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      Meta-analysis of gut microbiome studies identifies disease-specific and shared responses

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          Abstract

          Hundreds of clinical studies have demonstrated associations between the human microbiome and disease, yet fundamental questions remain on how we can generalize this knowledge. Results from individual studies can be inconsistent, and comparing published data is further complicated by a lack of standard processing and analysis methods. Here we introduce the MicrobiomeHD database, which includes 28 published case–control gut microbiome studies spanning ten diseases. We perform a cross-disease meta-analysis of these studies using standardized methods. We find consistent patterns characterizing disease-associated microbiome changes. Some diseases are associated with over 50 genera, while most show only 10–15 genus-level changes. Some diseases are marked by the presence of potentially pathogenic microbes, whereas others are characterized by a depletion of health-associated bacteria. Furthermore, we show that about half of genera associated with individual studies are bacteria that respond to more than one disease. Thus, many associations found in case–control studies are likely not disease-specific but rather part of a non-specific, shared response to health and disease.

          Abstract

          Reported associations between the human microbiome and disease are often inconsistent. Here, Duvallet et al. perform a meta-analysis of 28 gut microbiome studies spanning ten diseases, and find associations that are likely not disease-specific but potentially part of a shared response to disease.

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          Most cited references19

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          Meta-analyses of human gut microbes associated with obesity and IBD.

          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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            Gut Microbiota Diversity and Human Diseases: Should We Reintroduce Key Predators in Our Ecosystem?

            Most of the Human diseases affecting westernized countries are associated with dysbiosis and loss of microbial diversity in the gut microbiota. The Western way of life, with a wide use of antibiotics and other environmental triggers, may reduce the number of bacterial predators leading to a decrease in microbial diversity of the Human gut. We argue that this phenomenon is similar to the process of ecosystem impoverishment in macro ecology where human activity decreases ecological niches, the size of predator populations, and finally the biodiversity. Such pauperization is fundamental since it reverses the evolution processes, drives life backward into diminished complexity, stability, and adaptability. A simple therapeutic approach could thus be to reintroduce bacterial predators and restore a bacterial diversity of the host microbiota.
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              Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights

              Shotgun metagenomic analysis of the human associated microbiome provides a rich set of microbial features for prediction and biomarker discovery in the context of human diseases and health conditions. However, the use of such high-resolution microbial features presents new challenges, and validated computational tools for learning tasks are lacking. Moreover, classification rules have scarcely been validated in independent studies, posing questions about the generality and generalization of disease-predictive models across cohorts. In this paper, we comprehensively assess approaches to metagenomics-based prediction tasks and for quantitative assessment of the strength of potential microbiome-phenotype associations. We develop a computational framework for prediction tasks using quantitative microbiome profiles, including species-level relative abundances and presence of strain-specific markers. A comprehensive meta-analysis, with particular emphasis on generalization across cohorts, was performed in a collection of 2424 publicly available metagenomic samples from eight large-scale studies. Cross-validation revealed good disease-prediction capabilities, which were in general improved by feature selection and use of strain-specific markers instead of species-level taxonomic abundance. In cross-study analysis, models transferred between studies were in some cases less accurate than models tested by within-study cross-validation. Interestingly, the addition of healthy (control) samples from other studies to training sets improved disease prediction capabilities. Some microbial species (most notably Streptococcus anginosus) seem to characterize general dysbiotic states of the microbiome rather than connections with a specific disease. Our results in modelling features of the “healthy” microbiome can be considered a first step toward defining general microbial dysbiosis. The software framework, microbiome profiles, and metadata for thousands of samples are publicly available at http://segatalab.cibio.unitn.it/tools/metaml.
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                Author and article information

                Contributors
                ejalm@mit.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                5 December 2017
                5 December 2017
                2017
                : 8
                : 1784
                Affiliations
                [1 ]ISNI 0000 0001 2341 2786, GRID grid.116068.8, Department of Biological Engineering, , Massachusetts Institute of Technology, ; Cambridge, MA 02139 USA
                [2 ]ISNI 0000 0001 2341 2786, GRID grid.116068.8, Center for Microbiome Informatics and Therapeutics, , Massachusetts Institute of Technology, ; Cambridge, MA 02139 USA
                [3 ]GRID grid.66859.34, The Broad Institute of MIT and Harvard, ; Cambridge, MA 02139 USA
                [4 ]ISNI 0000 0001 2106 9910, GRID grid.65499.37, Department of Biostatistics and Computational Biology, , Dana-Farber Cancer Institute, ; Boston, MA 02215 USA
                [5 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Biostatistics, , Harvard T.H. Chan School of Public Health, ; Boston, MA 02115 USA
                Article
                1973
                10.1038/s41467-017-01973-8
                5716994
                29209090
                c4a3c00a-a515-47d6-8791-13de35f599ed
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 May 2017
                : 30 October 2017
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