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      Microbial Co-occurrence Relationships in the Human Microbiome

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          Abstract

          The healthy microbiota show remarkable variability within and among individuals. In addition to external exposures, ecological relationships (both oppositional and symbiotic) between microbial inhabitants are important contributors to this variation. It is thus of interest to assess what relationships might exist among microbes and determine their underlying reasons. The initial Human Microbiome Project (HMP) cohort, comprising 239 individuals and 18 different microbial habitats, provides an unprecedented resource to detect, catalog, and analyze such relationships. Here, we applied an ensemble method based on multiple similarity measures in combination with generalized boosted linear models (GBLMs) to taxonomic marker (16S rRNA gene) profiles of this cohort, resulting in a global network of 3,005 significant co-occurrence and co-exclusion relationships between 197 clades occurring throughout the human microbiome. This network revealed strong niche specialization, with most microbial associations occurring within body sites and a number of accompanying inter-body site relationships. Microbial communities within the oropharynx grouped into three distinct habitats, which themselves showed no direct influence on the composition of the gut microbiota. Conversely, niches such as the vagina demonstrated little to no decomposition into region-specific interactions. Diverse mechanisms underlay individual interactions, with some such as the co-exclusion of Porphyromonaceae family members and Streptococcus in the subgingival plaque supported by known biochemical dependencies. These differences varied among broad phylogenetic groups as well, with the Bacilli and Fusobacteria, for example, both enriched for exclusion of taxa from other clades. Comparing phylogenetic versus functional similarities among bacteria, we show that dominant commensal taxa (such as Prevotellaceae and Bacteroides in the gut) often compete, while potential pathogens (e.g. Treponema and Prevotella in the dental plaque) are more likely to co-occur in complementary niches. This approach thus serves to open new opportunities for future targeted mechanistic studies of the microbial ecology of the human microbiome.

          Author Summary

          The human body is a complex ecosystem where microbes compete, and cooperate. These interactions can support health or promote disease, e.g. in dental plaque formation. The Human Microbiome Project collected and sequenced ca. 5,000 samples from 18 different body sites, including the airways, gut, skin, oral cavity and vagina. These data allowed the first assessment of significant patterns of co-presence and exclusion among human-associated bacteria. We combined sparse regression with an ensemble of similarity measures to predict microbial relationships within and between body sites. This captured known relationships in the dental plaque, vagina, and gut, and also predicted novel interactions involving members of under-characterized phyla such as TM7. We detected relationships necessary for plaque formation and differences in community composition among dominant members of the gut and vaginal microbiomes. Most relationships were strongly niche-specific, with only a few hub microorganisms forming links across multiple body areas. We also found that phylogenetic distance had a strong impact on the interaction type: closely related microorganisms co-occurred within the same niche, whereas most exclusive relationships occurred between more distantly related microorganisms. This establishes both the specific organisms and general principles by which microbial communities associated with healthy humans are assembled and maintained.

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          Most cited references 52

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          Emergence of scaling in random networks

          Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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            Defining the healthy "core microbiome" of oral microbial communities

            Background Most studies examining the commensal human oral microbiome are focused on disease or are limited in methodology. In order to diagnose and treat diseases at an early and reversible stage an in-depth definition of health is indispensible. The aim of this study therefore was to define the healthy oral microbiome using recent advances in sequencing technology (454 pyrosequencing). Results We sampled and sequenced microbiomes from several intraoral niches (dental surfaces, cheek, hard palate, tongue and saliva) in three healthy individuals. Within an individual oral cavity, we found over 3600 unique sequences, over 500 different OTUs or "species-level" phylotypes (sequences that clustered at 3% genetic difference) and 88 - 104 higher taxa (genus or more inclusive taxon). The predominant taxa belonged to Firmicutes (genus Streptococcus, family Veillonellaceae, genus Granulicatella), Proteobacteria (genus Neisseria, Haemophilus), Actinobacteria (genus Corynebacterium, Rothia, Actinomyces), Bacteroidetes (genus Prevotella, Capnocytophaga, Porphyromonas) and Fusobacteria (genus Fusobacterium). Each individual sample harboured on average 266 "species-level" phylotypes (SD 67; range 123 - 326) with cheek samples being the least diverse and the dental samples from approximal surfaces showing the highest diversity. Principal component analysis discriminated the profiles of the samples originating from shedding surfaces (mucosa of tongue, cheek and palate) from the samples that were obtained from solid surfaces (teeth). There was a large overlap in the higher taxa, "species-level" phylotypes and unique sequences among the three microbiomes: 84% of the higher taxa, 75% of the OTUs and 65% of the unique sequences were present in at least two of the three microbiomes. The three individuals shared 1660 of 6315 unique sequences. These 1660 sequences (the "core microbiome") contributed 66% of the reads. The overlapping OTUs contributed to 94% of the reads, while nearly all reads (99.8%) belonged to the shared higher taxa. Conclusions We obtained the first insight into the diversity and uniqueness of individual oral microbiomes at a resolution of next-generation sequencing. We showed that a major proportion of bacterial sequences of unrelated healthy individuals is identical, supporting the concept of a core microbiome at health.
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              Oral multispecies biofilm development and the key role of cell-cell distance.

              Growth of oral bacteria in situ requires adhesion to a surface because the constant flow of host secretions thwarts the ability of planktonic cells to grow before they are swallowed. Therefore, oral bacteria evolved to form biofilms on hard tooth surfaces and on soft epithelial tissues, which often contain multiple bacterial species. Because these biofilms are easy to study, they have become the paradigm of multispecies biofilms. In this Review we describe the factors involved in the formation of these biofilms, including the initial adherence to the oral tissues and teeth, cooperation between bacterial species in the biofilm, signalling between the bacteria and its role in pathogenesis, and the transfer of DNA between bacteria. In all these aspects distance between cells of different species is integral for oral biofilm growth.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                July 2012
                July 2012
                12 July 2012
                : 8
                : 7
                Affiliations
                [1 ]Department of Structural Biology, VIB, Brussels, Belgium
                [2 ]Department of Applied Biological Sciences (DBIT), Vrije Universiteit Brussel, Brussels, Belgium
                [3 ]Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
                [4 ]Department of Molecular Genetics, Forsyth Institute, Cambridge, Massachusetts, United States of America
                [5 ]Department of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston, Massachusetts, United States of America
                [6 ]Microbial Systems and Communities, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
                The Centre for Research and Technology, Hellas, Greece
                Author notes

                Conceived and designed the experiments: KF JFS JR CH. Performed the experiments: KF JFS CH. Analyzed the data: KF JFS JI NS DG JR CH. Contributed reagents/materials/analysis tools: NS. Wrote the paper: KF JFS JI JR CH.

                Article
                PCOMPBIOL-D-12-00158
                10.1371/journal.pcbi.1002606
                3395616
                22807668
                Faust 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.
                Page count
                Pages: 17
                Categories
                Research Article
                Biology
                Computational Biology
                Genomics
                Metagenomics
                Systems Biology
                Microbiology
                Microbial Ecology
                Mathematics
                Statistics
                Statistical Methods
                Medicine
                Global Health

                Quantitative & Systems biology

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