Regulation of RhoA Signaling by the cAMP-dependent Phosphorylation of RhoGDIα

RhoA plays a pivotal role in regulating cell shape and movement. Protein kinase A (PKA) inhibits RhoA signaling and thereby induces a characteristic morphological change, cell rounding. This has been considered to result from cAMP-induced phosphorylation of RhoA at Ser-188, which induces a stable RhoA-GTP-RhoGDIα complex and sequesters RhoA to the cytosol. However, few groups have shown RhoA phosphorylation in intact cells. Here we show that phosphorylation of RhoGDIα but not RhoA plays an essential role in the PKA-induced inhibition of RhoA signaling and in the morphological changes using cardiac fibroblasts. The knockdown of RhoGDIα by siRNA blocks cAMP-induced cell rounding, which is recovered by RhoGDIα-WT expression but not when a RhoGDIα-S174A mutant is expressed. PKA phosphorylates RhoGDIα at Ser-174 and the phosphorylation of RhoGDIα is likely to induce the formation of a active RhoA-RhoGDIα complex. Our present results thus reveal a principal molecular mechanism underlying G_s/cAMP-induced cross-talk with G_q/G₁₃/RhoA signaling.     

Development and characterization of 21 polymorphic microsatellite loci in the aphidophagous gall midge, <Emphasis Type="Italic">Aphidoletes aphidimyza</Emphasis> (Diptera: Cecidomyiidae)

We have developed and characterized 21 microsatellite markers in the aphidophagous gall midge Aphidoletes aphidimyza (Rondani) (Diptera: Cecidomyiidae). All 21 loci tested were polymorphic: the number of alleles ranged from 2 to 17. Allelic richness and observed heterozygosities were higher in females than in males. Several loci had no heterozygosity in males, suggesting that the loci were located on sex chromosomes or E-chromosomes, common to cecidomyiids. The high polymorphism detected in this study suggests the markers will be of value in analyzing genetic structure of field populations.     

Structure-activity relationship studies of Bz amide-containing α-GalCer derivatives as natural killer T cell modulators

Abstract

CD1d is a non-polymorphic antigen-presenting glycoprotein that recognizes glycolipids as ligands. Ligands bind to the hydrophobic grooves of CD1d, and the resulting ligand-CD1d complexes activate natural killer T (NKT) cells by means of T cell receptor recognition, leading to the secretion of various cytokines. However, details of the ligand recognition mechanism of a large hydrophobic ligand binding pocket and the relationship between cytokine induction and ligand structure are unclear. We report the synthesis of α-GalCer derivatives containing a Bz amide group having various substituting groups in the ceramide moiety, and the analysis of the structure-activity relationships. The assays reveal that the Bz amide-containing CD1d ligands function as NKT cell modulators displaying Th2 cytokine biasing responses. Furthermore, molecular dynamics simulation studies suggest that the phenyl groups can interact with the aromatic amino acid residues in the lipid binding pocket of CD1d.     

Toward Probabilistic Diagnosis and Understanding of Depression Based on Functional MRI Data Analysis with Logistic Group LASSO

Diagnosis of psychiatric disorders based on brain imaging data is highly desirable in clinical applications. However, a common problem in applying machine learning algorithms is that the number of imaging data dimensions often greatly exceeds the number of available training samples. Furthermore, interpretability of the learned classifier with respect to brain function and anatomy is an important, but non-trivial issue. We propose the use of logistic regression with a least absolute shrinkage and selection operator (LASSO) to capture the most critical input features. In particular, we consider application of group LASSO to select brain areas relevant to diagnosis. An additional advantage of LASSO is its probabilistic output, which allows evaluation of diagnosis certainty. To verify our approach, we obtained semantic and phonological verbal fluency fMRI data from 31 depression patients and 31 control subjects, and compared the performances of group LASSO (gLASSO), and sparse group LASSO (sgLASSO) to those of standard LASSO (sLASSO), Support Vector Machine (SVM), and Random Forest. Over 90% classification accuracy was achieved with gLASSO, sgLASSO, as well as SVM; however, in contrast to SVM, LASSO approaches allow for identification of the most discriminative weights and estimation of prediction reliability. Semantic task data revealed contributions to the classification from left precuneus, left precentral gyrus, left inferior frontal cortex (pars triangularis), and left cerebellum (c rus1). Weights for the phonological task indicated contributions from left inferior frontal operculum, left post central gyrus, left insula, left middle frontal cortex, bilateral middle temporal cortices, bilateral precuneus, left inferior frontal cortex (pars triangularis), and left precentral gyrus. The distribution of normalized odds ratios further showed, that predictions with absolute odds ratios higher than 0.2 could be regarded as certain.     

Mitigation of copper toxicity by DNA oligomers in green paramecia

Impact of transition metals which catalyze the generation of reactive oxygen species (ROS), on activation of cell death signaling in plant cells have been documented to date. Similarly in green paramecia (Paramecium bursaria), an aquatic protozoan species harboring symbiotic green algae in the cytoplasm, toxicities of various metallic ions have been documented. We have recently examined the effects of double-stranded GC-rich DNA fragments with copper-binding nature and ROS removal catalytic activity as novel plant cell-protecting agents, using the suspension-cultured tobacco cells. Here, we show that above DNA oligomers protect the cells of green paramecia from copper-induced cell death, suggesting that the phenomenon firstly observed in tobacco cells is not limited only within higher plants but it could be universally observable in wider range of organisms.     

Hepatitis C Related Chronic Liver Cirrhosis: Feasibility of Texture Analysis of MR Images for Classification of Fibrosis Stage and Necroinflammatory Activity Grade

Purpose

To assess the feasibility of texture analysis for classifying fibrosis stage and necroinflammatory activity grade in patients with chronic hepatitis C on T2-weighted (T2W), T1-weighted (T1W) and Gd-EOB-DTPA-enhanced hepatocyte-phase (EOB-HP) imaging.

Materials and methods

From April 2008 to June 2012, MR images from 123 patients with pathologically proven chronic hepatitis C were retrospectively analyzed. Texture parameters derived from histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model and wavelet transform methods were estimated with imaging software. Fisher, probability of classification error and average correlation, and mutual information coefficients were used to extract subsets of optimized texture features. Linear discriminant analysis in combination with 1-nearest neighbor classifier (LDA/1-NN) was used for lesion classification. In compliance with the software requirement, classification was performed based on datasets from all patients, the patient group with necroinflammatory activity grade 1, and that with fibrosis stage 4, respectively.

Results

Based on all patient dataset, LDA/1-NN produced misclassification rates of 28.46%, 35.77% and 20.33% for fibrosis staging and 34.15%, 25.20% and 28.46% for necroinflammatory activity grading in T2W, T1W and EOB-HP images. In the patient group with necroinflammatory activity grade 1, LDA/1-NN yielded misclassification rates of 5.00%, 0% and 12.50% for fibrosis staging in T2W, T1W and EOB-HP images respectively. In the patient group with fibrosis stage 4, LDA/1-NN yielded misclassification rates of 5.88%, 12.94% and 11.76% for necroinflammatory activity grading in T2W, T1W and EOB-HP images respectively.

Conclusion

Texture quantitative parameters of MR images facilitate classification of the fibrosis stage as well as necroinflammatory activity grade in chronic hepatitis C, especially after categorizing the input dataset according to the activity or fibrosis degree in order to remove the interference between the fibrosis stage and necroinflammatory activity grade on texture features.     

Efficacy and Safety of Sitagliptin Added to Insulin in Japanese Patients with Type 2 Diabetes: The EDIT Randomized Trial

Aims

To clarify the efficacy and safety of adding sitagliptin to insulin therapy in Japanese patients with suboptimally controlled type 2 diabetes (T2DM).

Study Design and Methods

This was a 24-week, prospective, randomized, open-labeled, controlled trial. Patients with T2DM who were suboptimally controlled despite receiving at least twice daily injection of insulin were enrolled in the study. The patients were randomized to continuation of insulin treatment (Insulin group) or addition of sitagliptin 50 to 100 mg daily to insulin treatment (Ins+Sita group). The primary outcome was change in HbA1c at week 24.

Results

Adding sitagliptin to insulin significantly reduced HbA1c from 7.9 ± 1.0% at baseline to 7.0 ± 0.8% at week 24 (P <0.0001), while there was no significant change in HbA1c in the Insulin group (7.8 ± 0.7% vs. 7.8 ± 1.1%, P = 0.32). The difference in HbA1c reduction between the groups was 0.9% (95% confidence interval, 0.4 to 1.5, P = 0.01). There was no significant weight gain in either group. Incidence of hypoglycemia was significantly reduced in the Ins+Sita group compared with the Insulin group. Treatment satisfaction was improved in the Ins+Sita group. Baseline HbA1c level and beta cell function were associated with the magnitude of reduction in HbA1c in the Ins+Sita group.

Conclusion

Adding sitagliptin to insulin reduced HbA1c without weight gain or increase in hypoglycemia, and improved treatment satisfaction in Japanese patients with T2DM who were suboptimally controlled despite at least twice daily injection of insulin.

Trial Registration

The University Hospital Medical Information Network (UMIN) Clinical Trials Registry UMIN000004678     

PGC-1α-mediated changes in phospholipid profiles of exercise-trained skeletal muscle

Exercise training influences phospholipid fatty acid composition in skeletal muscle and these changes are associated with physiological phenotypes; however, the molecular mechanism of this influence on compositional changes is poorly understood. Peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), a nuclear receptor coactivator, promotes mitochondrial biogenesis, the fiber-type switch to oxidative fibers, and angiogenesis in skeletal muscle. Because exercise training induces these adaptations, together with increased PGC-1α, PGC-1α may contribute to the exercise-mediated change in phospholipid fatty acid composition. To determine the role of PGC-1α, we performed lipidomic analyses of skeletal muscle from genetically modified mice that overexpress PGC-1α in skeletal muscle or that carry KO alleles of PGC-1α. We found that PGC-1α affected lipid profiles in skeletal muscle and increased several phospholipid species in glycolytic muscle, namely phosphatidylcholine (PC) (18:0/22:6) and phosphatidylethanolamine (PE) (18:0/22:6). We also found that exercise training increased PC (18:0/22:6) and PE (18:0/22:6) in glycolytic muscle and that PGC-1α was required for these alterations. Because phospholipid fatty acid composition influences cell permeability and receptor stability at the cell membrane, these phospholipids may contribute to exercise training-mediated functional changes in the skeletal muscle.