ASaiM: A Galaxy-based framework to analyze microbiota data

Background: New generations of sequencing platforms coupled to numerous bioinformatics tools have led to rapid technological progress in metagenomics and metatranscriptomics to investigate complex microorganism communities. Nevertheless, a combination of different bioinformatic tools remains necessary to draw conclusions out of microbiota studies. Modular and user-friendly tools would greatly improve such studies. Findings: We therefore developed ASaiM, an Open-Source Galaxy-based framework dedicated to microbiota data analyses. ASaiM provides an extensive collection of tools to assemble, extract, explore, and visualize microbiota information from raw metataxonomic, metagenomic, or metatranscriptomic sequences. To guide the analyses, several customizable workflows are included and are supported by tutorials and Galaxy interactive tours, which guide users through the analyses step by step. ASaiM is implemented as a Galaxy Docker flavour. It is scalable to thousands of datasets but also can be used on a normal PC. The associated source code is available under Apache 2 license at https://github.com/ASaiM/framework and documentation can be found online (http://asaim.readthedocs.io). Conclusions: Based on the Galaxy framework, ASaiM offers a sophisticated environment with a variety of tools, workflows, documentation, and training to scientists working on complex microorganism communities. It makes analysis and exploration analyses of microbiota data easy, quick, transparent, reproducible, and shareable

Unveiling the Correlation between Inadequate Energy/Macronutrient Intake and Clinical Alterations in Volunteers at Risk of Metabolic Syndrome by a Predictive Model

Although lifestyle-based interventions are the most effective to prevent metabolic syndrome (MetS), there is no definitive agreement on which nutritional approach is the best. The aim of the present retrospective analysis was to identify a multivariate model linking energy and macronutrient intake to the clinical features of MetS. Volunteers at risk of MetS (F = 77, M = 80) were recruited in four European centres and finally eligible for analysis. For each subject, the daily energy and nutrient intake was estimated using the EPIC questionnaire and a 24-h dietary recall, and it was compared with the dietary reference values. Then we built a predictive model for a set of clinical outcomes computing shifts from recommended intake thresholds. The use of the ridge regression, which optimises prediction performances while retaining information about the role of all the nutritional variables, allowed us to assess if a clinical outcome was manly dependent on a single nutritional variable, or if its prediction was characterised by more complex interactions between the variables. The model appeared suitable for shedding light on the complexity of nutritional variables, which effects could be not evident with univariate analysis and must be considered in the framework of the reciprocal influence of the other variables

Activation of phagocytic cells and inflammatory response during experimental magnesium deficiency

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In vitro susceptibility of Aspergillus spp. clinical isolates to albendazole

International audienceThe in vitro antifungal activity of albendazole, a benzimidazole widely used as an antihelmintic drug in humans, was investigated and assessed for its activity against Aspergillus spp. Forty-eight isolates, representing the most frequent species found in human pathology [Aspergillus fumigatus (n = 27), Aspergillus flavus (n = 10), Aspergillus terreus (n = 7), Aspergillus nidulans (n = 3) and Aspergillus niger (n = 1)], and one quality control strain (A. niger ATCC 9804 83435) were tested according to the NCCLS M38-P methodology for moulds. All the strains were susceptible to albendazole, with homogeneous MICs for each species; three strains were resistant to itraconazole

Fecal microbiota variation across the lifespan of the healthy laboratory rat.

Laboratory rats are commonly used in life science research as a model for human biology and disease, but the composition and development of their gut microbiota during life is poorly understood. We determined the fecal microbiota composition of healthy Sprague Dawley laboratory rats from 3 weeks to 2 y of age, kept under controlled environmental and dietary conditions. Additionally, we determined fecal short-chain fatty acid profiles, and we compared the rat fecal microbiota with that of mice and humans. Gut microbiota and to a lesser extent SCFAs profiles separated rats into 3 different clusters according to age: before weaning, first year of life (12- to 26-week-old animals) and second year of life (52- to 104-week-old). A core of 46 bacterial species was present in all rats but its members' relative abundance progressively decreased with age. This was accompanied by an increase of microbiota α-diversity, likely due to the acquisition of environmental microorganisms during the lifespan. Contrastingly, the functional profile of the microbiota across animal species became more similar upon aging. Lastly, the microbiota of rats and mice were most similar to each other but at the same time the microbiota profile of rats was more similar to that of humans than was the microbiota profile of mice. These data offer an explanation as to why germ-free rats are more efficient recipients and retainers of human microbiota than mice. Furthermore, experimental design should take into account dynamic changes in the microbiota of model animals considering that their changing gut microbiota interacts with their physiology.NG received a PhD scholar grant from the French “Ministère de l'Enseignement et de la Recherche”. WT received a PhD scholar grant from the Auvergne council. PPC received a post-doctoral fellowship from “Université d'Auvergne”. Work in PWOT's laboratory was supported by Science Foundation Ireland through a Centre award to the APC Microbiome Institute (SFI/12/RC/2273)