Exercise modifies the innate immune response in equine bronchial epithelial cells

Peer reviewe

InfraPhenoGrid: A scientific workflow infrastructure for Plant Phenomics on the Grid

International audiencePlant phenotyping consists in the observation of physical and biochemical traits of plant genotypes in response to environmental conditions. Challenges , in particular in context of climate change and food security, are numerous. High-throughput platforms have been introduced to observe the dynamic growth of a large number of plants in different environmental conditions. Instead of considering a few genotypes at a time (as it is the case when phenomic traits are measured manually), such platforms make it possible to use completely new kinds of approaches. However, the data sets produced by such widely instrumented platforms are huge, constantly augmenting and produced by increasingly complex experiments, reaching a point where distributed computation is mandatory to extract knowledge from data. In this paper, we introduce InfraPhenoGrid, the infrastructure we designed and deploy to efficiently manage data sets produced by the PhenoArch plant phenomics platform in the context of the French Phenome Project. Our solution consists in deploying scientific workflows on a Grid using a middle-ware to pilot workflow executions. Our approach is user-friendly in the sense that despite the intrinsic complexity of the infrastructure, running scientific workflows and understanding results obtained (using provenance information) is kept as simple as possible for end-users

Exome Sequencing and Genetic Testing for MODY

Context: Genetic testing for monogenic diabetes is important for patient care. Given the extensive genetic and clinical heterogeneity of diabetes, exome sequencing might provide additional diagnostic potential when standard Sanger sequencing-based diagnostics is inconclusive. Objective: The aim of the study was to examine the performance of exome sequencing for a molecular diagnosis of MODY in patients who have undergone conventional diagnostic sequencing of candidate genes with negative results. Research Design and Methods: We performed exome enrichment followed by high-throughput sequencing in nine patients with suspected MODY. They were Sanger sequencing-negative for mutations in the HNF1A, HNF4A, GCK, HNF1B and INS genes. We excluded common, non-coding and synonymous gene variants, and performed in-depth analysis on filtered sequence variants in a pre-defined set of 111 genes implicated in glucose metabolism. Results: On average, we obtained 45 X median coverage of the entire targeted exome and found 199 rare coding variants per individual. We identified 0–4 rare non-synonymous and nonsense variants per individual in our a priori list of 111 candidate genes. Three of the variants were considered pathogenic (in ABCC8, HNF4A and PPARG, respectively), thus exome sequencing led to a genetic diagnosis in at least three of the nine patients. Approximately 91% of known heterozygous SNPs in the target exomes were detected, but we also found low coverage in some key diabetes genes using our current exome sequencing approach. Novel variants in the genes ARAP1, GLIS3, MADD, NOTCH2 and WFS1 need further investigation to reveal their possible role in diabetes. Conclusion: Our results demonstrate that exome sequencing can improve molecular diagnostics of MODY when used as a complement to Sanger sequencing. However, improvements will be needed, especially concerning coverage, before the full potential of exome sequencing can be realized

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

SciFloware

SciFloware is a middleware for the execution of scientific workflows in adistributed and parallel way. It capitalizes on our experience with the Shared-Data Overlay Network and aninnovative algebraic approach to the management of scientific workflows. SciFloware provides a developmentenvironment and a runtime environment for scientific workflows, interoperable with existing systems. Wevalidate SciFloware with workflows for analyzing biological data provided by our partners CIRAD, INRAand IRD

Connecting the Dots, or Nuclear Data in the Age of Supercomputing

Recent increases in computing power have allowed for much progress to be made in the field of nuclear data. The advances listed below are each significant, but together bring the potential to completely change our perspective on the nuclear data evaluation process. The use of modern nuclear modeling codes like TALYS and the Monte Carlo sampling of its model parameter space, together with a code system developed at NRG Petten, which automates the production of ENDF-6 formatted files, their processing, and their use in nuclear reactor calculations, constitutes the Total Monte Carlo approach, which directly links physical model parameters with calculated integral observables like keff. Together with the Backward-Forward Monte Carlo method for weighting samples according their statistical likelihood, the Total Monte Carlo can be applied to complete isotopic chains in a consistent way, to simultaneously evaluate nuclear data and the associated uncertainties in the continuum region. Another improvement is found in the uses of microscopic models for nuclear reaction calculations. For example, making use of QRPA excited states calculated with the Gogny interaction to solve the long standing question of the origin of the ad hoc "pseudo-states" that are introduced in evaluated nuclear data files to account for the Livermore pulsed sphere experiments. A third advance consists of the recent optimization of the Gogny D1M effective nuclear interaction, including constraints from experimental nuclear masses at the "beyond the mean field" level. All these advances are only made possible by the availability of vast resources of computing power, and even greater resources will allow connecting them, going continuously from the parameters of the nuclear interaction to reactor calculations. However, such scheme will surely only be usable for applications if a few fine-tuning "knobs" are introduced in it. The values of these adjusted parameters will have to be calibrated versus differential and integral experimental constraints. © 2014.SCOPUS: ar.jinfo:eu-repo/semantics/publishe