Small G Rac1 is involved in replication cycle of dengue serotype 2 virus in EAhy926 cells via the regulation of actin cytoskeleton

Bleeding is a clinical characteristic of severe dengue and may be due to increased vascular permeability. However, the pathogenesis of severe dengue remains unclear. In this study, we showed that the Rac1-microfilament signal pathway was involved in the process of DENV serotype 2 (DENV2) infection in EAhy926 cells. DENV2 infection induced dynamic changes in actin organization, and treatment with Cytochalasin D or Jasplakinolide disrupted microfilament dynamics, reduced DENV2 entry, and inhibited DENV2 assembly and maturation. Rac1 activities decreased during the early phase and gradually increased by the late phase of infection. Expression of the dominant-negative form of Rac1 promoted DENV2 entry but inhibited viral assembly, maturation and release. Our findings demonstrated that Rac1 plays an important role in the DENV2 life cycle by regulating actin reorganization in EAhy926 cells. This finding provides further insight into the pathogenesis of severe dengue.     

Functional role of metalloproteins in genome stability

Cells contain a large number of metalloproteins that commonly harbor at least one metal ion cofactor. In metalloproteins, metal ions are usually coordinated by oxygen, sulfur, or nitrogen centers belonging to amino acid residues in the protein. The presence of the metal ion in metalloproteins allows them to take part in diverse biological processes, such as genome stability, metabolic catalysis, and cell cycle progression. Clinically, alteration of the function of metalloproteins in mammals is genetically associated with diseases characterized by DNA damage and repair defects. The present review focuses on the current perspectives of metal ion homeostasis in different organisms and summarizes the most recent understanding on magnesium, copper, iron, and manganese-containing proteins and their functional involvement in the maintenance of genome stability.     

Classification of Chinese herbs based on the cluster analysis of delayed luminescence

Traditional Chinese material medica are an important component of the Chinese pharmacopeia. According to the traditional Chinese medicinal concept, Chinese herbal medicines are classified into different categories based on their therapeutic effects, however, the bioactive principles cannot be solely explained by chemical analysis. The aim of this study is to classify different Chinese herbs based on their therapeutic effects by using delayed luminescence (DL). The DL of 56 Chinese herbs was measured using an ultra‐sensitive luminescence detection system. The different DL parameters were used to classify Chinese herbs according to a hierarchical cluster analysis. The samples were divided into two groups based on their DL kinetic parameters. Interestingly, the DL classification results were quite consistent with classification according to the Chinese medicinal concepts of ‘cold’ and ‘heat’ properties. In this paper, we show for the first time that by using DL technology, it is possible to classify Chinese herbs according to the Chinese medicinal concept and it may even be possible to predict their therapeutic properties. Copyright © 2015 John Wiley & Sons, Ltd.     

Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies

False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days.     

Plasma lipidomic profiling in patients with rheumatoid arthritis

Introduction

Rheumatoid arthritis (RA) is linked to increased cardiovascular morbidity and mortality, not completely explained by traditional risk factors. Importantly, the increased risk occurs despite lower levels of total and low-density lipoprotein cholesterol. Whilst systemic inflammation may be a factor, it is possible that changes in individual lipid species contribute to the increased cardiovascular risk.

Objectives

In the present study, we characterized plasma lipidomic profiles in patients with RA in comparison with healthy controls.

Methods

Patients with RA (n = 32) and age- and gender-matched healthy volunteers (n = 84) were recruited. Fasting plasma lipid profiles were measured using electrospray-ionisation tandem mass spectrometry. 24 lipid classes and subclasses were measured.

Results

Patients with RA had normal total, low-density lipoprotein and high-density lipoprotein cholesterol, but higher triglycerides than controls. Five lipid classes (dihydroceramides, alkylphosphatidylethanolamine, alkenylphosphatidylethanolamine, lysophosphatidylinositol, phosphatidylserine) differed between patients with RA and controls. Then we measured 36 lipid species within these 5 classes and found that 11 lipid species were different between patients with RA and controls. Three lipid classes (dihydroceramides, lysophosphatidylinositol, phosphatidylserine) and 10 lipid species remained significantly associated with RA after adjusting for age, sex, body mass index, current smoking, systolic blood pressure and anti-hypertensive treatment in a binary logistic regression model.

Conclusion

This study has identified lipid alterations in RA. These alterations of lipids warrant further investigation as they may be associated with accelerated atherosclerosis and joint inflammation in patient with RA.

     

Metabolomic study of the bone trabecula of osteonecrosis femoral head patients based on UPLC–MS/MS

The severity and/or progression of osteonecrosis of the femoral head (ONFH) are commonly assessed by radiography, nuclear magnetic resonance image which aren’t invariably correlated to severity of disease and may be disturbed by other factors. Consequently, exploring the novel biochemical signatures of ONFH may be beneficial for diagnosing and understanding this disease. In this work, a bone trabecula metabolomics was undertaken to determine the expression pattern of low molecular mass metabolites in patients of femoral head necrosis based on the ultra-performance liquid chromatography/time-of-flight tandem mass spectrometry (UPLC/TOF MS/MS). Histological study showed that necrotic bone was characterized by necrosis, fibrosis and lacuna, but adjacent “normal” bone was pathologically normal. Principal component analysis in combination with orthogonal partial least-squares discrimination analysis was used to find out changed metabolites. MS/MS was used to speculate the corresponding molecule. Both osteonecrotic bone trabecula (ONBT) and adjacent “normal” bone trabecula (ANBT) showed higher levels of amino acids, such as proline, arginine, glutamine, dipeptides and lower levels of antioxidants. Most disrupted lipids, such as fatty acid esters, glycerophospholipids, sphingolipids, were found in osteonecrotic zone. The area under the receiver operating characteristic curve of combinational biomarkers (d-arginine, l-proline, l-carnitine, inosine) in ONBT and ANBT was 0.996 and 0.950, respectively. Our findings might provide a significant insight to understand the metabolic mechanism and diagnosis biomarkers of ONFH in the future.