Endoplasmic Reticulum-associated Degradation Controls Cell Surface Expression of γ-Aminobutyric Acid,Type B Receptors

Metabotropic GABA_B receptors are crucial for controlling the excitability of neurons by mediating slow inhibition in the CNS. The strength of receptor signaling depends on the number of cell surface receptors, which is thought to be regulated by trafficking and degradation mechanisms. Although the mechanisms of GABA_B receptor trafficking are studied to some extent, it is currently unclear whether receptor degradation actively controls the number of GABA_B receptors available for signaling. Here we tested the hypothesis that proteasomal degradation contributes to the regulation of GABA_B receptor expression levels. Blocking proteasomal activity in cultured cortical neurons considerably enhanced total and cell surface expression of GABA_B receptors, indicating the constitutive degradation of the receptors by proteasomes. Proteasomal degradation required Lys⁴⁸-linked polyubiquitination of lysines 767/771 in the C-terminal domain of the GABA_B2 subunit. Inactivation of these ubiquitination sites increased receptor levels and GABA_B receptor signaling in neurons. Proteasomal degradation was mediated by endoplasmic reticulum-associated degradation (ERAD) as shown by the accumulation of receptors in the endoplasmic reticulum upon inhibition of proteasomes, by the increase of receptor levels, as well as receptor signaling upon blocking ERAD function, and by the interaction of GABA_B receptors with the essential ERAD components Hrd1 and p97. In conclusion, the data support a model in which the fraction of GABA_B receptors available for plasma membrane trafficking is regulated by degradation via the ERAD machinery. Thus, modulation of ERAD activity by changes in physiological conditions may represent a mechanism to adjust receptor numbers and thereby signaling strength.     

Estimation of Cell Proliferation Dynamics Using CFSE Data

Advances in fluorescent labeling of cells as measured by flow cytometry have allowed for quantitative studies of proliferating populations of cells. The investigations (Luzyanina et al. in J. Math. Biol. 54:57–89, 2007; J. Math. Biol., 2009; Theor. Biol. Med. Model. 4:1–26, 2007) contain a mathematical model with fluorescence intensity as a structure variable to describe the evolution in time of proliferating cells labeled by carboxyfluorescein succinimidyl ester (CFSE). Here, this model and several extensions/modifications are discussed. Suggestions for improvements are presented and analyzed with respect to statistical significance for better agreement between model solutions and experimental data. These investigations suggest that the new decay/label loss and time dependent effective proliferation and death rates do indeed provide improved fits of the model to data. Statistical models for the observed variability/noise in the data are discussed with implications for uncertainty quantification. The resulting new cell dynamics model should prove useful in proliferation assay tracking and modeling, with numerous applications in the biomedical sciences.     

Impact of Genotype Imputation on the Performance of GBLUP and Bayesian Methods for Genomic Prediction

The aim of this study was to evaluate the impact of genotype imputation on the performance of the GBLUP and Bayesian methods for genomic prediction. A total of 10,309 Holstein bulls were genotyped on the BovineSNP50 BeadChip (50 k). Five low density single nucleotide polymorphism (SNP) panels, containing 6,177, 2,480, 1,536, 768 and 384 SNPs, were simulated from the 50 k panel. A fraction of 0%, 33% and 66% of the animals were randomly selected from the training sets to have low density genotypes which were then imputed into 50 k genotypes. A GBLUP and a Bayesian method were used to predict direct genomic values (DGV) for validation animals using imputed or their actual 50 k genotypes. Traits studied included milk yield, fat percentage, protein percentage and somatic cell score (SCS). Results showed that performance of both GBLUP and Bayesian methods was influenced by imputation errors. For traits affected by a few large QTL, the Bayesian method resulted in greater reductions of accuracy due to imputation errors than GBLUP. Including SNPs with largest effects in the low density panel substantially improved the accuracy of genomic prediction for the Bayesian method. Including genotypes imputed from the 6 k panel achieved almost the same accuracy of genomic prediction as that of using the 50 k panel even when 66% of the training population was genotyped on the 6 k panel. These results justified the application of the 6 k panel for genomic prediction. Imputations from lower density panels were more prone to errors and resulted in lower accuracy of genomic prediction. But for animals that have close relationship to the reference set, genotype imputation may still achieve a relatively high accuracy.     

Glucosylation of ethanol in Ilex paraguariensis cell suspension cultures

To study the stress and defense response of plant cell cultures of Ilex paraguariensis St. Hil., methyl jasmonate, salicylic acid, and cellulase were added, with ethanol and water used as controls. Comparison of methanolic extracts of the treated cells showed a clear decrease in the carbohydrate content of the cells relative to the controls. One new major compound was observed, which was isolated and identified by its spectral data as 1-O-ethyl-β-glucopyranoside. This compound was found especially in the cells treated with methyl jasmonate and salicylic acid, which were both dissolved in ethanol. Addition of ethanol alone also resulted in the formation of the glucoside. Addition of methanol lead to the formation of the corresponding glucoside, while n-propanol addition resulted in only a small amount of the propyl glucoside; with n-butanol, n-octanol, and n-decanol, the corresponding glucosides could not be observed. Cell growth was severely affected by addition of the higher alcohols. From the present study, it is clear that ethanol cannot be considered as an inert solvent for these cell cultures. Received: 11 April 1997 / Revision received: 23 September 1997 / Accepted: 1 November 1997     

Comparison between estimation of breeding values and fixed effects using Bayesian and empirical BLUP estimation under selection on parents and missing pedigree information

Bayesian (via Gibbs sampling) and empirical BLUP (EBLUP) estimation of fixed effects and breeding values were compared by simulation. Combinations of two simulation models (with or without effect of contemporary group (CG)), three selection schemes (random, phenotypic and BLUP selection), two levels of heritability (0.20 and 0.50) and two levels of pedigree information (0% and 15% randomly missing) were considered. Populations consisted of 450 animals spread over six discrete generations. An infinitesimal additive genetic animal model was assumed while simulating data. EBLUP and Bayesian estimates of CG effects and breeding values were, in all situations, essentially the same with respect to Spearman''s rank correlation between true and estimated values. Bias and mean square error (MSE) of EBLUP and Bayesian estimates of CG effects and breeding values showed the same pattern over the range of simulated scenarios. Methods were not biased by phenotypic and BLUP selection when pedigree information was complete, albeit MSE of estimated breeding values increased for situations where CG effects were present. Estimation of breeding values by Bayesian and EBLUP was similarly affected by joint effect of phenotypic or BLUP selection and randomly missing pedigree information. For both methods, bias and MSE of estimated breeding values and CG effects substantially increased across generations.     

Heat treatment of thioredoxin fusions increases the purity of α‐helical transmembrane protein constructs

Membrane proteins play key roles in cellular signaling and transport, represent the majority of drug targets, and are implicated in many diseases. Their relevance renders them important subjects for structural, biophysical, and functional investigations. However, obtaining membrane proteins in high purities is often challenging with conventional purification steps alone. To address this issue, we present here an approach to increase the purity of α‐helical transmembrane proteins. Our approach exploits the Thioredoxin (Trx) tag system, which is able to confer some of its favorable properties, such as high solubility and thermostability, to its fusion partners. Using Trx fusions of transmembrane helical hairpin constructs derived from the human cystic fibrosis transmembrane conductance regulator (CFTR) and a bacterial ATP synthase, we establish conditions for the successful implementation of the selective heat treatment procedure to increase sample purity. We further examine systematically its efficacy with respect to different incubation times and temperatures using quantitative gel electrophoresis. We find that minute‐timescale heat treatment of Trx‐tagged fusion constructs with temperatures ranging from 50 to 90°C increases the purity of the membrane protein samples from ~60 to 98% even after affinity purification. We show that this single‐step approach is even applicable in cases where regular selective heat purification from crude extracts, as reported for Trx fusions to soluble proteins, fails. Overall, our approach is easy to integrate into existing purification strategies and provides a facile route for increasing the purity of membrane protein constructs after purification by standard chromatography approaches.     

Searching protein structure databases with DaliLite v.3

The Red Queen said, 'It takes all the running you can do, to keep in the same place.' Lewis Carrol MOTIVATION: Newly solved protein structures are routinely scanned against structures already in the Protein Data Bank (PDB) using Internet servers. In favourable cases, comparing 3D structures may reveal biologically interesting similarities that are not detectable by comparing sequences. The number of known structures continues to grow exponentially. Sensitive-thorough but slow-search algorithms are challenged to deliver results in a reasonable time, as there are now more structures in the PDB than seconds in a day. The brute-force solution would be to distribute the individual comparisons on a massively parallel computer. A frugal solution, as implemented in the Dali server, is to reduce the total computational cost by pruning search space using prior knowledge about the distribution of structures in fold space. This note reports paradigm revisions that enable maintaining such a knowledge base up-to-date on a PC. AVAILABILITY: The Dali server for protein structure database searching at http://ekhidna.biocenter.helsinki.fi/dali_server is running DaliLite v.3. The software can be downloaded for academic use from http://ekhidna.biocenter.helsinki.fi/dali_lite/downloads/v3.