Ca2+-binding protein 2 inhibits Ca2+-channel inactivation in mouse inner hair cells

Ca2+ channels mediate excitation-secretion coupling and show little inactivation at sensory ribbon synapses, enabling reliable synaptic information transfer during sustained stimulation. Studies of Ca2+-channel complexes in HEK293 cells indicated that Ca2+-binding proteins (CaBPs) antagonize their calmodulin-dependent inactivation. Although human mutations affecting CABP2 were shown to cause hearing impairment, the role of CaBP2 in auditory function and the precise disease mechanism remained enigmatic. Here, we disrupted CaBP2 in mice and showed that CaBP2 is required for sound encoding at inner hair cell synapses, likely by suppressing Ca2+-channel inactivation. We propose that the number of activatable Ca2+ channels at the active zone is reduced when CaBP2 is lacking, as is likely the case with the newly described human CABP2 mutation

Effect of acute exercise on uncoupling protein 3 is a fat metabolism-mediated effect

Department of Human Biology, Maastricht University, 6200 MD Maastricht, The Netherlands. [email protected] Human and rodent uncoupling protein (UCP)3 mRNA is upregulated after acute exercise. Moreover, exercise increases plasma levels of free fatty acid (FFA), which are also known to upregulate UCP3. We investigated whether the upregulation of UCP3 after exercise is an effect of exercise per se or an effect of FFA levels or substrate oxidation. Seven healthy untrained men [age: 22.7 +/- 0.6 yr; body mass index: 23.8 +/- 1.0 kg/m(2); maximal O2 uptake (VO2 max): 3,852 +/- 211 ml/min] exercised at 50% VO2 max for 2 h and then rested for 4 h. Muscle biopsies and blood samples were taken before and immediately after 2 h of exercise and 1 and 4 h in the postexercise period. To modulate plasma FFA levels and fat/glucose oxidation, the experiment was performed two times, one time with glucose ingestion and one time while fasting. UCP3 mRNA and UCP3 protein were determined by RT-competitive PCR and Western blot. In the fasted state, plasma FFA levels significantly increased (P < 0.0001) during exercise (293 +/- 25 vs. 1,050 +/- 127 micromol/l), whereas they were unchanged after glucose ingestion (335 +/- 54 vs. 392 +/- 74 micromol/l). Also, fat oxidation was higher after fasting (P < 0.05), whereas glucose oxidation was higher after glucose ingestion (P < 0.05). In the fasted state, UCP3L mRNA expression was increased significantly (P < 0.05) 4 h after exercise (4.6 +/- 1.2 vs. 9.6 +/- 3.3 amol/microg RNA). This increase in UCP3L mRNA expression was prevented by glucose ingestion. Acute exercise had no effect on UCP3 protein levels. In conclusion, we found that acute exercise had no direct effect on UCP3 mRNA expression. Abolishing the commonly observed increase in plasma FFA levels and/or fatty acid oxidation during and after exercise prevents the upregulation of UCP3 after acute exercise. Therefore, the previously observed increase in UCP3 expression appears to be an effect of prolonged elevation of plasma FFA levels and/or increased fatty acid oxidation

Relationship between de novo lipogenesis and serum sex hormone binding globulin in humans

Objective Obesity and liver fat are associated with decreased levels of serum sex hormone binding globulin (SHBG). Laboratory studies suggest that hepatic de novo lipogenesis (DNL) is involved in the downregulation of SHBG synthesis. The aim of the present study was to address the role of DNL on serum SHBG in humans. Design A cross-sectional study examining the association between DNL, measured by stable isotopes, and serum SHBG, stratified by sex. Participants Healthy men (n = 34) and women (n = 21) were combined from two cross-sectional studies. Forty-two per cent of participants had hepatic steatosis, and the majority were overweight (62%) or obese (27%). Results DNL was inversely associated with SHBG in women (beta: -0.015, 95% CI: -0.030; 0.000), but not in men (beta: 0.007, 95% CI: -0.005; 0.019) (p for interaction = .068). Adjustment for study population, age and body mass index did not materially change these results, although statistical significance was lost after adjustment for serum insulin. Conclusions An inverse association between DNL and SHBG may explain the decreased SHBG levels that are observed in obesity, at least in women.Peer reviewe

Effects of dietary macronutrients on liver fat content in adults: a systematic review and meta-analysis of randomized controlled trials

Dietary macronutrient composition may affect hepatic liver content and its associated diseases, but the results from human intervention trials have been equivocal or underpowered. We aimed to assess the effects of dietary macronutrient composition on liver fat content by conducting a systematic review and meta-analysis of randomized controlled trials in adults. Four databases (PubMed, Embase, Web of Science, and COCHRANE Library) were systematically searched for trials with isocaloric diets evaluating the effect of dietary macronutrient composition (energy percentages of fat, carbohydrates, and protein, and their specific types) on liver fat content as assessed by magnetic resonance techniques, computed tomography or liver biopsy. Data on change in liver fat content were pooled by random or fixed-effects meta-analyses and expressed as standardized mean difference (SMD). We included 26 randomized controlled trials providing data for 32 comparisons on dietary macronutrient composition. Replacing dietary fat with carbohydrates did not result in changes in liver fat (12 comparisons, SMD 0.01 (95% CI -0.36; 0.37)). Unsaturated fat as compared with saturated fat reduced liver fat content (4 comparisons, SMD -0.80 (95% CI -1.09; -0.51)). Replacing carbohydrates with protein reduced liver fat content (5 comparisons, SMD -0.33 (95% CI -0.54; -0.12)). Our meta-analyses showed that replacing carbohydrates with total fat on liver fat content was not effective, while replacing carbohydrates with proteins and saturated fat with unsaturated fat was. More well-performed and well-described studies on the effect of types of carbohydrates and proteins on liver fat content are needed, especially studies comparing proteins with fats.Clinical epidemiolog

Serum sex hormone-binding globulin levels are reduced and inversely associated with intrahepatic lipid content and saturated fatty acid fraction in adult patients with glycogen storage disease type 1a

PURPOSE: De novo lipogenesis has been inversely associated with serum sex hormone-binding globulin (SHBG) levels. However, the directionality of this association has remained uncertain. We, therefore, studied individuals with glycogen storage disease type 1a (GSD1a), who are characterized by a genetic defect in glucose-6-phosphatase resulting in increased rates of de novo lipogenesis, to assess the downstream effect on serum SHBG levels. METHODS: A case-control study comparing serum SHBG levels in patients with GSD1a (n = 10) and controls matched for age, sex, and BMI (n = 10). Intrahepatic lipid content and saturated fatty acid fraction were quantified by proton magnetic resonance spectroscopy. RESULTS: Serum SHBG levels were statistically significantly lower in patients with GSD1a compared to the controls (p = 0.041), while intrahepatic lipid content and intrahepatic saturated fatty acid fraction-a marker of de novo lipogenesis-were significantly higher in patients with GSD1a (p = 0.001 and p = 0.019, respectively). In addition, there was a statistically significant, inverse association of intrahepatic lipid content and saturated fatty acid fraction with serum SHBG levels in patients and controls combined (β: - 0.28, 95% CI: - 0.47;- 0.09 and β: - 0.02, 95% CI: - 0.04;- 0.01, respectively). CONCLUSION: Patients with GSD1a, who are characterized by genetically determined higher rates of de novo lipogenesis, have lower serum SHBG levels than controls

Optimized parameter search for large datasets of the regularization parameter and feature selection for ridge regression

In this paper we propose mathematical optimizations to select the optimal regularization parameter for ridge regression using cross-validation. The resulting algorithm is suited for large datasets and the computational cost does not depend on the size of the training set. We extend this algorithm to forward or backward feature selection in which the optimal regularization parameter is selected for each possible feature set. These feature selection algorithms yield solutions with a sparse weight matrix using a quadratic cost on the norm of the weights. A naive approach to optimizing the ridge regression parameter has a computational complexity of the order with the number of applied regularization parameters, the number of folds in the validation set, the number of input features and the number of data samples in the training set. Our implementation has a computational complexity of the order . This computational cost is smaller than that of regression without regularization for large datasets and is independent of the number of applied regularization parameters and the size of the training set. Combined with a feature selection algorithm the algorithm is of complexity and for forward and backward feature selection respectively, with the number of selected features and the number of removed features. This is an order faster than and for the naive implementation, with for large datasets. To show the performance and reduction in computational cost, we apply this technique to train recurrent neural networks using the reservoir computing approach, windowed ridge regression, least-squares support vector machines (LS-SVMs) in primal space using the fixed-size LS-SVM approximation and extreme learning machines

Non-Water-Suppressed 1H MR Spectroscopy with Orientational Prior Knowledge Shows Potential for Separating Intra- and Extramyocellular Lipid Signals in Human Myocardium

Conditions such as type II diabetes are linked with elevated lipid levels in the heart, and significantly increased risk of heart failure; however, metabolic processes underlying the development of cardiac disease in type II diabetes are not fully understood. Here we present a non-invasive method for in vivo investigation of cardiac lipid metabolism: namely, IVS-McPRESS. This technique uses metabolite-cycled, non-water suppressed 1H cardiac magnetic resonance spectroscopy with prospective and retrospective motion correction. High-quality IVS-McPRESS data acquired from healthy volunteers allowed us to investigate the frequency shift of extramyocellular lipid signals, which depends on the myocardial fibre orientation. Assuming consistent voxel positioning relative to myofibres, the myofibre angle with the magnetic field was derived from the voxel orientation. For separation and individual analysis of intra- and extramyocellular lipid signals, the angle myocardial fibres in the spectroscopy voxel take with the magnetic field should be within ±24.5°. Metabolite and lipid concentrations were analysed with respect to BMI. Significant correlations between BMI and unsaturated fatty acids in intramyocellular lipids, and methylene groups in extramyocellular lipids were found. The proposed IVS-McPRESS technique enables non-invasive investigation of cardiac lipid metabolism and may thus be a useful tool to study healthy and pathological conditions

Echo State Property of Deep Reservoir Computing Networks

In the last years, the Reservoir Computing (RC) framework has emerged as a state of-the-art approach for efficient learning in temporal domains. Recently, within the RC context, deep Echo State Network (ESN) models have been proposed. Being composed of a stack of multiple non-linear reservoir layers, deep ESNs potentially allow to exploit the advantages of a hierarchical temporal feature representation at different levels of abstraction, at the same time preserving the training efficiency typical of the RC methodology. In this paper, we generalize to the case of deep architectures the fundamental RC conditions related to the Echo State Property (ESP), based on the study of stability and contractivity of the resulting dynamical system. Besides providing a necessary condition and a sufficient condition for the ESP of layered RC networks, the results of our analysis provide also insights on the nature of the state dynamics in hierarchically organized recurrent models. In particular, we find out that by adding layers to a deep reservoir architecture, the regime of network’s dynamics can only be driven towards (equally or) less stable behaviors. Moreover, our investigation shows the intrinsic ability of temporal dynamics differentiation at the different levels in a deep recurrent architecture, with higher layers in the stack characterized by less contractive dynamics. Such theoretical insights are further supported by experimental results that show the effect of layering in terms of a progressively increased short-term memory capacity of the recurrent models

Estimates of array and pool-construction variance for planning efficient DNA-pooling genome wide association studies

<p>Abstract</p> <p>Background</p> <p>Until recently, genome-wide association studies (GWAS) have been restricted to research groups with the budget necessary to genotype hundreds, if not thousands, of samples. Replacing individual genotyping with genotyping of DNA pools in Phase I of a GWAS has proven successful, and dramatically altered the financial feasibility of this approach. When conducting a pool-based GWAS, how well SNP allele frequency is estimated from a DNA pool will influence a study's power to detect associations. Here we address how to control the variance in allele frequency estimation when DNAs are pooled, and how to plan and conduct the most efficient well-powered pool-based GWAS.</p> <p>Methods</p> <p>By examining the variation in allele frequency estimation on SNP arrays between and within DNA pools we determine how array variance [var(e_array)] and pool-construction variance [var(e_construction)] contribute to the total variance of allele frequency estimation. This information is useful in deciding whether replicate arrays or replicate pools are most useful in reducing variance. Our analysis is based on 27 DNA pools ranging in size from 74 to 446 individual samples, genotyped on a collective total of 128 Illumina beadarrays: 24 1M-Single, 32 1M-Duo, and 72 660-Quad.</p> <p>Results</p> <p>For all three Illumina SNP array types our estimates of var(e_array) were similar, between 3-4 × 10^-4for normalized data. Var(e_construction) accounted for between 20-40% of pooling variance across 27 pools in normalized data.</p> <p>Conclusions</p> <p>We conclude that relative to var(e_array), var(e_construction) is of less importance in reducing the variance in allele frequency estimation from DNA pools; however, our data suggests that on average it may be more important than previously thought. We have prepared a simple online tool, PoolingPlanner (available at <url>http://www.kchew.ca/PoolingPlanner/</url>), which calculates the effective sample size (ESS) of a DNA pool given a range of replicate array values. ESS can be used in a power calculator to perform pool-adjusted calculations. This allows one to quickly calculate the loss of power associated with a pooling experiment to make an informed decision on whether a pool-based GWAS is worth pursuing.</p