Modulation of carbachol-stimulated inositol phospholipid breakdown in rat cerebral cortical miniprisms by excitatory amino acids and by BAY K-8644 is dependent upon the assay calcium and potassium concentrations used.

The calcium and potassium ion dependency of the inositol phospholipid breakdown response to stimulatory agents has been investigated in rat cerebral cortical miniprisms. The calcium channel agonist BAY K-8644 (10 microM) potentiated the response to carbachol at 6 mM K+ when Ca2(+)-free, but not when 2.52 mM Ca2+ assay buffer was used. In Ca2(+)-free buffer, verapamil (10 microM) inhibited the response to carbachol at both 6 and 18 mM K+ but higher concentrations (30-300 microM) were needed when 2.52 mM Ca2+ was used. At these higher concentrations, however, verapamil inhibited the binding of 2 nM [3H]pirenzepine to muscarinic recognition sites. N-Methyl-D-Aspartate (NMDA, 100 microM) significantly reduced the basal phosphoinositide breakdown rate at 18 mM K+ at 1.3 mM Ca2+, but was without effect on the basal rate at other K+ and Ca2+ concentrations. In the presence of NMDA (100 microM) or quisqualate (100 microM), the responses to carbachol were reduced, the degree of reduction showing a complex dependency upon the assay K+ and Ca2+ concentrations used. These results indicate that the inositol phospholipid breakdown response to carbachol in cerebral cortical miniprisms can be modulated in a manner dependent upon the extracellular calcium and potassium concentrations used.     

On-line multi-analyzer monitoring of biomass, glucose and acetate for growth rate control of a Vibrio cholerae fed-batch cultivation

In situ near-infrared (NIR) spectroscopy and in-line electronic nose (EN) mapping were used to monitor and control a cholera-toxin producing Vibrio cholerae fed-batch cultivation carried out with a laboratory method as well as with a production method. Prediction models for biomass, glucose and acetate using NIR spectroscopy were developed based on spectral identification and partial-least squares (PLS) regression resulting in high correlation to reference data (standard errors of prediction for biomass, glucose and acetate were 0.20 gl(-1), 0.26 gl(-1) and 0.28 gl(-1)). A compensation algorithm for aerated bioreactor disturbances was integrated in the model computation, which in particular improved the prediction by the biomass model. First, the NIR data were applied together with EN in-line data selected by principal component analysis (PCA) for generating a trajectory representation of the fed-batch cultivation. A correlation between the culture progression and EN signals was demonstrated, which proved to be beneficial in monitoring the culture quality. It was shown that a deviation from a normal cultivation behavior could easily be recognized and that the trajectory was able to alarm a bacterial contamination. Second, the NIR data indicated the potential of predicting the concentration of formed cholera toxin with a model prediction error of 0.020 gl(-1). Third, the on-line biomass prediction based on the NIR model was used to control the overflow metabolism acetate formation of the V. cholerae culture. The controller compared actual specific growth rate as estimated from the prediction with the critical acetate formation growth rate, and from that difference adjusted the glucose feed rate.     

Ligand-induced alpha2-adrenoceptor endocytosis: relationship to Gi protein activation

Most G protein-coupled receptors are desensitized by a uniform two-step mechanism: phosphorylation followed by arrestin binding and internalization. In this study we explored the time-, ligand-, and concentration dependence of alpha2-adrenoceptor internalization in human embryonal kidney (HEK-293) cells expressing alpha2A- and alpha2B-adrenoceptors. We also explored the relationship between ligand-induced receptor internalization and agonist efficacy, determined with a [35S]GTPgammaS binding assay. The results showed rapid dose-dependent internalization of both alpha2A- and alpha2B-receptors; the extent of internalization was directly proportional to agonist efficacy. The agonist UK 14,304 had a subtype-specific high efficacy at alpha2A-AR and dexmedetomidine at alpha2B-AR. Agonist-induced [35S]GTPgammaS binding was totally blocked by pretreatment with pertussis toxin (PTX) for both receptor subtypes, while only about 50% of the internalization was blocked by PTX. The results indicate that the extent of internalization of alpha2A-AR and alpha2B-AR is proportional to agonist efficacy, but only partly dependent on Gi protein coupling.     

Probabilistic Identification of Cerebellar Cortical Neurones across Species

Despite our fine-grain anatomical knowledge of the cerebellar cortex, electrophysiological studies of circuit information processing over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cell types. We approached this problem by assessing the spontaneous activity signatures of identified cerebellar cortical neurones. A range of statistics describing firing frequency and irregularity were then used, individually and in combination, to build Gaussian Process Classifiers (GPC) leading to a probabilistic classification of each neurone type and the computation of equi-probable decision boundaries between cell classes. Firing frequency statistics were useful for separating Purkinje cells from granular layer units, whilst firing irregularity measures proved most useful for distinguishing cells within granular layer cell classes. Considered as single statistics, we achieved classification accuracies of 72.5% and 92.7% for granular layer and molecular layer units respectively. Combining statistics to form twin-variate GPC models substantially improved classification accuracies with the combination of mean spike frequency and log-interval entropy offering classification accuracies of 92.7% and 99.2% for our molecular and granular layer models, respectively. A cross-species comparison was performed, using data drawn from anaesthetised mice and decerebrate cats, where our models offered 80% and 100% classification accuracy. We then used our models to assess non-identified data from awake monkeys and rabbits in order to highlight subsets of neurones with the greatest degree of similarity to identified cell classes. In this way, our GPC-based approach for tentatively identifying neurones from their spontaneous activity signatures, in the absence of an established ground-truth, nonetheless affords the experimenter a statistically robust means of grouping cells with properties matching known cell classes. Our approach therefore may have broad application to a variety of future cerebellar cortical investigations, particularly in awake animals where opportunities for definitive cell identification are limited.     

Automated ensemble modeling with modelMaGe: analyzing feedback mechanisms in the Sho1 branch of the HOG pathway

In systems biology uncertainty about biological processes translates into alternative mathematical model candidates. Here, the goal is to generate, fit and discriminate several candidate models that represent different hypotheses for feedback mechanisms responsible for downregulating the response of the Sho1 branch of the yeast high osmolarity glycerol (HOG) signaling pathway after initial stimulation. Implementing and testing these candidate models by hand is a tedious and error-prone task. Therefore, we automatically generated a set of candidate models of the Sho1 branch with the tool modelMaGe. These candidate models are automatically documented, can readily be simulated and fitted automatically to data. A ranking of the models with respect to parsimonious data representation is provided, enabling discrimination between candidate models and the biological hypotheses underlying them. We conclude that a previously published model fitted spurious effects in the data. Moreover, the discrimination analysis suggests that the reported data does not support the conclusion that a desensitization mechanism leads to the rapid attenuation of Hog1 signaling in the Sho1 branch of the HOG pathway. The data rather supports a model where an integrator feedback shuts down the pathway. This conclusion is also supported by dedicated experiments that can exclusively be predicted by those models including an integrator feedback.modelMaGe is an open source project and is distributed under the Gnu General Public License (GPL) and is available from http://modelmage.org.     

In vivo analysis of inhibitory synaptic inputs and rebounds in deep cerebellar nuclear neurons

Neuronal function depends on the properties of the synaptic inputs the neuron receive and on its intrinsic responsive properties. However, the conditions for synaptic integration and activation of intrinsic responses may to a large extent depend on the level of background synaptic input. In this respect, the deep cerebellar nuclear (DCN) neurons are of particular interest: they feature a massive background synaptic input and an intrinsic, postinhibitory rebound depolarization with profound effects on the synaptic integration. Using in vivo whole cell patch clamp recordings from DCN cells in the cat, we find that the background of Purkinje cell input provides a tonic inhibitory synaptic noise in the DCN cell. Under these conditions, individual Purkinje cells appear to have a near negligible influence on the DCN cell and clear-cut rebounds are difficult to induce. Peripheral input that drives the simple spike output of the afferent PCs to the DCN cell generates a relatively strong DCN cell inhibition, but do not induce rebounds. In contrast, synchronized climbing fiber activation, which leads to a synchronized input from a large number of Purkinje cells, can induce profound rebound responses. In light of what is known about climbing fiber activation under behaviour, the present findings suggest that DCN cell rebound responses may be an unusual event. Our results also suggest that cortical modulation of DCN cell output require a substantial co-modulation of a large proportion of the PCs that innervate the cell, which is a possible rationale for the existence of the cerebellar microcomplex.     

Genetic Variation in the Epidermal Transglutaminase Genes Is Not Associated with Atopic Dermatitis

Background

Atopic dermatitis (AD) is a common chronic inflammatory skin disorder where epidermal barrier dysfunction is a major factor in the pathogenesis. The identification of AD susceptibility genes related to barrier dysfunction is therefore of importance. The epidermal transglutaminases (TGM1, TGM3 and TGM5) encodes essential cross-linking enzymes in the epidermis.

Objective

To determine whether genetic variability in the epidermal transglutaminases contributes to AD susceptibility.

Methods

Forty-seven single nucleotide polymorphisms (SNPs) in the TGM1, TGM3 and TGM5 gene region were tested for genetic association with AD, independently and in relation to FLG genotype, using a pedigree disequilibrium test (PDT) in a Swedish material consisting of 1753 individuals from 539 families. In addition, a German case-control material, consisting of 533 AD cases and 1996 controls, was used for in silico analysis of the epidermal TGM regions. Gene expression of the TGM1, TGM3 and TGM5 gene was investigated by relative quantification with Real Time PCR (qRT-PCR). Immunohistochemical (IHC) analysis was performed to detect TG1, TG3 and TG5 protein expression in the skin of patients and healthy controls.

Results

PDT analysis identified a significant association between the TGM1 SNP rs941505 and AD with allergen-specific IgE in the Swedish AD family material. However, the association was not replicated in the German case-control material. No significant association was detected for analyzed SNPs in relation to FLG genotype. TG1, TG3 and TG5 protein expression was detected in AD skin and a significantly increased TGM3 mRNA expression was observed in lesional skin by qRT-PCR.

Conclusion

Although TGM1 and TGM3 may be differentially expressed in AD skin, the results from the genetic analysis suggest that genetic variation in the epidermal transglutaminases is not an important factor in AD susceptibility.