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      Genomic diversity and ecology of human-associated Akkermansia species in the gut microbiome revealed by extensive metagenomic assembly

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          Abstract

          Background

          Akkermansia muciniphila is a human gut microbe with a key role in the physiology of the intestinal mucus layer and reported associations with decreased body mass and increased gut barrier function and health. Despite its biomedical relevance, the genomic diversity of A. muciniphila remains understudied and that of closely related species, except for A. glycaniphila, unexplored.

          Results

          We present a large-scale population genomics analysis of the Akkermansia genus using 188 isolate genomes and 2226 genomes assembled from 18,600 metagenomes from humans and other animals. While we do not detect A. glycaniphila, the Akkermansia strains in the human gut can be grouped into five distinct candidate species, including A. muciniphila, that show remarkable whole-genome divergence despite surprisingly similar 16S rRNA gene sequences. These candidate species are likely human-specific, as they are detected in mice and non-human primates almost exclusively when kept in captivity. In humans, Akkermansia candidate species display ecological co-exclusion, diversified functional capabilities, and distinct patterns of associations with host body mass. Analysis of CRISPR-Cas loci reveals new variants and spacers targeting newly discovered putative bacteriophages. Remarkably, we observe an increased relative abundance of Akkermansia when cognate predicted bacteriophages are present, suggesting ecological interactions. A. muciniphila further exhibits subspecies-level genetic stratification with associated functional differences such as a putative exo/lipopolysaccharide operon.

          Conclusions

          We uncover a large phylogenetic and functional diversity of the Akkermansia genus in humans. This variability should be considered in the ongoing experimental and metagenomic efforts to characterize the health-associated properties of A. muciniphila and related bacteria.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13059-021-02427-7.

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          Most cited references113

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            Basic local alignment search tool.

            A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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              RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

              Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                nicola.segata@unitn.it
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                14 July 2021
                14 July 2021
                2021
                : 22
                : 209
                Affiliations
                [1 ]GRID grid.11696.39, ISNI 0000 0004 1937 0351, Department CIBIO, , University of Trento, ; Trento, Italy
                [2 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, , University of Copenhagen, ; Copenhagen, Denmark
                [3 ]GRID grid.4818.5, ISNI 0000 0001 0791 5666, Laboratory of Microbiology, , Wageningen University, ; Wageningen, The Netherlands
                [4 ]GRID grid.419905.0, ISNI 0000 0001 0066 4948, Current address: Nestlé Institute of Health Sciences, Nestlé Research, Société des Produits Nestlé S.A., ; Lausanne, Switzerland
                [5 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Human Microbiome Research Program, Faculty of Medicine, , University of Helsinki, ; Helsinki, Finland
                [6 ]GRID grid.15667.33, ISNI 0000 0004 1757 0843, IEO, European Institute of Oncology IRCCS, ; Milan, Italy
                Author information
                http://orcid.org/0000-0002-1583-5794
                Article
                2427
                10.1186/s13059-021-02427-7
                8278651
                34261503
                3e4173cf-1bbf-4332-9201-8a153717ce2d
                © The Author(s) 2021

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

                History
                : 18 December 2020
                : 30 June 2021
                Funding
                Funded by: European Research Council
                Award ID: MetaPG-716575
                Award Recipient :
                Funded by: MIUR
                Award ID: RBFR13EWWI_001
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100010661, Horizon 2020 Framework Programme;
                Award ID: ONCOBIOME-825410
                Award ID: MASTER-818368 project
                Award Recipient :
                Funded by: National Cancer Institute of the National Institutes of Health
                Award ID: 1U01CA230551
                Award Recipient :
                Funded by: SIAM Gravitation Grant
                Award ID: 024.002.002
                Funded by: 2008 Spinoza Award of the Netherlands Organization for Scientific Research
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                Genetics
                Genetics

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