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      Strongyle Infection and Gut Microbiota: Profiling of Resistant and Susceptible Horses Over a Grazing Season

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          Abstract

          Gastrointestinal strongyles are a major threat to horses' health and welfare. Given that strongyles inhabit the same niche as the gut microbiota, they may interact with each other. These beneficial or detrimental interactions are unknown in horses and could partly explain contrasted susceptibility to infection between individuals. To address these questions, an experimental pasture trial with 20 worm-free female Welsh ponies (10 susceptible (S) and 10 resistant (R) to parasite infection) was implemented for 5 months. Fecal egg counts (FEC), hematological and biochemical data, body weight and gut microbiological composition were studied in each individual after 0, 24, 43, 92 and 132 grazing days. R and S ponies displayed divergent immunological profiles and slight differences in microbiological composition under worm-free conditions. After exposure to natural infection, the predicted R ponies exhibited lower FEC after 92 and 132 grazing days, and maintained higher levels of circulating monocytes and eosinophils, while lymphocytosis persisted in S ponies. Although the overall gut microbiota diversity and structure remained similar during the parasite infection between the two groups, S ponies exhibited a reduction of bacteria such as Ruminococcus, Clostridium XIVa and members of the Lachnospiraceae family, which may have promoted a disruption of mucosal homeostasis at day 92. In line with this hypothesis, an increase in pathobionts such as Pseudomonas and Campylobacter together with changes in several predicted immunological pathways, including pathogen sensing, lipid metabolism, and activation of signal transduction that are critical for the regulation of immune system and energy homeostasis were observed in S relative to R ponies. Moreover, S ponies displayed an increase in protozoan concentrations at day 92, suggesting that strongyles and protozoa may contribute to each other's success in the equine intestines. It could also be that S individuals favor the increase of these carbohydrate-degrading microorganisms to enhance the supply of nutrients needed to fight strongyle infection. Overall, this study provides a foundation to better understand the mechanisms that underpin the relationship between equines and natural strongyle infection. The profiling of horse immune response and gut microbiota should contribute to the development of novel biomarkers for strongyle infection.

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          Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences

          We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because more of our algorithm can be run in parallel relative to “classic” open-reference OTU picking, it makes open-reference OTU picking tractable on massive amplicon sequence data sets (though on smaller data sets, “classic” open-reference OTU clustering is often faster). We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed, and we estimate that our new algorithm runs in less than 1/5 the time than would be required of “classic” open reference OTU picking. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by “classic” open-reference OTU picking through comparisons on three well-studied datasets. An implementation of this algorithm is provided in the popular QIIME software package, which uses uclust for read clustering. All analyses were performed using QIIME’s uclust wrappers, though we provide details (aided by the open-source code in our GitHub repository) that will allow implementation of subsampled open-reference OTU picking independently of QIIME (e.g., in a compiled programming language, where runtimes should be further reduced). Our analyses should generalize to other implementations of these OTU picking algorithms. Finally, we present a comparison of parameter settings in QIIME’s OTU picking workflows and make recommendations on settings for these free parameters to optimize runtime without reducing the quality of the results. These optimized parameters can vastly decrease the runtime of uclust-based OTU picking in QIIME.
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            Adiposity, gut microbiota and faecal short chain fatty acids are linked in adult humans

            Background/Objectives: High dietary fibre intakes may protect against obesity by influencing colonic fermentation and the colonic microbiota. Though, recent studies suggest that increased colonic fermentation contributes to adiposity. Diet influences the composition of the gut microbiota. Previous research has not evaluated dietary intakes, body mass index (BMI), faecal microbiota and short chain fatty acid (SCFA) in the same cohort. Our objectives were to compare dietary intakes, faecal SCFA concentrations and gut microbial profiles in healthy lean (LN, BMI⩽25) and overweight or obese (OWOB, BMI>25) participants. Design: We collected demographic information, 3-day diet records, physical activity questionnaires and breath and faecal samples from 94 participants of whom 52 were LN and 42 OWOB. Results: Dietary intakes and physical activity levels did not differ significantly between groups. OWOB participants had higher faecal acetate (P=0.05), propionate (P=0.03), butyrate (P=0.05), valerate (P=0.03) and total short chain fatty acid (SCFA; P=0.02) concentrations than LN. No significant differences in Firmicutes to Bacteroides/Prevotella (F:B) ratio was observed between groups. However, in the entire cohort, Bacteroides/Prevotella counts were negatively correlated with faecal total SCFA (r=−0.32, P=0.002) and F:B ratio was positively correlated with faecal total SCFA (r=0.42, P<0.0001). Principal component analysis identified distinct gut microbiota and SCFA–F:B ratio components, which together accounted for 59% of the variation. F:B ratio loaded with the SCFA and not with the microbiota suggesting that SCFA and F:B ratio vary together and may be interrelated. Conclusions: The results support the hypothesis that colonic fermentation patterns may be altered, leading to different faecal SCFA concentrations in OWOB compared with LN humans. More in-depth studies looking at the metabolic fate of SCFA produced in LN and OWOB participants are needed in order to determine the role of SCFA in obesity.
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              Advancing our understanding of the human microbiome using QIIME.

              High-throughput DNA sequencing technologies, coupled with advanced bioinformatics tools, have enabled rapid advances in microbial ecology and our understanding of the human microbiome. QIIME (Quantitative Insights Into Microbial Ecology) is an open-source bioinformatics software package designed for microbial community analysis based on DNA sequence data, which provides a single analysis framework for analysis of raw sequence data through publication-quality statistical analyses and interactive visualizations. In this chapter, we demonstrate the use of the QIIME pipeline to analyze microbial communities obtained from several sites on the bodies of transgenic and wild-type mice, as assessed using 16S rRNA gene sequences generated on the Illumina MiSeq platform. We present our recommended pipeline for performing microbial community analysis and provide guidelines for making critical choices in the process. We present examples of some of the types of analyses that are enabled by QIIME and discuss how other tools, such as phyloseq and R, can be applied to expand upon these analyses. © 2013 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Front Physiol
                Front Physiol
                Front. Physiol.
                Frontiers in Physiology
                Frontiers Media S.A.
                1664-042X
                21 March 2018
                2018
                : 9
                : 272
                Affiliations
                [1] 1Department of Health Science, Open University of Catalonia , Barcelona, Spain
                [2] 2UMR 1282, Institut National de la Recherche Agronomique, Infectiologie et Santé Publique, Université François-Rabelais , Nouzilly, France
                [3] 3UMR 1313, Institut National de la Recherche Agronomique, AgroParisTech, Université Paris-Saclay , Jouy-en-Josas, France
                [4] 4UEPAO 1297, Institut National de la Recherche Agronomique, Unité Expérimentale de Physiologie Animale de l'Orfrasière , Nouzilly, France
                [5] 5UMR 1388, Institut National de la Recherche Agronomique, GenPhySE , Toulouse, France
                [6] 6UE-1277, Institut National de la Recherche Agronomique, Plate-Forme d'Infectiologie Expérimentale , Nouzilly, France
                [7] 7Pancosma SA , Geneva, Switzerland
                Author notes

                Edited by: Jean-Pierre Montani, University of Fribourg, Switzerland

                Reviewed by: Joan Elizabeth Edwards, Wageningen University & Research, Netherlands; Marcio Costa, Université de Montréal, Canada

                *Correspondence: Núria Mach nuria.mach@ 123456inra.fr

                This article was submitted to Integrative Physiology, a section of the journal Frontiers in Physiology

                †These authors have contributed equally to this work.

                Article
                10.3389/fphys.2018.00272
                5871743
                29618989
                53dcd8fa-01de-4c13-874f-e811501f5e3b
                Copyright © 2018 Clark, Sallé, Ballan, Reigner, Meynadier, Cortet, Koch, Riou, Blanchard and Mach.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 12 December 2017
                : 08 March 2018
                Page count
                Figures: 6, Tables: 0, Equations: 0, References: 109, Pages: 18, Words: 15181
                Categories
                Physiology
                Original Research

                Anatomy & Physiology
                bacteria,cyathostomin,anaerobic fungi,gut microbiome,immunity,horse,protozoa
                Anatomy & Physiology
                bacteria, cyathostomin, anaerobic fungi, gut microbiome, immunity, horse, protozoa

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