University of Copenhagen

About: University of Copenhagen is a education organization based out in Copenhagen, Denmark. It is known for research contribution in the topics: Population & Galaxy. The organization has 57645 authors who have published 149740 publications receiving 5903093 citations. The organization is also known as: Copenhagen University & Københavns Universitet.

Quantitative Phosphoproteomics Reveals Widespread Full Phosphorylation Site Occupancy During Mitosis

Abstract: Eukaryotic cells replicate by a complex series of evolutionarily conserved events that are tightly regulated at defined stages of the cell division cycle. Progression through this cycle involves a large number of dedicated protein complexes and signaling pathways, and deregulation of this process is implicated in tumorigenesis. We applied high-resolution mass spectrometry-based proteomics to investigate the proteome and phosphoproteome of the human cell cycle on a global scale and quantified 6027 proteins and 20,443 unique phosphorylation sites and their dynamics. Co-regulated proteins and phosphorylation sites were grouped according to their cell cycle kinetics and compared to publicly available messenger RNA microarray data. Most detected phosphorylation sites and more than 20% of all quantified proteins showed substantial regulation, mainly in mitotic cells. Kinase-motif analysis revealed global activation during S phase of the DNA damage response network, which was mediated by phosphorylation by ATM or ATR or DNA-dependent protein kinases. We determined site-specific stoichiometry of more than 5000 sites and found that most of the up-regulated sites phosphorylated by cyclin-dependent kinase 1 (CDK1) or CDK2 were almost fully phosphorylated in mitotic cells. In particular, nuclear proteins and proteins involved in regulating metabolic processes have high phosphorylation site occupancy in mitosis. This suggests that these proteins may be inactivated by phosphorylation in mitotic cells.

Lipoprotein(a) as a cardiovascular risk factor: current status

Abstract: AIMS: The aims of the study were, first, to critically evaluate lipoprotein(a) [Lp(a)] as a cardiovascular risk factor and, second, to advise on screening for elevated plasma Lp(a), on desirable levels, and on therapeutic strategies. METHODS AND RESULTS: The robust and specific association between elevated Lp(a) levels and increased cardiovascular disease (CVD)/coronary heart disease (CHD) risk, together with recent genetic findings, indicates that elevated Lp(a), like elevated LDL-cholesterol, is causally related to premature CVD/CHD. The association is continuous without a threshold or dependence on LDL- or non-HDL-cholesterol levels. Mechanistically, elevated Lp(a) levels may either induce a prothrombotic/anti-fibrinolytic effect as apolipoprotein(a) resembles both plasminogen and plasmin but has no fibrinolytic activity, or may accelerate atherosclerosis because, like LDL, the Lp(a) particle is cholesterol-rich, or both. We advise that Lp(a) be measured once, using an isoform-insensitive assay, in subjects at intermediate or high CVD/CHD risk with premature CVD, familial hypercholesterolaemia, a family history of premature CVD and/or elevated Lp(a), recurrent CVD despite statin treatment, ≥3% 10-year risk of fatal CVD according to European guidelines, and/or ≥10% 10-year risk of fatal + non-fatal CHD according to US guidelines. As a secondary priority after LDL-cholesterol reduction, we recommend a desirable level for Lp(a) <80th percentile (less than ∼50 mg/dL). Treatment should primarily be niacin 1-3 g/day, as a meta-analysis of randomized, controlled intervention trials demonstrates reduced CVD by niacin treatment. In extreme cases, LDL-apheresis is efficacious in removing Lp(a). CONCLUSION: We recommend screening for elevated Lp(a) in those at intermediate or high CVD/CHD risk, a desirable level <50 mg/dL as a function of global cardiovascular risk, and use of niacin for Lp(a) and CVD/CHD risk reduction.

Gut microbiota in human metabolic health and disease.

Abstract: Observational findings achieved during the past two decades suggest that the intestinal microbiota may contribute to the metabolic health of the human host and, when aberrant, to the pathogenesis of various common metabolic disorders including obesity, type 2 diabetes, non-alcoholic liver disease, cardio-metabolic diseases and malnutrition. However, to gain a mechanistic understanding of how the gut microbiota affects host metabolism, research is moving from descriptive microbiota census analyses to cause-and-effect studies. Joint analyses of high-throughput human multi-omics data, including metagenomics and metabolomics data, together with measures of host physiology and mechanistic experiments in humans, animals and cells hold potential as initial steps in the identification of potential molecular mechanisms behind reported associations. In this Review, we discuss the current knowledge on how gut microbiota and derived microbial compounds may link to metabolism of the healthy host or to the pathogenesis of common metabolic diseases. We highlight examples of microbiota-targeted interventions aiming to optimize metabolic health, and we provide perspectives for future basic and translational investigations within the nascent and promising research field. In this Review, Fan and Pedersen discuss how the gut microbiota and derived microbial compounds may contribute to human metabolic health and to the pathogenesis of common metabolic diseases, and highlight examples of microbiota-targeted interventions aiming to optimize metabolic health.

Class of antiretroviral drugs and the risk of myocardial infarction.

Abstract: BACKGROUND: We have previously demonstrated an association between combination antiretroviral therapy and the risk of myocardial infarction. It is not clear whether this association differs according to the class of antiretroviral drugs. We conducted a study to investigate the association of cumulative exposure to protease inhibitors and nonnucleoside reverse-transcriptase inhibitors with the risk of myocardial infarction. METHODS: We analyzed data collected through February 2005 from our prospective observational study of 23,437 patients infected with the human immunodeficiency virus. The incidence rates of myocardial infarction during the follow-up period were calculated, and the associations between myocardial infarction and exposure to protease inhibitors or nonnucleoside reverse-transcriptase inhibitors were determined. RESULTS: Three hundred forty-five patients had a myocardial infarction during 94,469 person-years of observation. The incidence of myocardial infarction increased from 1.53 per 1000 person-years in those not exposed to protease inhibitors to 6.01 per 1000 person-years in those exposed to protease inhibitors for more than 6 years. After adjustment for exposure to the other drug class and established cardiovascular risk factors (excluding lipid levels), the relative rate of myocardial infarction per year of protease-inhibitor exposure was 1.16 (95% confidence interval [CI], 1.10 to 1.23), whereas the relative rate per year of exposure to nonnucleoside reverse-transcriptase inhibitors was 1.05 (95% CI, 0.98 to 1.13). Adjustment for serum lipid levels further reduced the effect of exposure to each drug class to 1.10 (95% CI, 1.04 to 1.18) and 1.00 (95% CI, 0.93 to 1.09), respectively. CONCLUSIONS: Increased exposure to protease inhibitors is associated with an increased risk of myocardial infarction, which is partly explained by dyslipidemia. We found no evidence of such an association for nonnucleoside reverse-transcriptase inhibitors; however, the number of person-years of observation for exposure to this class of drug was less than that for exposure to protease inhibitors.

Bioassay analysis using R

Abstract: We describe an add-on package for the language and environment R which allows simultaneous fitting of several non-linear regression models. The focus is on analysis of dose response curves, but the functionality is applicable to arbitrary non-linear regression models. Features of the package is illustrated in examples.