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Genomics of the Genus Bifidobacterium Reveals Species-Specific Adaptation to the Glycan-Rich Gut Environment

Christian Milani, Francesca Turroni, Sabrina Duranti, Gabriele Andrea Lugli, Leonardo Mancabelli, Chiara Ferrario, Douwe van Sinderen, Marco Ventura
H. L. Drake, Editor
Christian Milani
aLaboratory of Probiogenomics, Department of Life Sciences, University of Parma, Parma, Italy
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Francesca Turroni
aLaboratory of Probiogenomics, Department of Life Sciences, University of Parma, Parma, Italy
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Sabrina Duranti
aLaboratory of Probiogenomics, Department of Life Sciences, University of Parma, Parma, Italy
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Gabriele Andrea Lugli
aLaboratory of Probiogenomics, Department of Life Sciences, University of Parma, Parma, Italy
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Leonardo Mancabelli
aLaboratory of Probiogenomics, Department of Life Sciences, University of Parma, Parma, Italy
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Chiara Ferrario
aLaboratory of Probiogenomics, Department of Life Sciences, University of Parma, Parma, Italy
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Douwe van Sinderen
bAPC Microbiome Institute and School of Microbiology, National University of Ireland, Cork, Ireland
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Marco Ventura
aLaboratory of Probiogenomics, Department of Life Sciences, University of Parma, Parma, Italy
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H. L. Drake
University of Bayreuth
Roles: Editor
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DOI: 10.1128/AEM.03500-15
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    FIG 1

    Phylogenomic overview of the genus Bifidobacterium. A supertree based on the alignment of 413 core COGs (with a single representative identified for each bifidobacterial genome) was constructed in order to obtain a robust phylogenetic reconstruction. Phylogenetic clusters are highlighted with branches of the same color, and nodes with bootstrap values higher than 70% are marked with a purple dot. Circles surrounding the tree represent the approximate genome sizes (in red), relative percentage of genes predicted to be involved in carbohydrate metabolism and transport (in orange), relative percentage of genes predicted to have undergone horizontal gene transfer (in green), and relative percentage of genes predicted to have been subject to horizontal gene transfer or to be involved in carbohydrate metabolism and transport (in blue). The outermost layer represents the proportion of GH families (i.e., GH3, GH13, and GH43). E. coli, Escherichia coli; met., metabolism; ORFs, open reading frames. Bifidobacterial species names are colored based on their ecological origin. In addition, the tree in the lower part of the image represents the phylogeny of the host species from which bifidobacteria had been isolated. This tree was constructed with the Superfamily database and software (85).

  • FIG 2
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    FIG 2

    Gene gain and loss events in a reconstruction of data representing the family Bifidobacteriaceae. A tree was constructed based on information regarding the presence or absence of COGs for the whole Bifidobacteriaceae pan-genome. Each node is represented by a pie diagram showing the acquired COGs (in red) and the COGs derived from the previous node (in green). Furthermore, additional information is displayed at each node as follows: number of acquired genes, number of lost genes, and total number of COGs. The predicted Bifidobacterium ancestor is highlighted with a thick purple circle surrounding the pie diagram.

  • FIG 3
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    FIG 3

    Reconstruction of gene gain and loss events regarding genes encoding members of the GH3, GH13, and GH43 families in the family Bifidobacteriaceae. A tree was constructed using information related to the presence or absence of COGs for the whole Bifidobacteriaceae pan-genome. For each node, a pie diagram shows the acquired COGs (in red) and the COGs derived from the previous node (in green). Furthermore, for each node the number of GH family members acquired is reported. (Gene decay events were omitted to allow readability of the figure.) Numbers are indicated when multiple COGs of the same GH family were acquired: otherwise only one COG was gained. GH3, GH13, and GH43 are colored in blue, orange, and green, respectively.

  • FIG 4
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    FIG 4

    The glycobiome of the Bifidobacterium genus and some additional members of the Bifidobacteriaceae family. Panel a shows a comparative analysis of the bifidobacterial GH3, GH13, and GH43 repertoire against that found in other gut bacteria. The heat maps show GH prediction data from 2,721 sequenced bacterial strains belonging to bacterial orders residing in the human gut, identified by different color codes as explained in the underlying table. Data regarding Bifidobacteriales are highlighted in blue. In panel b, GH family profiles identified by the CAZy database were used to construct a hierarchical clustering of all tested species of the Bifidobacterium genus and additional members of the Bifidobacteriaceae family. This clustering highlights the presence of three distinct clusters named GHP/A, GHP/B, and GHP/C displaying a different repertoire of GHs. GH arsenal prediction for each analyzed Bifidobacterium species is represented by a bar plot, and the GH index (the number of GHs predicted in each genome normalized by genome size expressed as megabase pairs) is illustrated as an orange bar plot.

  • FIG 5
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    FIG 5

    The pan-genome of Bifidobacterium bifidum. Panel a shows a genome atlas representation of all publicly available genomes of the species B. bifidum in which each circle represents a different strain identified by a different color. Inside the genome atlas, a Venn diagram illustrates the number of identified core and unique genes. Panel b displays a heat map that summarizes the presence and number of particular genes predicted to be involved in mucin degradation in the analyzed B. bifidum genomes.

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Genomics of the Genus Bifidobacterium Reveals Species-Specific Adaptation to the Glycan-Rich Gut Environment
Christian Milani, Francesca Turroni, Sabrina Duranti, Gabriele Andrea Lugli, Leonardo Mancabelli, Chiara Ferrario, Douwe van Sinderen, Marco Ventura
Applied and Environmental Microbiology Feb 2016, 82 (4) 980-991; DOI: 10.1128/AEM.03500-15

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Genomics of the Genus Bifidobacterium Reveals Species-Specific Adaptation to the Glycan-Rich Gut Environment
Christian Milani, Francesca Turroni, Sabrina Duranti, Gabriele Andrea Lugli, Leonardo Mancabelli, Chiara Ferrario, Douwe van Sinderen, Marco Ventura
Applied and Environmental Microbiology Feb 2016, 82 (4) 980-991; DOI: 10.1128/AEM.03500-15
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