Ultrasound complex enzymes assisted extraction and biochemical activities of polysaccharides from Epimedium leaves

Ultrasound complex enzymes (UCE) were used to extract crude polysaccharides (CEP) from Epimedium leaves. The response surface methodology (RSM) based on Box–Behnken Design (BBD) was employed to further optimize the extraction conditions. The optimum conditions were: complex enzyme dose of 50, 250, 200, 100 U/g (papain, pectase, cellulase and α-amylase), extraction temperature 46.8 °C, ultrasound time 42.3 min, pH 4.28, and ultrasound power 311 W. Under these conditions, the experimental yield of CEP was 5.98%, which was well matched with the value predicted by the model. Three major polysaccharide fractions (EP-1, EP-2 and EP-3) were obtained by ultrafiltration, chromatography on DEAE-Sepharose Fast Flow and Sephadex G-100 column. Their chemical–physical characteristics were investigated using chemical methods, gas chromatography (GC) and high-performance size-exclusion chromatography (HPSEC). Biochemical characterization of the polysaccharide fractions was evaluated on the basis of free radical scavenging, ferric-reducing antioxidant power (FRAP), lymphocyte proliferation, macrophage phagocytosis and NK cell activity assay in vitro. The results showed that all fractions contained higher galacturonic acid (GalA) and galactose (Gal), possessed considerable antioxidant activity, and could promote proliferation of splenic lymphocytes, activate macrophages and enhance NK cells’ activity. UCE as a more effective and environment-friendly technique could be employed for extracting active ingredients from plant materials, extensively applied in pharmaceutical and food industries.     

Dietary Intake of Whole and Refined Grain Breakfast Cereals and Weight Gain in Men

Objective: Prospective studies have suggested that substituting whole grain for refined grain products may lower the risk of overweight and obesity. Breakfast cereal intake is a major source of whole and refined grains and has also been associated with having a lower BMI. The aim of this study was to prospectively assess the association between whole and refined grain breakfast cereal intakes and risk of overweight (BMI ≥ 25 kg/m²) and weight gain. Research Methods and Procedures: We examined 17, 881 U.S. male physicians 40 to 84 years of age in 1982 who were free of cardiovascular disease, diabetes mellitus, and cancer at baseline and reported measures of breakfast cereal intake, weight, and height. Results: Over 8 and 13 years of follow‐up, respectively, men who consumed breakfast cereal, regardless of type, consistently weighed less than those who consumed breakfast cereals less often (p value for trend = 0.01). Whole and refined grain breakfast cereal intake was inversely associated with body weight gain over 8 years, after adjustment for age, smoking, baseline BMI, alcohol intake, physical activity, hypertension, high cholesterol, and use of multivitamins. Compared with men who rarely or never consumed breakfast cereals, those who consumed ≥1 serving/d of breakfast cereals were 22% and 12% less likely to become overweight during follow‐up periods of 8 and 13 years (relative risk, 0.78 and 0.88; 95% confidence interval, 0.67 to 0.91 and 0.76 to 1.00, respectively). Discussion: BMI and weight gain were inversely associated with intake of breakfast cereals, independently of other risk factors.     

Multiparametric evaluation of hindlimb ischemia using time‐series indocyanine green fluorescence imaging

Peripheral arterial disease (PAD) can further cause lower limb ischemia. Quantitative evaluation of the vascular perfusion in the ischemic limb contributes to diagnosis of PAD and preclinical development of new drug. In vivo time‐series indocyanine green (ICG) fluorescence imaging can noninvasively monitor blood flow and has a deep tissue penetration. The perfusion rate estimated from the time‐series ICG images is not enough for the evaluation of hindlimb ischemia. The information relevant to the vascular density is also important, because angiogenesis is an essential mechanism for post‐ischemic recovery. In this paper, a multiparametric evaluation method is proposed for simultaneous estimation of multiple vascular perfusion parameters, including not only the perfusion rate but also the vascular perfusion density and the time‐varying ICG concentration in veins. The target method is based on a mathematical model of ICG pharmacokinetics in the mouse hindlimb. The regression analysis performed on the time‐series ICG images obtained from a dynamic reflectance fluorescence imaging system. The results demonstrate that the estimated multiple parameters are effective to quantitatively evaluate the vascular perfusion and distinguish hypo‐perfused tissues from well‐perfused tissues in the mouse hindlimb. The proposed multiparametric evaluation method could be useful for PAD diagnosis.

The estimated perfusion rate and vascular perfusion density maps (left) and the time‐varying ICG concentration in veins of the ankle region (right) of the normal and ischemic hindlimbs.     

1株H6N6亚型禽流感病毒分子遗传特性分析

本研究采用无特定病原体(specific pathogen free,SPF)鸡胚,从某活禽市场环境中分离出1株H6N6亚型禽流感病毒(A/environment/Zhenjiang/zj18/2013,en/zj18)。通过二代测序技术进行全基因组测序,通过BLASTn 进行同源性检索,并采用MEGA5.0软件构建系统发生树。基因进化树分析表明,分离株en/zj18的所有8个基因节段(PB2、PB1、PA、HA、NP、NA、M和NS)均与近年来中国华东地区流行的H6N6亚型禽流感病毒的相应基因位于同一进化分支,与参考株的核苷酸同源性达96.7%～99.6%。分离株en/zj18的HA蛋白裂解位点为PQIETR↓GL,是低致病性禽流感病毒的分子特征。HA蛋白上关键受体结合位点190和228位(按H3亚型的HA蛋白序列排序)氨基酸分别是E和G,理论上更易与α2,3-半乳糖苷唾液酸受体结合。结果提示,需加强活禽市场禽流感病毒的持续监测,从而为有效应对禽流感病毒对公共卫生的持续威胁提供科学依据。     

远缘杂交在转移有益基因创造小麦新种质中的潜力

本文概述了小麦远缘杂交技术的发展以及这些技术的应用对以染色体易位方式转移有益基因到普通小麦中的影响。通过对小麦远缘杂交技术的总结得出,普通小麦由于本身的多倍性,对导入的外源基因具有较强的调节能力,是适宜外源有益基因导入的良好受体。而以染色体易位方式转移有益基因是创造小麦新种质的有效方法之一,许多研究也表明以染色体易位导入的外源有益基因更利于表达。近几年,随着细胞遗传学以及其它生物技术的发展,对小麦族进化途径和染色体间的亲缘关系进一步明确,从而更便于进行易位导入的技术选择,也使得染色体易位鉴定方法更趋完善。现在已有更良好的外源导入的工具和方法,使多基因控制的外源优良性状导入成为可能。在小麦远缘杂交中染色体易位所具有的上述优势,在育种实践中逐步显示出来,为开拓小麦种质资源开创了一条新的途径。     

Insulin Resistance,Hyperleptinemia, and Obstructive Sleep Apnea in Launois‐Bensaude Syndrome

Objective: Launois‐Bensaude Syndrome (LBS) is a very rare cause of obesity, characterized by a symmetrical accumulation of a very large number of lipomata in different regions of the body, excluding the face, the forearms, and the shanks. Obesity is known to be closely associated with insulin resistance, hyperleptinemia, and obstructive sleep apnea (OSA). We were interested in studying whether these conditions are also present in patients with obesity due to LBS with a similar frequency as in patients with “simple” truncal obesity. Research Methods and Procedures: We performed polysomnography and hyperinsulinemic euglycemic clamp studies and measured serum leptin in three patients with LBS and in six patients with “simple” truncal obesity, matched for sex and body mass index (LBS group, 36.39 kg/m²; controls, 35.82 kg/m²). Results: Polysomnography revealed severe OSA in one LBS patient with marked “horsecollar lipomata.” In the other LBS patients, no OSA could be demonstrated. The leptin levels of the two groups were comparable (LBS group, 36.39 μg/liter; controls, 37.18 μg/liter) and the insulin responsiveness index was also comparable in the two groups (LBS group, 3.47 μmol/kg · minute; controls, 3.79 μmol/kg · minute). Discussion: Patients with LBS demonstrated similar metabolic features in terms of insulin sensitivity and hyperleptinemia as patients with “simple” truncal obesity. LBS is not strictly associated with OSA.     

Phosphoproteomics identified Endofin, DCBLD2, and KIAA0582 as novel tyrosine phosphorylation targets of EGF signaling and Iressa in human cancer cells

With the completion of the human genome project, analysis of enriched phosphotyrosyl proteins from epidermal growth factor (EGF)-induced phosphotyrosine proteome permits the identification of novel downstream substrates of the EGF receptor (EGFR). Using cICAT-based LC-MS/MS method, we identified and relatively quantified the tyrosine phosphorylation levels of 21 proteins between control and EGF-treated A431 human cervical cancer cells. Of these, Endofin, DCBLD2, and KIAA0582 were validated to be novel tyrosine-phosphorylation targets of EGF signaling and Iressa, a highly selective inhibitor of EGFR. In addition, EGFR activity was shown to be necessary for EGF-induced localization of Endofin, an FYVE domain-containing protein regulated by phosphoinositol lipid and engaged in endosome-mediated receptor modulation. Although several groups have conducted phosphoproteomics of EGF signaling in recent years, our study is the first to identify and validate Endofin, DCBLD2, and KIAA0582 as part of a complex EGF phosphotyrosine signaling network. These novel data will provide new insights into the complex EGF signaling and may have implications on target-directed cancer therapeutics.     

Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure

The identification of drug-target interactions has great significance for pharmaceutical scientific research. Since traditional experimental methods identifying drug-target interactions is costly and time-consuming, the use of machine learning methods to predict potential drug-target interactions has attracted widespread attention. This paper presents a novel drug-target interactions prediction method called LRF-DTIs. Firstly, the pseudo-position specific scoring matrix (PsePSSM) and FP2 molecular fingerprinting were used to extract the features of drug-target. Secondly, using Lasso to reduce the dimension of the extracted feature information and then the Synthetic Minority Oversampling Technique (SMOTE) method was used to deal with unbalanced data. Finally, the processed feature vectors were input into a random forest (RF) classifier to predict drug-target interactions. Through 10 trials of 5-fold cross-validation, the overall prediction accuracies on the enzyme, ion channel (IC), G-protein-coupled receptor (GPCR) and nuclear receptor (NR) datasets reached 98.09%, 97.32%, 95.69%, and 94.88%, respectively, and compared with other prediction methods. In addition, we have tested and verified that our method not only could be applied to predict the new interactions but also could obtain a satisfactory result on the new dataset. All the experimental results indicate that our method can significantly improve the prediction accuracy of drug-target interactions and play a vital role in the new drug research and target protein development. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/LRF-DTIs/ for academic use.