Phospholipase Cgamma1 controls surface expression of TRPC3 through an intermolecular PH domain

Many ion channels are regulated by lipids, but prominent motifs for lipid binding have not been identified in most ion channels. Recently, we reported that phospholipase Cgamma1 (PLC-gamma1) binds to and regulates TRPC3 channels, components of agonist-induced Ca2+ entry into cells. This interaction requires a domain in PLC-gamma1 that includes a partial pleckstrin homology (PH) domain-a consensus lipid-binding and protein-binding sequence. We have developed a gestalt algorithm to detect hitherto 'invisible' PH and PH-like domains, and now report that the partial PH domain of PLC-gamma1 interacts with a complementary partial PH-like domain in TRPC3 to elicit lipid binding and cell-surface expression of TRPC3. Our findings imply a far greater abundance of PH domains than previously appreciated, and suggest that intermolecular PH-like domains represent a widespread signalling mode.     

A new acute transforming feline retrovirus and relationship of its oncogene v-kit with the protein kinase gene family

A new acute transforming feline retrovirus, the Hardy-Zuckerman 4 feline sarcoma virus (HZ4-FeSV), has been isolated from a feline fibrosarcoma. The viral genome of HZ4-FeSV contains a new oncogene designated v-kit, has the structure 5' delta gag-kit-delta pol-delta env 3' and specifies a gag-kit polyprotein of relative molecular mass 80,000. The predicted kit amino-acid sequence displays partial homology with tyrosine-specific protein kinase oncogenes. HZ4-FeSV appears to have been generated by transduction of feline c-kit sequences with feline leukaemia virus.     

Prediction intervals for exponential smoothing using two new classes of state space models

Three general classes of state space models are presented, using the single source of error formulation. The first class is the standard linear model with homoscedastic errors, the second retains the linear structure but incorporates a dynamic form of heteroscedasticity, and the third allows for non‐linear structure in the observation equation as well as heteroscedasticity. These three classes provide stochastic models for a wide variety of exponential smoothing methods. We use these classes to provide exact analytic (matrix) expressions for forecast error variances that can be used to construct prediction intervals one or multiple steps ahead. These formulas are reduced to non‐matrix expressions for 15 state space models that underlie the most common exponential smoothing methods. We discuss relationships between our expressions and previous suggestions for finding forecast error variances and prediction intervals for exponential smoothing methods. Simpler approximations are developed for the more complex schemes and their validity examined. The paper concludes with a numerical example using a non‐linear model. Copyright © 2005 John Wiley & Sons, Ltd.