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      Host lifestyle affects human microbiota on daily timescales

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          Abstract

          Background

          Disturbance to human microbiota may underlie several pathologies. Yet, we lack a comprehensive understanding of how lifestyle affects the dynamics of human-associated microbial communities.

          Results

          Here, we link over 10,000 longitudinal measurements of human wellness and action to the daily gut and salivary microbiota dynamics of two individuals over the course of one year. These time series show overall microbial communities to be stable for months. However, rare events in each subjects’ life rapidly and broadly impacted microbiota dynamics. Travel from the developed to the developing world in one subject led to a nearly two-fold increase in the Bacteroidetes to Firmicutes ratio, which reversed upon return. Enteric infection in the other subject resulted in the permanent decline of most gut bacterial taxa, which were replaced by genetically similar species. Still, even during periods of overall community stability, the dynamics of select microbial taxa could be associated with specific host behaviors. Most prominently, changes in host fiber intake positively correlated with next-day abundance changes among 15% of gut microbiota members.

          Conclusions

          Our findings suggest that although human-associated microbial communities are generally stable, they can be quickly and profoundly altered by common human actions and experiences.

          Electronic supplementary material

          The online version of this article (doi:10.1186/gb-2014-15-7-r89) contains supplementary material, which is available to authorized users.

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          Most cited references24

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          Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample.

          The ongoing revolution in high-throughput sequencing continues to democratize the ability of small groups of investigators to map the microbial component of the biosphere. In particular, the coevolution of new sequencing platforms and new software tools allows data acquisition and analysis on an unprecedented scale. Here we report the next stage in this coevolutionary arms race, using the Illumina GAIIx platform to sequence a diverse array of 25 environmental samples and three known "mock communities" at a depth averaging 3.1 million reads per sample. We demonstrate excellent consistency in taxonomic recovery and recapture diversity patterns that were previously reported on the basis of metaanalysis of many studies from the literature (notably, the saline/nonsaline split in environmental samples and the split between host-associated and free-living communities). We also demonstrate that 2,000 Illumina single-end reads are sufficient to recapture the same relationships among samples that we observe with the full dataset. The results thus open up the possibility of conducting large-scale studies analyzing thousands of samples simultaneously to survey microbial communities at an unprecedented spatial and temporal resolution.
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            Temporal dynamics of the human vaginal microbiota.

            Elucidating the factors that impinge on the stability of bacterial communities in the vagina may help in predicting the risk of diseases that affect women's health. Here, we describe the temporal dynamics of the composition of vaginal bacterial communities in 32 reproductive-age women over a 16-week period. The analysis revealed the dynamics of five major classes of bacterial communities and showed that some communities change markedly over short time periods, whereas others are relatively stable. Modeling community stability using new quantitative measures indicates that deviation from stability correlates with time in the menstrual cycle, bacterial community composition, and sexual activity. The women studied are healthy; thus, it appears that neither variation in community composition per se nor higher levels of observed diversity (co-dominance) are necessarily indicative of dysbiosis.
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              Spurious regressions in econometrics

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                Author and article information

                Contributors
                l.d@duke.edu
                amaterna@clcbio.com
                yonatanf@mit.edu
                baptista@mit.edu
                matthew.blackburn@epfl.ch
                aperrott@mit.edu
                serdman@mit.edu
                ejalm@mit.edu
                Journal
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1465-6906
                1465-6914
                25 July 2014
                25 July 2014
                2014
                : 15
                : 7
                : R89
                Affiliations
                [ ]Society of Fellows, Harvard University, Cambridge, MA 02138 USA
                [ ]FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138 USA
                [ ]QIAGEN Aarhus A/S, Silkeborgvej 2, 8000 Aarhus C, Denmark
                [ ]Computational & Systems Biology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
                [ ]Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
                [ ]Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
                [ ]Department of Civil & Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
                [ ]Division of Comparative Medicine, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
                [ ]Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
                [ ]The Broad Institute of MIT and Harvard, Cambridge, MA 02139 USA
                [ ]Molecular Genetics & Microbiology and Center for Genomic & Computational Biology, Duke University, Durham, NC 27708 USA
                Article
                3286
                10.1186/gb-2014-15-7-r89
                4405912
                25146375
                2a122bea-822b-4c09-85c3-082c63993d61
                © David et al.; licensee BioMed Central Ltd. 2014

                This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 23 November 2013
                : 25 July 2014
                Categories
                Research
                Custom metadata
                © The Author(s) 2014

                Genetics
                Genetics

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