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      The large-scale organization of the bacterial network of ecological co-occurrence interactions

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          Abstract

          In their natural environments, microorganisms form complex systems of interactions. Understating the structure and organization of bacterial communities is likely to have broad medical and ecological consequences, yet a comprehensive description of the network of environmental interactions is currently lacking. Here, we mine co-occurrences in the scientific literature to construct such a network and demonstrate an expected pattern of association between the species’ lifestyle and the recorded number of co-occurring partners. We further focus on the well-annotated gut community and show that most co-occurrence interactions of typical gut bacteria occur within this community. The network is then clustered into species-groups that significantly correspond with natural occurring communities. The relationships between resource competition, metabolic yield and growth rate within the clusters correspond with the r/K selection theory. Overall, these results support the constructed clusters as a first approximation of a bacterial ecosystem model. This comprehensive collection of predicted communities forms a new data resource for further systematic characterization of the ecological design principals shaping communities. Here, we demonstrate its utility for predicting cooperation and inhibition within communities.

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          Most cited references48

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          The diversity and biogeography of soil bacterial communities.

          For centuries, biologists have studied patterns of plant and animal diversity at continental scales. Until recently, similar studies were impossible for microorganisms, arguably the most diverse and abundant group of organisms on Earth. Here, we present a continental-scale description of soil bacterial communities and the environmental factors influencing their biodiversity. We collected 98 soil samples from across North and South America and used a ribosomal DNA-fingerprinting method to compare bacterial community composition and diversity quantitatively across sites. Bacterial diversity was unrelated to site temperature, latitude, and other variables that typically predict plant and animal diversity, and community composition was largely independent of geographic distance. The diversity and richness of soil bacterial communities differed by ecosystem type, and these differences could largely be explained by soil pH (r(2) = 0.70 and r(2) = 0.58, respectively; P < 0.0001 in both cases). Bacterial diversity was highest in neutral soils and lower in acidic soils, with soils from the Peruvian Amazon the most acidic and least diverse in our study. Our results suggest that microbial biogeography is controlled primarily by edaphic variables and differs fundamentally from the biogeography of "macro" organisms.
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            Microbial diversity and function in soil: from genes to ecosystems.

            Soils sustain an immense diversity of microbes, which, to a large extent, remains unexplored. A range of novel methods, most of which are based on rRNA and rDNA analyses, have uncovered part of the soil microbial diversity. The next step in the era of microbial ecology is to extract genomic, evolutionary and functional information from bacterial artificial chromosome libraries of the soil community genomes (the metagenome). Sophisticated analyses that apply molecular phylogenetics, DNA microarrays, functional genomics and in situ activity measurements will provide huge amounts of new data, potentially increasing our understanding of the structure and function of soil microbial ecosystems, and the interactions that occur within them. This review summarizes the recent progress in studies of soil microbial communities with focus on novel methods and approaches that provide new insight into the relationship between phylogenetic and functional diversity.
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              A network of protein-protein interactions in yeast.

              A global analysis of 2,709 published interactions between proteins of the yeast Saccharomyces cerevisiae has been performed, enabling the establishment of a single large network of 2,358 interactions among 1,548 proteins. Proteins of known function and cellular location tend to cluster together, with 63% of the interactions occurring between proteins with a common functional assignment and 76% occurring between proteins found in the same subcellular compartment. Possible functions can be assigned to a protein based on the known functions of its interacting partners. This approach correctly predicts a functional category for 72% of the 1,393 characterized proteins with at least one partner of known function, and has been applied to predict functions for 364 previously uncharacterized proteins.
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                Author and article information

                Journal
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                July 2010
                July 2010
                27 February 2010
                27 February 2010
                : 38
                : 12
                : 3857-3868
                Affiliations
                1Blavatnik School of Computer Sciences, 2School of Medicine, 3School of Mathematical Science, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel, 4Department of Biomedical Informatics, Columbia University, NY 10032, New York, USA and 5Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel
                Author notes
                *To whom correspondence should be addressed. Tel: +972 3 6407864; Fax: +972 3 6409357; Email: shiri.freilich@ 123456gmail.com
                Article
                gkq118
                10.1093/nar/gkq118
                2896517
                20194113
                0fb07580-d39a-4d2d-973a-244ef55f98b0
                © The Author(s) 2010. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 1 November 2009
                : 8 February 2010
                : 9 February 2010
                Categories
                Computational Biology

                Genetics
                Genetics

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