University of Southern Denmark

About: University of Southern Denmark is a education organization based out in Odense, Syddanmark, Denmark. It is known for research contribution in the topics: Population & Randomized controlled trial. The organization has 11928 authors who have published 37918 publications receiving 1258559 citations. The organization is also known as: SDU.

Seipin is required for converting nascent to mature lipid droplets.

Abstract: Living organisms often store energy in the form of fat molecules called triglycerides. Enzymes in a compartment of the cell called the endoplasmic reticulum catalyze the chemical reactions needed to make these triglycerides. The cell then stores the triglycerides in a different structure called the lipid droplet. Lipid droplets form from the endoplasmic reticulum in an organized manner, but little is known about the cellular machinery that gives rise to lipid droplets. A protein called seipin is thought to be involved in lipid droplet formation. Seipin resides in the endoplasmic reticulum and a shortage of this protein in cells leads to abnormal lipid droplets – that is, cells often have lots of tiny lipid droplets or a few giant ones. People who lack seipin lose much of their fat tissue and instead store fat in the wrong places, such as the liver. Now, Wang et al. have studied the seipin protein in insect and human cells grown in the laboratory. The experiments confirmed that cells that lack the seipin protein form lots of tiny dot-like structures containing triglycerides that fail to grow into normal-sized lipid droplets. These lipid droplets have different proteins on their surface, which may impair their ability to store fat. Wang et al. also discovered that in normal cells, the seipin protein is found at distinct spots in the endoplasmic reticulum. This distribution appears to allow seipin to come into contact with the small, newly formed lipid droplets and enable them to grow. Together these findings suggest that the seipin protein could form part of a molecular machine that allows more triglycerides to be added into newly formed lipid droplets causing the droplets to grow as normal. When seipin is not present the newly formed lipid droplets initially become stuck in a smaller form. As a consequence, a few of these tiny droplets later enter a different cellular pathway of lipid droplet expansion, which turns them into abnormally large lipid droplets. Future challenges will be to determine precisely how seipin enables newly formed lipid droplets to grow. It will also be important to confirm whether seipin works with other proteins as part of a molecular machine and, if so, to investigate how these proteins affect the formation and growth of lipid droplets.

Multiscale modeling of emergent materials: biological and soft matter

Abstract: In this review, we focus on four current related issues in multiscale modeling of soft and biological matter. First, we discuss how to use structural information from detailed models (or experiments) to construct coarse-grained ones in a hierarchical and systematic way. This is discussed in the context of the so-called Henderson theorem and the inverse Monte Carlo method of Lyubartsev and Laaksonen. In the second part, we take a different look at coarse graining by analyzing conformations of molecules. This is done by the application of self-organizing maps, i.e., a neural network type approach. Such an approach can be used to guide the selection of the relevant degrees of freedom. Then, we discuss technical issues related to the popular dissipative particle dynamics (DPD) method. Importantly, the potentials derived using the inverse Monte Carlo method can be used together with the DPD thermostat. In the final part we focus on solvent-free modeling which offers a different route to coarse graining by integrating out the degrees of freedom associated with solvent.

Meta-analysis of Genome-wide Association Studies for Neuroticism, and the Polygenic Association With Major Depressive Disorder

Abstract: Importance Neuroticism is a pervasive risk factor for psychiatric conditions. It genetically overlaps with major depressive disorder (MDD) and is therefore an important phenotype for psychiatric genetics. The Genetics of Personality Consortium has created a resource for genome-wide association analyses of personality traits in more than 63 000 participants (including MDD cases). Objectives To identify genetic variants associated with neuroticism by performing a meta-analysis of genome-wide association results based on 1000 Genomes imputation; to evaluate whether common genetic variants as assessed by single-nucleotide polymorphisms (SNPs) explain variation in neuroticism by estimating SNP-based heritability; and to examine whether SNPs that predict neuroticism also predict MDD. Design, Setting, and Participants Genome-wide association meta-analysis of 30 cohorts with genome-wide genotype, personality, and MDD data from the Genetics of Personality Consortium. The study included 63 661 participants from 29 discovery cohorts and 9786 participants from a replication cohort. Participants came from Europe, the United States, or Australia. Analyses were conducted between 2012 and 2014. Main Outcomes and Measures Neuroticism scores harmonized across all 29 discovery cohorts by item response theory analysis, and clinical MDD case-control status in 2 of the cohorts. Results A genome-wide significant SNP was found on 3p14 in MAGI1 (rs35855737; P = 9.26 × 10−9 in the discovery meta-analysis). This association was not replicated (P = .32), but the SNP was still genome-wide significant in the meta-analysis of all 30 cohorts (P = 2.38 × 10−8). Common genetic variants explain 15% of the variance in neuroticism. Polygenic scores based on the meta-analysis of neuroticism in 27 cohorts significantly predicted neuroticism (1.09 × 10−12 < P < .05) and MDD (4.02 × 10−9 < P < .05) in the 2 other cohorts. Conclusions and Relevance This study identifies a novel locus for neuroticism. The variant is located in a known gene that has been associated with bipolar disorder and schizophrenia in previous studies. In addition, the study shows that neuroticism is influenced by many genetic variants of small effect that are either common or tagged by common variants. These genetic variants also influence MDD. Future studies should confirm the role of the MAGI1 locus for neuroticism and further investigate the association of MAGI1 and the polygenic association to a range of other psychiatric disorders that are phenotypically correlated with neuroticism

The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers.

Abstract: BACKGROUND: Common cancers develop through a multistep process often including inherited susceptibility. Collaboration among multiple institutions, and funding from multiple sources, has allowed the development of an inexpensive genotyping microarray, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers, and cancer-related traits. METHODS: The OncoArray can be genotyped using a novel technology developed by Illumina to facilitate efficient genotyping. The consortium developed standard approaches for selecting SNPs for study, for quality control of markers, and for ancestry analysis. The array was genotyped at selected sites and with prespecified replicate samples to permit evaluation of genotyping accuracy among centers and by ethnic background. RESULTS: The OncoArray consortium genotyped 447,705 samples. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis. CONCLUSIONS: Results from these analyses will enable researchers to identify new susceptibility loci, perform fine-mapping of new or known loci associated with either single or multiple cancers, assess the degree of overlap in cancer causation and pleiotropic effects of loci that have been identified for disease-specific risk, and jointly model genetic, environmental, and lifestyle-related exposures. IMPACT: Ongoing analyses will shed light on etiology and risk assessment for many types of cancer. Cancer Epidemiol Biomarkers Prev; 26(1); 126-35. ©2016 AACR.