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      Microbiome-wide association studies link dynamic microbial consortia to disease.

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          Abstract

          Rapid advances in DNA sequencing, metabolomics, proteomics and computational tools are dramatically increasing access to the microbiome and identification of its links with disease. In particular, time-series studies and multiple molecular perspectives are facilitating microbiome-wide association studies, which are analogous to genome-wide association studies. Early findings point to actionable outcomes of microbiome-wide association studies, although their clinical application has yet to be approved. An appreciation of the complexity of interactions among the microbiome and the host's diet, chemistry and health, as well as determining the frequency of observations that are needed to capture and integrate this dynamic interface, is paramount for developing precision diagnostics and therapies that are based on the microbiome.

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          EMPeror: a tool for visualizing high-throughput microbial community data

          Yoshiki Vázquez-Baeza, Meg Pirrung, Antonio González González … (2013)
          Background As microbial ecologists take advantage of high-throughput sequencing technologies to describe microbial communities across ever-increasing numbers of samples, new analysis tools are required to relate the distribution of microbes among larger numbers of communities, and to use increasingly rich and standards-compliant metadata to understand the biological factors driving these relationships. In particular, the Earth Microbiome Project drives these needs by profiling the genomic content of tens of thousands of samples across multiple environment types. Findings Features of EMPeror include: ability to visualize gradients and categorical data, visualize different principal coordinates axes, present the data in the form of parallel coordinates, show taxa as well as environmental samples, dynamically adjust the size and transparency of the spheres representing the communities on a per-category basis, dynamically scale the axes according to the fraction of variance each explains, show, hide or recolor points according to arbitrary metadata including that compliant with the MIxS family of standards developed by the Genomic Standards Consortium, display jackknifed-resampled data to assess statistical confidence in clustering, perform coordinate comparisons (useful for procrustes analysis plots), and greatly reduce loading times and overall memory footprint compared with existing approaches. Additionally, ease of sharing, given EMPeror’s small output file size, enables agile collaboration by allowing users to embed these visualizations via emails or web pages without the need for extra plugins. Conclusions Here we present EMPeror, an open source and web browser enabled tool with a versatile command line interface that allows researchers to perform rapid exploratory investigations of 3D visualizations of microbial community data, such as the widely used principal coordinates plots. EMPeror includes a rich set of controllers to modify features as a function of the metadata. By being specifically tailored to the requirements of microbial ecologists, EMPeror thus increases the speed with which insight can be gained from large microbiome datasets.
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            Meta-analyses of human gut microbes associated with obesity and IBD.

            William Walters, Zech Xu, Rob Knight (2014)
            Recent studies have linked human gut microbes to obesity and inflammatory bowel disease, but consistent signals have been difficult to identify. Here we test for indicator taxa and general features of the microbiota that are generally consistent across studies of obesity and of IBD, focusing on studies involving high-throughput sequencing of the 16S rRNA gene (which we could process using a common computational pipeline). We find that IBD has a consistent signature across studies and allows high classification accuracy of IBD from non-IBD subjects, but that although subjects can be classified as lean or obese within each individual study with statistically significant accuracy, consistent with the ability of the microbiota to experimentally transfer this phenotype, signatures of obesity are not consistent between studies even when the data are analyzed with consistent methods. The results suggest that correlations between microbes and clinical conditions with different effect sizes (e.g. the large effect size of IBD versus the small effect size of obesity) may require different cohort selection and analysis strategies.
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              Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters.

              Peter Cimermancic, Marnix H Medema, Jan Claesen … (2014)
              Although biosynthetic gene clusters (BGCs) have been discovered for hundreds of bacterial metabolites, our knowledge of their diversity remains limited. Here, we used a novel algorithm to systematically identify BGCs in the extensive extant microbial sequencing data. Network analysis of the predicted BGCs revealed large gene cluster families, the vast majority uncharacterized. We experimentally characterized the most prominent family, consisting of two subfamilies of hundreds of BGCs distributed throughout the Proteobacteria; their products are aryl polyenes, lipids with an aryl head group conjugated to a polyene tail. We identified a distant relationship to a third subfamily of aryl polyene BGCs, and together the three subfamilies represent the largest known family of biosynthetic gene clusters, with more than 1,000 members. Although these clusters are widely divergent in sequence, their small molecule products are remarkably conserved, indicating for the first time the important roles these compounds play in Gram-negative cell biology. Copyright © 2014 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): Nature
                Title: Nature
                ISSN (Electronic): 1476-4687
                ISSN (Print): 0028-0836
                Publication date (Electronic): Jul 7 2016
                Volume: 535
                Issue: 7610
                Affiliations
                [1 ] Department of Surgery, University of Chicago, Chicago, Illinois 60637, USA.
                [2 ] Department of Pharmacology, University of California San Diego, La Jolla, California 92093, USA.
                [3 ] Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, USA.
                [4 ] Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, La Jolla, California 92093, USA.
                [5 ] Department of Pediatrics, University of California, San Diego School of Medicine, La Jolla, California 92093, USA.
                [6 ] Department of Computer Science and Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla, California 92093, USA.
                [7 ] Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, USA.
                Article
                Publisher Item ID: nature18850
                DOI: 10.1038/nature18850
                PubMed ID: 27383984
                SO-VID: 92516b2a-378d-46f1-a8b8-60a12f9bfab9
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