1,132 research outputs found

    Predicting Phenotypic Diversity and the Underlying Quantitative Molecular Transitions

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    During development, signaling networks control the formation of multicellular patterns. To what extent quantitative fluctuations in these complex networks may affect multicellular phenotype remains unclear. Here, we describe a computational approach to predict and analyze the phenotypic diversity that is accessible to a developmental signaling network. Applying this framework to vulval development in C. elegans, we demonstrate that quantitative changes in the regulatory network can render ~500 multicellular phenotypes. This phenotypic capacity is an order-of-magnitude below the theoretical upper limit for this system but yet is large enough to demonstrate that the system is not restricted to a select few outcomes. Using metrics to gauge the robustness of these phenotypes to parameter perturbations, we identify a select subset of novel phenotypes that are the most promising for experimental validation. In addition, our model calculations provide a layout of these phenotypes in network parameter space. Analyzing this landscape of multicellular phenotypes yielded two significant insights. First, we show that experimentally well-established mutant phenotypes may be rendered using non-canonical network perturbations. Second, we show that the predicted multicellular patterns include not only those observed in C. elegans, but also those occurring exclusively in other species of the Caenorhabditis genus. This result demonstrates that quantitative diversification of a common regulatory network is indeed demonstrably sufficient to generate the phenotypic differences observed across three major species within the Caenorhabditis genus. Using our computational framework, we systematically identify the quantitative changes that may have occurred in the regulatory network during the evolution of these species. Our model predictions show that significant phenotypic diversity may be sampled through quantitative variations in the regulatory network without overhauling the core network architecture. Furthermore, by comparing the predicted landscape of phenotypes to multicellular patterns that have been experimentally observed across multiple species, we systematically trace the quantitative regulatory changes that may have occurred during the evolution of the Caenorhabditis genus

    COMPARATIVE STUDY OF EFFECT OF SWERTIA CHIRATA LEAF EXTRACT ON INDINAVIR TREATED RATS

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    Background: Indinavir is widely used for the treatment of human immunodeficiency virus (HIV) infection. It is known to cause hyperglycemia or insulin resistance and hyperlipidemia.Aim and Objectives: To study the effect of Swertia chirata leaf extract with metformin and pioglitazone on indinavir treated rats.Methods: Swiss albino rats were divided into five Groups of six animals each. All the groups (except control) were treated with indinavir 216 mg/kg (oral) for 15 days. Group I (control) received normal saline (oral) from day 8 to day 15, Group II received indinavir 216 mg/kg (oral), Group III received S. chirata plant extract 500 mg/kg (oral) from day 8 to day 15, Group IV received pioglitazone 4 mg/kg (oral) from day 8 to day 15, and Group V received metformin 36 mg/kg (oral) from day 8 to day 15. The biochemical parameters such as serum glucose, insulin, and lipid levels were measured on day 15. Results were analyzed using one-way analysis of variance followed by Bonferroni's multiple comparison test.Results: Indinavir (216 mg/kg) treated rats showed a significant (p<0.05) increase in glucose and insulin levels and also altered lipid levels. This indicates indinavir produces diabetic-like state in rats. S. chirata extract (500 mg/kg) decreases glucose and insulin levels and also improves lipid levels the effect is almost similar to metformin and pioglitazone.Conclusion: Indinavir causes elevated glucose, insulin and lipid levels, so care must be taken while prescribing indinavir for HIV patients. Treatment with S. chirata extract improved the altered glucose, insulin, and lipid profile in indinavir treated rats.Key words: Indinavir, Insulin resistance, Diabetes dyslipidemia, Glucose intolerance

    Aurora kinase A drives the evolution of resistance to third-generation EGFR inhibitors in lung cancer.

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    Although targeted therapies often elicit profound initial patient responses, these effects are transient due to residual disease leading to acquired resistance. How tumors transition between drug responsiveness, tolerance and resistance, especially in the absence of preexisting subclones, remains unclear. In epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma cells, we demonstrate that residual disease and acquired resistance in response to EGFR inhibitors requires Aurora kinase A (AURKA) activity. Nongenetic resistance through the activation of AURKA by its coactivator TPX2 emerges in response to chronic EGFR inhibition where it mitigates drug-induced apoptosis. Aurora kinase inhibitors suppress this adaptive survival program, increasing the magnitude and duration of EGFR inhibitor response in preclinical models. Treatment-induced activation of AURKA is associated with resistance to EGFR inhibitors in vitro, in vivo and in most individuals with EGFR-mutant lung adenocarcinoma. These findings delineate a molecular path whereby drug resistance emerges from drug-tolerant cells and unveils a synthetic lethal strategy for enhancing responses to EGFR inhibitors by suppressing AURKA-driven residual disease and acquired resistance

    Determination of Pericardial Adipose Tissue Increases the Prognostic Accuracy of Coronary Artery Calcification for Future Cardiovascular Events

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    Objectives: Pericardial adipose tissue (PAT) is associated with coronary artery plaque accumulation and the incidence of coronary heart disease. We evaluated the possible incremental prognostic value of PAT for future cardiovascular events. Methods: 145 patients (94 males, age 60 10 years) with stable coronary artery disease underwent coronary artery calcification (CAC) scanning in a multislice CT scanner, and the volume of pericardial fat was measured. Mean observation time was 5.4 years. Results: 34 patients experienced a severe cardiac event. They had a significantly higher CAC score (1,708 +/- 2,269 vs. 538 +/- 1,150, p 400, 3.5 (1.9-5.4; p = 0.007) for scores > 800 and 5.9 (3.7-7.8; p = 0.005) for scores > 1,600. When additionally a PAT volume > 200 cm(3) was determined, there was a significant increase in the event rate and relative risk. We calculated a relative risk of 2.9 (1.9-4.2; p = 0.01) for scores > 400, 4.0 (2.1-5.0; p = 0.006) for scores > 800 and 7.1 (4.1-10.2; p = 0.005) for scores > 1,600. Conclusions:The additional determination of PAT increases the predictive power of CAC for future cardiovascular events. PAT might therefore be used as a further parameter for risk stratification. Copyright (C) 2012 S. Karger AG, Base

    A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease

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    Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association studies (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of 185 thousand CAD cases and controls, interrogating 6.7 million common (MAF>0.05) as well as 2.7 million low frequency (0.005<MAF<0.05) variants. In addition to confirmation of most known CAD loci, we identified 10 novel loci, eight additive and two recessive, that contain candidate genes that newly implicate biological processes in vessel walls. We observed intra-locus allelic heterogeneity but little evidence of low frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect siz

    Do we need to distance ourselves from the distance concept? Why home and host country context might matter more than (cultural) distance

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    We scrutinize the explanatory power of one of the key concepts in International Business: the concept of (cultural) distance. Here we focus on its effect on entry mode choice, one of the most researched fields in international business strategy. Our findings might, however, be equally be relevant for the field of International Business as a whole. Our analysis is based on a review of 92 prior studies on entry mode choice, as well as an empirical investigation in over 800 subsidiaries of MNCs, covering nine host and fifteen home countries across the world. We conclude that the explanatory power of distance is highly limited once home and host country context are accounted for, and that any significant effects of cultural distance on entry mode choice might simply be caused by inadequate sampling. Entry mode studies in particular, and International Business research in general, would do well to reconsider its fascination with distance measures, and instead, focus first and foremost on differences in home and host country context. We argue that serious engagement with deep contextualization is necessary in International Business research to pose new and relevant questions and develop new and innovative theories that explain empirical phenomena
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