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      Taxon abundance, diversity, co-occurrence and network analysis of the ruminal microbiota in response to dietary changes in dairy cows

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          Abstract

          The ruminal microbiome, comprising large numbers of bacteria, ciliate protozoa, archaea and fungi, responds to diet and dietary additives in a complex way. The aim of this study was to investigate the benefits of increasing the depth of the community analysis in describing and explaining responses to dietary changes. Quantitative PCR, ssu rRNA amplicon based taxa composition, diversity and co-occurrence network analyses were applied to ruminal digesta samples obtained from four multiparous Nordic Red dairy cows fitted with rumen cannulae. The cows received diets with forage:concentrate ratio either 35:65 (diet H) or 65:35 (L), supplemented or not with sunflower oil (SO) (0 or 50 g/kg diet dry matter), supplied in a 4 × 4 Latin square design with a 2 × 2 factorial arrangement of treatments and four 35-day periods. Digesta samples were collected on days 22 and 24 and combined. QPCR provided a broad picture in which a large fall in the abundance of fungi was seen with SO in the H but not the L diet. Amplicon sequencing showed higher community diversity indices in L as compared to H diets and revealed diet specific taxa abundance changes, highlighting large differences in protozoal and fungal composition. Methanobrevibacter ruminantium and Mbb. gottschalkii dominated archaeal communities, and their abundance correlated negatively with each other. Co-occurrence network analysis provided evidence that no microbial domain played a more central role in network formation, that some minor-abundance taxa were at nodes of highest centrality, and that microbial interactions were diet specific. Networks added new dimensions to our understanding of the diet effect on rumen microbial community interactions.

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          We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.
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            Ocean plankton. Determinants of community structure in the global plankton interactome.

            Species interaction networks are shaped by abiotic and biotic factors. Here, as part of the Tara Oceans project, we studied the photic zone interactome using environmental factors and organismal abundance profiles and found that environmental factors are incomplete predictors of community structure. We found associations across plankton functional types and phylogenetic groups to be nonrandomly distributed on the network and driven by both local and global patterns. We identified interactions among grazers, primary producers, viruses, and (mainly parasitic) symbionts and validated network-generated hypotheses using microscopy to confirm symbiotic relationships. We have thus provided a resource to support further research on ocean food webs and integrating biological components into ocean models.
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              Impact of diet and individual variation on intestinal microbiota composition and fermentation products in obese men.

              There is growing interest in understanding how diet affects the intestinal microbiota, including its possible associations with systemic diseases such as metabolic syndrome. Here we report a comprehensive and deep microbiota analysis of 14 obese males consuming fully controlled diets supplemented with resistant starch (RS) or non-starch polysaccharides (NSPs) and a weight-loss (WL) diet. We analyzed the composition, diversity and dynamics of the fecal microbiota on each dietary regime by phylogenetic microarray and quantitative PCR (qPCR) analysis. In addition, we analyzed fecal short chain fatty acids (SCFAs) as a proxy of colonic fermentation, and indices of insulin sensitivity from blood samples. The diet explained around 10% of the total variance in microbiota composition, which was substantially less than the inter-individual variance. Yet, each of the study diets induced clear and distinct changes in the microbiota. Multiple Ruminococcaceae phylotypes increased on the RS diet, whereas mostly Lachnospiraceae phylotypes increased on the NSP diet. Bifidobacteria decreased significantly on the WL diet. The RS diet decreased the diversity of the microbiota significantly. The total 16S ribosomal RNA gene signal estimated by qPCR correlated positively with the three major SCFAs, while the amount of propionate specifically correlated with the Bacteroidetes. The dietary responsiveness of the individual's microbiota varied substantially and associated inversely with its diversity, suggesting that individuals can be stratified into responders and non-responders based on the features of their intestinal microbiota.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                13 July 2017
                2017
                : 12
                : 7
                : e0180260
                Affiliations
                [1 ] Green Technology, Natural Resources Institute Finland, Jokioinen, Finland
                [2 ] Bio-based business and industry, Natural Resources Institute Finland, Jokioinen, Finland
                [3 ] Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, United Kingdom
                [4 ] Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
                Wageningen University, NETHERLANDS
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: EN KJS JV.

                • Formal analysis: IT DF LB MT.

                • Funding acquisition: EN KJS JV.

                • Investigation: IT ARB LV.

                • Writing – original draft: IT RJW MT DF JV.

                • Writing – review & editing: DF LB MT RJW ARB LV EN JV MK.

                Author information
                http://orcid.org/0000-0002-0752-9551
                Article
                PONE-D-17-02713
                10.1371/journal.pone.0180260
                5509137
                28704445
                ab39f741-3cda-4738-afb2-17d59ce640df
                © 2017 Tapio et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 January 2017
                : 13 June 2017
                Page count
                Figures: 5, Tables: 1, Pages: 21
                Funding
                Funded by: Finnish Ministry of Agriculture and Forestry
                Award ID: 2908234
                Award Recipient :
                Study was funded by the Finnish Ministry of Agriculture and Forestry as part of the GreenDairy Project (Developing Genetic and Nutritional Tools to Mitigate the Environmental Impact of Milk Production; Project No. 2908234). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Nutrition
                Diet
                Medicine and Health Sciences
                Nutrition
                Diet
                Biology and Life Sciences
                Organisms
                Bacteria
                Biology and Life Sciences
                Organisms
                Protozoans
                Biology and Life Sciences
                Organisms
                Fungi
                Biology and Life Sciences
                Microbiology
                Archaean Biology
                Biology and Life Sciences
                Microbiology
                Archaean Biology
                Archaeal Taxonomy
                Biology and Life Sciences
                Taxonomy
                Microbial Taxonomy
                Archaeal Taxonomy
                Computer and Information Sciences
                Data Management
                Taxonomy
                Microbial Taxonomy
                Archaeal Taxonomy
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbial Pathogens
                Bacterial Pathogens
                Clostridium
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogens
                Microbial Pathogens
                Bacterial Pathogens
                Clostridium
                Biology and Life Sciences
                Organisms
                Bacteria
                Gut Bacteria
                Clostridium
                Biology and Life Sciences
                Biochemistry
                Lipids
                Oils
                Custom metadata
                Data are available within the Dryad Digital Repository (doi: 10.5061/dryad.t6t0q).

                Uncategorized
                Uncategorized

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