Enhanced tumour antiangiogenic effects when combining gefitinib with the antivascular agent ZD6126

Current experimental and clinical knowledge supports the optimisation of endothelial cell targeting using a strategy combining anti-EGFR drugs with antivascular agents. The purpose of the present study was to examine the effects of the association of ZD6126, an antivascular microtubule-destabilising agent, with gefitinib and irradiation on the growth of six head and neck human cancer cell lines xenografted in nude mice and to study predictive and molecular factors responsible for antitumour effects. CAL33- and Hep-2-grafted cell lines were the most sensitive to ZD6126 treatment, with VEGF levels significantly higher (P=0.0336) in these tumour xenografts compared to Detroit 562- and CAL27-grafted cell lines with relatively low VEGF levels that were not sensitive to ZD6126. In contrast, neither IL8 levels nor EGFR expression was linked to the antitumour effects of ZD6126. ZD6126 in combination with gefitinib resulted in a synergistic cytotoxic interaction with greater antitumour effects than gefitinib alone. The synergistic interaction between ZD6126 and gefitinib was corroborated by a significant decrease in CD31 labelling. The present study may serve for future innovative clinical applications, as it suggests that VEGF tumour levels are possible predictors for ZD6126 antitumour efficacy. It also supports the notion of antitumour supra-additivity when combining gefitinib and ZD6126, and identifies neoangiogenesis as the main determinant of this synergistic combination

The EpsE Flagellar Clutch Is Bifunctional and Synergizes with EPS Biosynthesis to Promote Bacillus subtilis Biofilm Formation

Many bacteria inhibit motility concomitant with the synthesis of an extracellular polysaccharide matrix and the formation of biofilm aggregates. In Bacillus subtilis biofilms, motility is inhibited by EpsE, which acts as a clutch on the flagella rotor to inhibit motility, and which is encoded within the 15 gene eps operon required for EPS production. EpsE shows sequence similarity to the glycosyltransferase family of enzymes, and we demonstrate that the conserved active site motif is required for EPS biosynthesis. We also screen for residues specifically required for either clutch or enzymatic activity and demonstrate that the two functions are genetically separable. Finally, we show that, whereas EPS synthesis activity is dominant for biofilm formation, both functions of EpsE synergize to stabilize cell aggregates and relieve selective pressure to abolish motility by genetic mutation. Thus, the transition from motility to biofilm formation may be governed by a single bifunctional enzyme

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Genetic contributions to two special factors of neuroticism are associated with affluence, higher intelligence, better health, and longer life

Higher scores on the personality trait of neuroticism, the tendency to experience negative emotions, are associated with worse mental and physical health. Studies examining links between neuroticism and health typically operationalize neuroticism by summing the items from a neuroticism scale. However, neuroticism is made up of multiple heterogeneous facets, each contributing to the effect of neuroticism as a whole. A recent study showed that a 12-item neuroticism scale described one broad trait of general neuroticism and two special factors, one characterizing the extent to which people worry and feel vulnerable, and the other characterizing the extent to which people are anxious and tense. This study also found that, although individuals who were higher on general neuroticism lived shorter lives, individuals whose neuroticism was characterized by worry and vulnerability lived longer lives. Here, we examine the genetic contributions to the two special factors of neuroticism—anxiety/tension and worry/vulnerability—and how they contrast with that of general neuroticism. First, we show that, whereas the polygenic load for neuroticism is associated with the genetic risk of coronary artery disease, lower intelligence, lower socioeconomic status (SES), and poorer self-rated health, the genetic variants associated with high levels of anxiety/tension, and high levels of worry/vulnerability are associated with genetic variants linked to higher SES, higher intelligence, better self-rated health, and longer life. Second, we identify genetic variants that are uniquely associated with these protective aspects of neuroticism. Finally, we show that different neurological pathways are linked to each of these neuroticism phenotypes.</p

Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness

Hand grip strength is a widely used proxy of muscular fitness, a marker of frailty, and predictor of a range of morbidities and all-cause mortality. To investigate the genetic determinants of variation in grip strength, we perform a large-scale genetic discovery analysis in a combined sample of 195,180 individuals and identify 16 loci associated with grip strength (P<5 × 10−8) in combined analyses. A number of these loci contain genes implicated in structure and function of skeletal muscle fibres (ACTG1), neuronal maintenance and signal transduction (PEX14, TGFA, SYT1), or monogenic syndromes with involvement of psychomotor impairment (PEX14, LRPPRC and KANSL1). Mendelian randomization analyses are consistent with a causal effect of higher genetically predicted grip strength on lower fracture risk. In conclusion, our findings provide new biological insight into the mechanistic underpinnings of grip strength and the causal role of muscular strength in age-related morbidities and mortality.This research has been conducted using the UK Biobank Resource. The Fenland Study is supported by the UK Medical Research Council (MRC) (MC_UU_12015/1; MC_UU_12015/2; MC_UU_12015/3). EPIC-Norfolk is supported by the MRC (G401527, G1000143) and Cancer Research UK (A8257). The HCS is gratefully supported by the University of Newcastle (Australia) and the Fairfax Family Foundation. Sydney MAS is supported by the Australian National Health and Medical Research Council (NHMRC), grants ID568969, ID350833 and ID109308. Sydney MAS DNA was extracted by Genetic Repositories Australia, funded by NHMRC Enabling Grant 401184. The GEFOS Study, used as controls for the US and Jamaican athletes, was supported in part by NIH grants U01 HG004436 and P30 DK072488, and the Baltimore Geriatrics Research, Education, and Clinical Center of the Department of Veterans Affairs. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk). TwinsUK was funded by the Wellcome Trust (WT), MRC, and European Union. The study also receives support from the National Institute for Health Research (NIHR) BioResource Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London. SNP Genotyping was performed by The WT Sanger Institute and National Eye Institute via NIH/CIDR. M.McC is a WT Senior Investigator and receives support from WT 090532 and 098381. TW is the recipient of a studentship from MedImmune. Research by A. Lucia is supported by Fondo de Investigaciones Sanitarias and Fondos Feder (grant # PI15/0558). EM-M. was a recipient of a Grant-in-Aid for JSPS Fellow from the Japan Society for the Promotion of Science. This work was supported in part by grants from the Grant-in-Aid for Scientific Research (B) (15H03081 to NF) of the Japanese Ministry of Education, Culture, Sports, Science and Technology and by a grant-in-aid for scientific research (to M. Miyachi) from the Japanese Ministry of Health, Labor, and Welfare. This work was further supported by NIH grants R01 AR41398 and U24 AG051129

Towards the modular decomposition of the metabolic network

International audienceModular systems emerged in biology through the natural selection and evolution, even at the scale of the cell with the cellular processes which perform elementary and specialized tasks. However, the existence of modules is questionable when the regulatory networks of the cell are superimposed, in particular for the metabolic network. In this chapter, a theoretical framework is introduced allowing to break down the metabolic network into elementary modules in steady-state. The modular decomposition confers to the whole system good systemic and control properties, like the decoupling of the steady-state regime with respect to co-metabolites or co-factors. Biological configurations, and their impact on module properties, are deeply discussed. In particular, irreversible enzymes are critical in the module definition. Moreover, through the proposed framework, the dynamics of module components can be qualitatively predicted and used to analyze biological datasets