elements provide excellent markers for phylogenetic analysis, because their mode of evolution is predominantly homoplasy-free and unidirectional. Historically, phylogenetic studies using repetitive elements have relied on biological methods such as PCR analysis, and computational inference is limited to a few isolated repeats. Here, we present a novel computational method for inferring phylogenetic relationships from partial sequence data using orthologous repeats. We apply our method to reconstructing the phylogeny of 28 mammals, using more than 1000 orthologous repeats obtained from sequence data available from the NISC Comparative Sequencing Program. The resulting phylogeny has robust bootstrap numbers, and broadly matches results from previous studies which were obtained using entirely different data and methods. In addition, we shed light on some of the debatable aspects of the phylogeny. With rapid expansion of available partial sequence data, computational analysis of repetitive elements holds great promise for the future of phylogenetic inference.