Alogliptin ameliorates postprandial lipemia and postprandial endothelial dysfunction in non- diabetic subjects: a preliminary report

Background

Osteoprotegerin is a member of the tumor necrosis factor-related family and inhibits RANK stimulation of osteoclast formation as a soluble decoy receptor. The goal of this study was to determine the relationship of serum osteoprotegerin with vascular calcification in patients with type 2 diabetes.

Methods

The subjects were 124 patients with type 2 diabetes mellitus, including 88 males and 36 females with a mean (± SD) age of 65.6 ± 8.2 years old. Serum levels of osteoprotegerin, osteocalcin, fibroblast growth factor 23 (FGF23), 25-hydroxyvitamin D3 and adiponectin were measured by ELISA. Vascular calcification in the cervical artery was examined by ultrasound sonography. The subjects were divided into 4 quartiles depending on serum osteoprotegerin levels.

Results

Vascular calcification was significantly higher in the 4th quartile and significantly lower in the 1st quartile of serum osteoprotegerin levels, compared to other quartiles. There were no differences in serum osteoprotegerin and vascular calcification among patients with different stages of diabetic nephropathy, but serum FGF23 levels were elevated in those with stage 4 diabetic nephropathy. Simple regression analysis showed that serum osteoprotegerin levels had significant positive correlations with age, systolic blood pressure and serum adiponectin levels, and significant negative correlations with BMI and serum 25-hydroxyvitamin D3.

Conclusions

These findings suggest that elevated serum osteoprotegerin may be involved in vascular calcification independently of progression of diabetic nephropathy in patients with type 2 diabetes.     

Gephyromycinifex aptenodytis gen. nov., sp. nov., isolated from gut of Antarctic emperor penguin Aptenodytes forsteri

Antonie van Leeuwenhoek - A novel actinobacterium NJES-13T was isolated from the gut of Antarctic emperor penguin Aptenodytes forsteri. The new isolate produces bioactive gephyromycin metabolites...     

Patterns of bilateral asymmetry and allometry in Late Devonian Polygnathus conodonts

Conodont animals were early jawless vertebrates equipped with a feeding apparatus composed of several tooth‐like elements. The P1 elements, at the rear of the apparatus, were characterized by a robust shape and rapid morphological evolution. Occlusion occurred between paired right and left P1 elements, occasioning some bilateral asymmetry, which, together with allometric growth, may partially obliterate the temporal differences. The present study aims to disentangle these different components of morphological variation in Late Devonian Polygnathus P1 conodont elements. An extensive 2D geometric morphometric analysis of the platform shape was performed through the Famennian record of two outcrops. This analysis was completed by a 3D study on a subset of conodont elements. The 2D and 3D morphometric quantifications provided highly congruent results, showing that the 2D shape constitutes a good approximation of the element geometry. The 3D analysis delivered further insights into the relationship between the geometry of the elements and the constraints related to occlusion. The 2D analysis allowed a quantitative assessment of the variation among species and through time. Allometry and bilateral asymmetry were differently expressed depending on the species considered, suggesting that constraints imposed on pairing by the morphology of the elements varied even among related species. The within‐species variation was so important that it largely obliterated temporal trends; a relationship of Polygnathus shape and conodont biofacies variations through the Famennian nevertheless suggested an evolution driven by ecological interactions between conodont genera.     

Shorter leukocyte telomere length is associated with severity of COVID-19 infection.

The infection by COVID-19 is a serious global public health problem. An efficient way to improve this disease's clinical management would be to characterize patients at higher risk of progressing to critically severe infection using prognostic biomarkers. The telomere length could be used for this purpose. Telomeres are responsible for controlling the number of maximum cell divisions. The telomere length is a biomarker of aging and several diseases. We aimed to compare leukocyte telomere length (LTL) between patients without COVID-19 and patients with different clinical severity of the infection. Were included 53 patients who underwent SARS-CoV-2 PCR divided in four groups. The first group was composed by patients with a negative diagnosis for COVID-19 (n = 12). The other three groups consisted of patients with a confirmed diagnosis of COVID-19 divided according to the severity of the disease: mild (n = 15), moderate (n = 17) and severe (n = 9). The LTL was determined by Q-PCR. The severe group had the shortest LTL, followed by the moderate group. The negative and mild groups showed no differences. There is an increase of patients with hypertension (p = 0.0099) and diabetes (p = 0.0067) in moderate and severe groups. Severe group was composed by older patients in comparison with the other three groups (p = 0.0083). Regarding sex, there was no significant difference between groups (p = 0.6279). In an ordinal regression model, only LTL and diabetes were significantly associated with disease severity. Shorter telomere length was significantly associated with the severity of COVID-19 infection, which can be useful as a biomarker or to better understand the SARS-CoV-2 pathophysiology.     

DROP: an SVM domain linker predictor trained with optimal features selected by random forest

MOTIVATION: Biologically important proteins are often large, multidomain proteins, which are difficult to characterize by high-throughput experimental methods. Efficient domain/boundary predictions are thus increasingly required in diverse area of proteomics research for computationally dissecting proteins into readily analyzable domains. RESULTS: We constructed a support vector machine (SVM)-based domain linker predictor, DROP (Domain linker pRediction using OPtimal features), which was trained with 25 optimal features. The optimal combination of features was identified from a set of 3000 features using a random forest algorithm complemented with a stepwise feature selection. DROP demonstrated a prediction sensitivity and precision of 41.3 and 49.4%, respectively. These values were over 19.9% higher than those of control SVM predictors trained with non-optimized features, strongly suggesting the efficiency of our feature selection method. In addition, the mean NDO-Score of DROP for predicting novel domains in seven CASP8 FM multidomain proteins was 0.760, which was higher than any of the 12 published CASP8 DP servers. Overall, these results indicate that the SVM prediction of domain linkers can be improved by identifying optimal features that best distinguish linker from non-linker regions.     

GRIPDB - G protein coupled Receptor Interaction Partners DataBase

The G protein Coupled Receptor (GPCR) superfamily is one of the most important pharmaceutical targets. Studies of GPCRs have long been performed under the assumption that GPCRs function as monomers. However, recent studies have revealed that many GPCRs function as homo- and/or hetero-dimers or higher-order oligomeric molecular complexes. As a result, information about GPCR oligomerization is rapidly accumulating, although the molecular mechanisms of oligomerization are not fully understood. A comprehensive collection of information about oligomerization would accelerate investigations of the molecular mechanisms of GPCRs' oligomerization and involvement in signaling. Hence, we have developed a database, G protein coupled Receptor Interaction Partners DataBase (GRIPDB), which provides information about GPCR oligomerization. The entries in the database are divided into two sections: (I) Experiment Information section and (II) Prediction Information section. The Experiment Information section contains (I-i) experimentally indentified GPCR oligomers and their annotations, and (I-ii) experimentally suggested interfaces for the oligomerization. Since the number of experimentally suggested interfaces is limited, the entries in the Prediction Information section have been introduced to provide information about the oligomerization interfaces predicted by our computational method. The experimentally suggested or computationally predicted interfaces are displayed by 3D graphics, using GPCRs with available coordinates. The information in the GRIPDB, especially that about the interfaces, is useful to investigate the molecular mechanisms of signal transduction via GPCR oligomerization. The GRIPDB is available on the web at the following URL: http://grip.cbrc.jp/GDB/index.html .