Estimating the effect of liver and pancreas volume and fat content on risk of diabetes: a Mendelian randomization study.

This is the author accepted manuscript. The final version is available from the American Diabetes Association via the DOI in this record OBJECTIVE: Fat content and volume of liver and pancreas are associated with risk of diabetes in observational studies; whether these associations are causal is unknown. We conducted a Mendelian randomization (MR) study to examine causality of such associations. RESEARCH DESIGN AND METHODS: We used genetic variants associated (P < 5 × 10-8) with the exposures (liver and pancreas volume and fat content) using MRI scans of UK Biobank participants (n = 32,859). We obtained summary-level data for risk of type 1 (9,358 cases) and type 2 (55,005 cases) diabetes from the largest available genome-wide association studies. We performed inverse-variance weighted MR as main analysis and several sensitivity analyses to assess pleiotropy and to exclude variants with potential pleiotropic effects. RESULTS: Observationally, liver fat and volume were associated with type 2 diabetes (odds ratio per 1 SD higher exposure 2.16 [2.02, 2.31] and 2.11 [1.96, 2.27], respectively). Pancreatic fat was associated with type 2 diabetes (1.42 [1.34, 1.51]) but not type 1 diabetes, and pancreas volume was negatively associated with type 1 diabetes (0.42 [0.36, 0.48]) and type 2 diabetes (0.73 [0.68, 0.78]). MR analysis provided evidence only for a causal role of liver fat and pancreas volume in risk of type 2 diabetes (1.27 [1.08, 1.49] or 27% increased risk and 0.76 [0.62, 0.94] or 24% decreased risk per 1SD, respectively) and no causal associations with type 1 diabetes. CONCLUSIONS: Our findings assist in understanding the causal role of ectopic fat in the liver and pancreas and of organ volume in the pathophysiology of type 1 and 2 diabetes.Diabetes UKBritish Heart FoundationMedical Research CouncilCalico Life Sciences LL

Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning

Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000 abdominal MRI scans in the UK Biobank, we used deep learning to quantify volume, fat, and iron in seven organs and tissues, and demonstrate that imaging-derived phenotypes reflect health status. We show that these traits have a substantial heritable component (8–44%) and identify 93 independent genome-wide significant associations, including four associations with liver traits that have not previously been reported. Our work demonstrates the tractability of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues, and use the largest-ever study of its kind to generate new insights into the genetic architecture of these traits

Direct observation of topoisomerase IA gate dynamics

Type IA topoisomerases cleave single-stranded DNA and relieve negative supercoils in discrete steps corresponding to the passage of the intact DNA strand through the cleaved strand. Although type IA topoisomerases are assumed to accomplish this strand passage via a protein-mediated DNA gate, opening of this gate has never been observed. We developed a single-molecule assay to directly measure gate opening of the Escherichia coli type IA topoisomerases I and III. We found that after cleavage of single-stranded DNA, the protein gate opens by as much as 6.6 nm and can close against forces in excess of 16 pN. Key differences in the cleavage, ligation, and gate dynamics of these two enzymes provide insights into their different cellular functions. The single-molecule results are broadly consistent with conformational changes obtained from molecular dynamics simulations. These results allowed us to develop a mechanistic model of interactions between type IA topoisomerases and single-stranded DNA

Centrioles: active players or passengers during mitosis?

Centrioles are cylinders made of nine microtubule (MT) triplets present in many eukaryotes. Early studies, where centrosomes were seen at the poles of the mitotic spindle led to their coining as “the organ for cell division”. However, a variety of subsequent observational and functional studies showed that centrosomes might not always be essential for mitosis. Here we review the arguments in this debate. We describe the centriole structure and its distribution in the eukaryotic tree of life and clarify its role in the organization of the centrosome and cilia, with an historical perspective. An important aspect of the debate addressed in this review is how centrioles are inherited and the role of the spindle in this process. In particular, germline inheritance of centrosomes, such as their de novo formation in parthenogenetic species, poses many interesting questions. We finish by discussing the most likely functions of centrioles and laying out new research avenues

Search for neutral MSSM Higgs bosons decaying to a pair of tau leptons in pp collisions

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Measurement of top quark–antiquark pair production in association with a W or Z boson in pp collisions at √s=8 TeV

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Searches for electroweak production of charginos, neutralinos, and sleptons decaying to leptons and W, Z, and Higgs bosons in pp collisions at 8 TeV

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Study of hadronic event-shape variables in multijet final states in pp collisions at √s=7 TeV

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Measurement of prompt J/ψ pair production in pp collisions at √s = 7 Tev

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Constraints on parton distribution functions and extraction of the strong coupling constant from the inclusive jet cross section in pp collisions at √s=7 TeV

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