Functionally active targeting domain of the beta-adrenergic receptor kinase: an inhibitor of G beta gamma-mediated stimulation of type II adenylyl cyclase.

The beta-adrenergic receptor kinase (beta ARK) phosphorylates its membrane-associated receptor substrates, such as the beta-adrenergic receptor, triggering events leading to receptor desensitization. beta ARK activity is markedly stimulated by the isoprenylated beta gamma subunit complex of heterotrimeric guanine nucleotide-binding proteins (G beta gamma), which translocates the kinase to the plasma membrane and thereby targets it to its receptor substrate. The amino-terminal two-thirds of beta ARK1 composes the receptor recognition and catalytic domains, while the carboxyl third contains the G beta gamma binding sequences, the targeting domain. We prepared this domain as a recombinant His6 fusion protein from Escherichia coli and found that it had both independent secondary structure and functional activity. We demonstrated the inhibitory properties of this domain against G beta gamma activation of type II adenylyl cyclase both in a reconstituted system utilizing Sf9 insect cell membranes and in a permeabilized 293 human embryonic kidney cell system. Gi alpha-mediated inhibition of adenylyl cyclase was not affected. These data suggest that this His6 fusion protein derived from the carboxyl terminus of beta ARK1 provides a specific probe for defining G beta gamma-mediated processes and for studying the structural features of a G beta gamma-binding domain.

Lognormal and gamma mixed negative binomial regression

In regression analysis of counts, a lack of simple and efficient algorithms for posterior computation has made Bayesian approaches appear unattractive and thus underdeveloped. We propose a lognormal and gamma mixed negative binomial (NB) regression model for counts, and present efficient closed-form Bayesian inference; unlike conventional Poisson models, the proposed approach has two free parameters to include two different kinds of random effects, and allows the incorporation of prior information, such as sparsity in the regression coefficients. By placing a gamma distribution prior on the NB dispersion parameter r, and connecting a log-normal distribution prior with the logit of the NB probability parameter p, efficient Gibbs sampling and variational Bayes inference are both developed. The closed-form updates are obtained by exploiting conditional conjugacy via both a compound Poisson representation and a Polya-Gamma distribution based data augmentation approach. The proposed Bayesian inference can be implemented routinely, while being easily generalizable to more complex settings involving multivariate dependence structures. The algorithms are illustrated using real examples. Copyright 2012 by the author(s)/owner(s).

Selected Gamma Aminobutyric Acid (GABA) Esters may Provide Analgesia for Some Central Pain Conditions.

Central pain is an enigmatic, intractable condition, related to destruction of thalamic areas, resulting in likely loss of inhibitory synaptic transmission mediated by GABA. It is proposed that treatment of central pain, a localized process, may be treated by GABA supplementation, like Parkinson's disease and depression. At physiologic pH, GABA exists as a zwitterion that is poorly permeable to the blood brain barrier (BBB). Because the pH of the cerebral spinal fluid (CSF) is acidic relative to the plasma, ion trapping may allow a GABA ester prodrug to accumulate and be hydrolyzed within the CSF. Previous investigations with ester local anesthetics may be applicable to some GABA esters since they are weak bases, hydrolyzed by esterases and cross the BBB. Potential non-toxic GABA esters are discussed. Many GABA esters were investigated in the 1980s and it is hoped that this paper may spark renewed interest in their development.