Consistency of cruise data of the CARINA database in the Atlantic sector of the Southern Ocean

Initially a North Atlantic project, the CARINA carbon synthesis was extended to include the Southern Ocean. Carbon and relevant hydrographic and geochemical ancillary data from cruises all across the Arctic Mediterranean Seas, Atlantic and Southern Ocean were released to the public and merged into a new database as part of the CARINA synthesis effort. Of a total of 188 cruises, 37 cruises are part of the Southern Ocean, including 11 from the Atlantic sector. The variables from all Southern Ocean cruises, including dissolved inorganic carbon (TCO2), total alkalinity, oxygen, nitrate, phosphate and silicate, were examined for cruise-to-cruise consistency in one collective effort. Seawater pH and chlorofluorocarbons (CFCs) are also part of the database, but the pH quality control (QC) is described in another Earth System Science Data publication, while the complexity of the Southern Ocean physics and biogeochemistry prevented a proper QC analysis of the CFCs. The area-specific procedures of quality control, including crossover analysis between stations and inversion analysis of all crossover data (i.e. secondary QC), are briefly described here for the Atlantic sector of the Southern Ocean. Data from an existing, quality controlled database (GLODAP) were used as a reference for our computations – however, the reference data were included into the analysis without applying the recommended GLODAP adjustments so the corrections could be independently verified. The outcome of this effort is an internally consistent, high-quality carbon data set for all cruises, including the reference cruises. The suggested corrections by the inversion analysis were allowed to vary within a fixed envelope, thus accounting for natural variability. The percentage of cruises adjusted ranged from 31% (for nitrate) to 54% (for phosphate) depending on the variable

Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli

The set of regulatory interactions between genes, mediated by transcription factors, forms a species' transcriptional regulatory network (TRN). By comparing this network with measured gene expression data one can identify functional properties of the TRN and gain general insight into transcriptional control. We define the subnet of a node as the subgraph consisting of all nodes topologically downstream of the node, including itself. Using a large set of microarray expression data of the bacterium Escherichia coli, we find that the gene expression in different subnets exhibits a structured pattern in response to environmental changes and genotypic mutation. Subnets with less changes in their expression pattern have a higher fraction of feed-forward loop motifs and a lower fraction of small RNA targets within them. Our study implies that the TRN consists of several scales of regulatory organization: 1) subnets with more varying gene expression controlled by both transcription factors and post-transcriptional RNA regulation, and 2) subnets with less varying gene expression having more feed-forward loops and less post-transcriptional RNA regulation.Comment: 14 pages, 8 figures, to be published in PLoS Computational Biolog

Abnormal cognition, sleep, EEG and brain metabolism in a novel knock-in Alzheimer mouse, PLB1

Peer reviewedPublisher PD

Periostin Responds to Mechanical Stress and Tension by Activating the MTOR Signaling Pathway

Current knowledge about Periostin biology has expanded from its recognized functions in embryogenesis and bone metabolism to its roles in tissue repair and remodeling and its clinical implications in cancer. Emerging evidence suggests that Periostin plays a critical role in the mechanism of wound healing; however, the paracrine effect of Periostin in epithelial cell biology is still poorly understood. We found that epithelial cells are capable of producing endogenous Periostin that, unlike mesenchymal cell, cannot be secreted. Epithelial cells responded to Periostin paracrine stimuli by enhancing cellular migration and proliferation and by activating the mTOR signaling pathway. Interestingly, biomechanical stimulation of epithelial cells, which simulates tension forces that occur during initial steps of tissue healing, induced Periostin production and mTOR activation. The molecular association of Periostin and mTOR signaling was further dissected by administering rapamycin, a selective pharmacological inhibitor of mTOR, and by disruption of Raptor and Rictor scaffold proteins implicated in the regulation of mTORC1 and mTORC2 complex assembly. Both strategies resulted in ablation of Periostin-induced mitogenic and migratory activity. These results indicate that Periostin-induced epithelial migration and proliferation requires mTOR signaling. Collectively, our findings identify Periostin as a mechanical stress responsive molecule that is primarily secreted by fibroblasts during wound healing and expressed endogenously in epithelial cells resulting in the control of cellular physiology through a mechanism mediated by the mTOR signaling cascade.This work was funded by the National Institutes of Health (NIH/NCI) P50-CA97248 (University of Michigan Head and Neck SPORE)

Statistical Guidance for Experimental Design and Data Analysis of Mutation Detection in Rare Monogenic Mendelian Diseases by Exome Sequencing

Recently, whole-genome sequencing, especially exome sequencing, has successfully led to the identification of causal mutations for rare monogenic Mendelian diseases. However, it is unclear whether this approach can be generalized and effectively applied to other Mendelian diseases with high locus heterogeneity. Moreover, the current exome sequencing approach has limitations such as false positive and false negative rates of mutation detection due to sequencing errors and other artifacts, but the impact of these limitations on experimental design has not been systematically analyzed. To address these questions, we present a statistical modeling framework to calculate the power, the probability of identifying truly disease-causing genes, under various inheritance models and experimental conditions, providing guidance for both proper experimental design and data analysis. Based on our model, we found that the exome sequencing approach is well-powered for mutation detection in recessive, but not dominant, Mendelian diseases with high locus heterogeneity. A disease gene responsible for as low as 5% of the disease population can be readily identified by sequencing just 200 unrelated patients. Based on these results, for identifying rare Mendelian disease genes, we propose that a viable approach is to combine, sequence, and analyze patients with the same disease together, leveraging the statistical framework presented in this work

Globally convergent evolution strategies for constrained optimization

International audienceIn this paper we propose, analyze, and test algorithms for constrained optimization when no use of derivatives of the objective function is made. The proposed methodology is built upon the globally convergent evolution strategies previously introduced by the authors for unconstrained optimization. Two approaches are encompassed to handle the constraints. In a first approach, feasibility is first enforced by a barrier function and the objective function is then evaluated directly at the feasible generated points. A second approach projects first all the generated points onto the feasible domain before evaluating the objective function.The resulting algorithms enjoy favorable global convergence properties (convergence to stationarity from arbitrary starting points), regardless of the linearity of the constraints.The algorithmic implementation (i) includes a step where previously evaluated points are used to accelerate the search (by minimizing quadratic models) and (ii) addresses the particular cases of bounds on the variables and linear constraints. Our solver is compared to others, and the numerical results confirm its competitiveness in terms of efficiency and robustness

Platelets of patients with chronic kidney disease demonstrate deficient platelet reactivity in vitro

<p>Abstract</p> <p>Background</p> <p>In patients with chronic kidney disease studies focusing on platelet function and properties often are non-conclusive whereas only few studies use functional platelet tests. In this study we evaluated a recently developed functional flow cytometry based assay for the analysis of platelet function in chronic kidney disease.</p> <p>Methods</p> <p>Platelet reactivity was measured using flow cytometric analysis. Platelets in whole blood were triggered with different concentrations of agonists (TRAP, ADP, CRP). Platelet activation was quantified with staining for P-selectin, measuring the mean fluorescence intensity. Area under the curve and the concentration of half-maximal response were determined.</p> <p>Results</p> <p>We studied 23 patients with chronic kidney disease (9 patients with cardiorenal failure and 14 patients with end stage renal disease) and 19 healthy controls. Expression of P-selectin on the platelet surface measured as mean fluorescence intensity was significantly less in chronic kidney disease patients compared to controls after maximal stimulation with TRAP (9.7 (7.9-10.8) vs. 11.4 (9.2-12.2), P = 0.032), ADP (1.6 (1.2-2.1) vs. 2.6 (1.9-3.5), P = 0.002) and CRP (9.2 (8.5-10.8) vs. 11.5 (9.5-12.9), P = 0.004). Also the area under the curve was significantly different. There was no significant difference in half-maximal response between both groups.</p> <p>Conclusion</p> <p>In this study we found that patients with chronic kidney disease show reduced platelet reactivity in response of ADP, TRAP and CRP compared to controls. These results contribute to our understanding of the aberrant platelet function observed in patients with chronic kidney disease and emphasize the significance of using functional whole blood platelet activation assays.</p