A Scale-Free, Fully Connected Global Transition Network Underlies Known Microbiome Diversity

Microbiomes are inherently linked by their structural similarity, yet the global features of such similarity are not clear. Here we propose as solution a search-based microbiome transition network. By traversing a composition-similarity based network of 177,022 microbiomes, we show that although the compositions are distinct by habitat, each microbiome is on-average only seven neighbors from any other microbiome on Earth, indicating the inherent homology of microbiome at the global scale. This network is scale-free, suggesting a high degree of stability and robustness in microbiome transition. By tracking the minimum spanning tree in this network, a global roadmap of microbiome dispersal was derived that tracks the potential paths of formulating and propagating microbiome diversity. Such search-based global microbiome networks, reconstructed within hours on just one computing node, provide a readily expanded reference for tracing the origin and evolution of existing or new microbiomes.

[1]  E. Alm,et al.  Unraveling the processes shaping mammalian gut microbiomes over evolutionary time , 2017, Nature Communications.

[2]  E. Papoutsakis,et al.  Interspecies Microbial Fusion and Large-Scale Exchange of Cytoplasmic Proteins and RNA in a Syntrophic Clostridium Coculture , 2020, mBio.

[3]  Kang Ning,et al.  Efficient search for similar microbial communities based on a novel indexing scheme and similarity score for metagenomic data , 2016 .

[4]  Jennifer M. Fettweis,et al.  The Integrative Human Microbiome Project , 2019, Nature.

[5]  I-Min A. Chen,et al.  IMG/M: a data management and analysis system for metagenomes , 2007, Nucleic Acids Res..

[6]  Andrew W. Brooks,et al.  Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History , 2016, PLoS biology.

[7]  R. Knight,et al.  Moving pictures of the human microbiome , 2011, Genome Biology.

[8]  Katherine H. Huang,et al.  Structure, Function and Diversity of the Healthy Human Microbiome , 2012, Nature.

[9]  Peer Bork,et al.  Extensive transmission of microbes along the gastrointestinal tract , 2019, eLife.

[10]  Zhongtang Yu,et al.  The Microbiota of Recreational Freshwaters and the Implications for Environmental and Public Health , 2016, Front. Microbiol..

[11]  Rick L. Stevens,et al.  A communal catalogue reveals Earth’s multiscale microbial diversity , 2017, Nature.

[12]  R. Knight,et al.  Microbiota and Host Nutrition across Plant and Animal Kingdoms. , 2015, Cell host & microbe.

[13]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[14]  Joseph R. Mihaljevic,et al.  Linking metacommunity theory and symbiont evolutionary ecology. , 2012, Trends in ecology & evolution.

[15]  J. Clemente,et al.  Diet Drives Convergence in Gut Microbiome Functions Across Mammalian Phylogeny and Within Humans , 2011, Science.

[16]  Kang Ning,et al.  GPU-Meta-Storms: computing the structure similarities among massive amount of microbial community samples using GPU , 2014, Bioinform..

[17]  Noah Fierer,et al.  Home Life: Factors Structuring the Bacterial Diversity Found within and between Homes , 2013, PloS one.

[18]  Zheng Sun,et al.  Identifying and Predicting Novelty in Microbiome Studies , 2018, mBio.

[19]  J. Kruskal.  On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[20]  Jure Leskovec,et al.  Planetary-scale views on a large instant-messaging network , 2008, WWW.

[21]  Rob Knight,et al.  Longitudinal analysis of microbial interaction between humans and the indoor environment , 2014, Science.

[22]  Kang Ning,et al.  Meta-Prism: Ultra-fast and highly accurate microbial community structure search utilizing dual indexing and parallel computation , 2020, Briefings Bioinform..

[23]  Natalia N. Ivanova,et al.  Microbiome Data Science: Understanding Our Microbial Planet. , 2016, Trends in microbiology.

[24]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[25]  Cesar Cardona,et al.  Earth microbial co-occurrence network reveals interconnection pattern across microbiomes , 2020, Microbiome.

[26]  Eric P. Nawrocki,et al.  An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea , 2011, The ISME Journal.

[27]  Jian Xu,et al.  Meta-Storms: efficient search for similar microbial communities based on a novel indexing scheme and similarity score for metagenomic data , 2012, Bioinform..

[28]  Siobhán Harty,et al.  Microbial transmission in animal social networks and the social microbiome , 2020, Nature Ecology & Evolution.

[29]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[30]  Curtis Huttenhower,et al.  Microbial Co-occurrence Relationships in the Human Microbiome , 2012, PLoS Comput. Biol..

[31]  Amnon Amir,et al.  Preservation Methods Differ in Fecal Microbiome Stability, Affecting Suitability for Field Studies , 2016, mSystems.

[32]  Kang Ning,et al.  Parallel-META 3: Comprehensive taxonomical and functional analysis platform for efficient comparison of microbial communities , 2017, Scientific Reports.

[33]  Luis Pedro Coelho,et al.  Structure and function of the global ocean microbiome , 2015, Science.

[34]  Albert-László Barabási,et al.  Scale-Free Networks: A Decade and Beyond , 2009, Science.

[35]  William A. Walters,et al.  Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample , 2010, Proceedings of the National Academy of Sciences.

[36]  Rob Knight,et al.  Multiple-Disease Detection and Classification across Cohorts via Microbiome Search , 2020, mSystems.