Regiospecific analysis of Mono and Diglycerides in Glycerolysis products by GC x GC TOF-MS.

Comprehensive bidimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOF-MS) was used for the characterization of regiospecific mono- and diglycerides (MG-DG) content in the glycerolysis products derived from five different lipids included lard (LA), sun flower seed oil (SF), corn oil (CO), butter (BU), and palm oil (PA). The combination of fast and high temperature non-orthogonal column set namely DB17ht (6 m × 0.10 mm × 0.10 μm) as the primary column and SLB-5 ms (60 cm × 0.10 mm × 0.10 μm) as the secondary column was applied in this work. System configuration involved high oven ramp temperature to obtain precise mass spectral identification and highest effluent’s resolution. 3-Monopalmitoyl-sn-glycerol (MG 3-C16) was the highest concentration in LA, BU and PA while monostearoyl-sn-glycerol (MG C18) in CO and 1,3-dilinoleol-rac-glycerol (DG C18:2c) in SF. Principal component analysis accounted 82% of variance using combination of PC1 and PC2. The presence of monostearoyl-sn-glycerol (MG C18), 3-Monopalmitoyl-sn-glycerol (MG 3-C16), 1,3-dilinoleol-rac-glycerol (DG C18:2c), 1,3-dipalmitoyl-glycerol (DG 1,3-C16), and 1,3-dielaidin (DG C18:1t) caused differentiation of the samples tested

Effect of strain rate on yielding strength of a Zr-based bulk metallic glass

Uniaxial tension and compression experiments were performed on a typical Zr52.5Cu17.9N44.6Al10Ti5 (Vit 105) bulk metallic glass over a wide range of strain rates at room temperature. It is found that the strain rate effect of the yielding strength will change from insensitive to negative with increasing strain rate above a critical value. This phenomenon can be quantitatively described by a modified cooperative-shear model of shear transformation zones that takes the adiabatic temperature rise into account. The model predicts well the present and other experimental data

INFOGEST static in vitro simulation of gastrointestinal food digestion

peer-reviewedSupplementary information is available at http://dx.doi.org/10.1038/s41596-018-0119-1 or https://www.nature.com/articles/s41596-018-0119-1#Sec45.Developing a mechanistic understanding of the impact of food structure and composition on human health has increasingly involved simulating digestion in the upper gastrointestinal tract. These simulations have used a wide range of different conditions that often have very little physiological relevance, and this impedes the meaningful comparison of results. The standardized protocol presented here is based on an international consensus developed by the COST INFOGEST network. The method is designed to be used with standard laboratory equipment and requires limited experience to encourage a wide range of researchers to adopt it. It is a static digestion method that uses constant ratios of meal to digestive fluids and a constant pH for each step of digestion. This makes the method simple to use but not suitable for simulating digestion kinetics. Using this method, food samples are subjected to sequential oral, gastric and intestinal digestion while parameters such as electrolytes, enzymes, bile, dilution, pH and time of digestion are based on available physiological data. This amended and improved digestion method (INFOGEST 2.0) avoids challenges associated with the original method, such as the inclusion of the oral phase and the use of gastric lipase. The method can be used to assess the endpoints resulting from digestion of foods by analyzing the digestion products (e.g., peptides/amino acids, fatty acids, simple sugars) and evaluating the release of micronutrients from the food matrix. The whole protocol can be completed in ~7 d, including ~5 d required for the determination of enzyme activities.COST action FA1005 INFOGEST (http://www.cost-infogest.eu/ ) is acknowledged for providing funding for travel, meetings and conferences (2011-2015). The French National Institute for Agricultural Research (INRA, www.inra.fr) is acknowledged for their continuous support of the INFOGEST network by organising and co-funding the International Conference on Food Digestion and workgroup meeting

Variants in genes encoding small GTPases and association with epithelial ovarian cancer susceptibility

Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer mortality in American women. Normal ovarian physiology is intricately connected to small GTP binding proteins of the Ras superfamily (Ras, Rho, Rab, Arf, and Ran) which govern processes such as signal transduction, cell proliferation, cell motility, and vesicle transport. We hypothesized that common germline variation in genes encoding small GTPases is associated with EOC risk. We investigated 322 variants in 88 small GTPase genes in germline DNA of 18,736 EOC patients and 26,138 controls of European ancestry using a custom genotype array and logistic regression fitting log-additive models. Functional annotation was used to identify biofeatures and expression quantitative trait loci that intersect with risk variants. One variant, ARHGEF10L (Rho guanine nucleotide exchange factor 10 like) rs2256787, was associated with increased endometrioid EOC risk (OR=1.33, p=4.46 x 10-6). Other variants of interest included another in ARHGEF10L, rs10788679, which was associated with invasive serous EOC risk (OR=1.07, p=0.00026) and two variants in AKAP6 (A-kinase anchoring protein 6) which were associated with risk of invasive EOC (rs1955513, OR=0.90, p=0.00033; rs927062, OR =0.94, p=0.00059). Functional annotation revealed that the two ARHGEF10L variants were located in super-enhancer regions and that AKAP6 rs927062 was associated with expression of GTPase gene ARHGAP5 (Rho GTPase activating protein 5). Inherited variants in ARHGEF10L and AKAP6, with potential transcriptional regulatory function and association with EOC risk, warrant investigation in independent EOC study populations

A transcriptome-wide association study among 97,898 women to identify candidate susceptibility genes for epithelial ovarian cancer risk

Large-scale genome-wide association studies (GWAS) have identified approximately 35 loci associated with epithelial ovarian cancer (EOC) risk. The majority of GWAS-identified disease susceptibility variants are located in non-coding regions, and causal genes underlying these associations remain largely unknown. Here we performed a transcriptome-wide association study to search for novel genetic loci and plausible causal genes at known GWAS loci. We used RNA sequencing data (68 normal ovarian-tissue samples from 68 individuals and 6,124 cross-tissue samples from 369 individuals) and high-density genotyping data from European descendants of the Genotype-Tissue Expression (GTEx V6) project to build ovarian and cross-tissue models of genetically regulated expression using elastic net methods. We evaluated 17,121 genes for their cis-predicted gene expression in relation to EOC risk using summary statistics data from GWAS of 97,898 women, including 29,396 EOC cases. With a Bonferroni-corrected significance level of P<2.2×10-6, we identified 35 genes including FZD4 at 11q14.2 (Z=5.08, P=3.83×10-7, the cross-tissue model; 1 Mb away from any GWAS-identified EOC risk variant), a potential novel locus for EOC risk. All other 34 significantly-associated genes were located within 1 Mb of known GWAS-identified loci, including 23 genes at 6 loci not previously linked to EOC risk. Upon conditioning on nearby known EOC GWAS-identified variants, the associations for 31 genes disappeared and 3 genes remained (P<1.47 x 10-3). These data identify one novel locus (FZD4) and 34 genes at 13 known EOC risk loci associated with EOC risk, providing new insights into EOC carcinogenesis

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs