Bacteriophages isolated from Lake Michigan demonstrate broad host-range across several bacterial phyla

Malki, Kema; Kula, Alex; Bruder, Katherine; Sible, Emily; Hatzopoulos, Thomas; Steidel, Stephanie; Watkins, Siobhan C.; Putonti, Catherine

doi:10.1186/s12985-015-0395-0

Cited by 72 publications

(73 citation statements)

References 23 publications

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“…Such population-genetic assessment of phage evolution, coupled with the ecological implications of genome heterogeneity, will inform how to define nodes in future iterations of the ecological network developed here. Even though we are hesitant to speculate on phage host ranges at low taxonomic levels in our dataset, the data does agree with previous reports of instances of broad phage host range [60,75]. Additionally, visualization of our dataset interactions using the heat map approach previously used in other host range studies, suggests a trend toward modular and nested tropism, but we do not have the strain-level resolution that powered those previous experimental studies.…”

Section: Discussionsupporting

confidence: 75%

Biogeography & environmental conditions shape bacteriophage-bacteria networks across the human microbiome

Hannigan

Duhaime

Koutra

et al. 2017

Preprint

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19Viruses and bacteria are critical components of the human microbiome and play important roles in health and 20 disease. Most previous work has relied on studying bacteria and viruses independently, thereby reducing 21 them to two separate communities. Such approaches are unable to capture how these microbial communities 22 interact, such as through processes that maintain community robustness or allow phage-host populations 23 to co-evolve. We implemented a network-based analytical approach to describe phage-bacteria network 24 diversity throughout the human body. We built these community networks using a machine learning algorithm 25 to predict which phages could infect which bacteria in a given microbiome. Our algorithm was applied to 26 paired viral and bacterial metagenomic sequence sets from three previously published human cohorts. We 27 organized the predicted interactions into networks that allowed us to evaluate phage-bacteria connectedness 28 across the human body. We observed evidence that gut and skin network structures were person-specific 29 and not conserved among cohabitating family members. High-fat diets appeared to be associated with 30 less connected networks. Network structure differed between skin sites, with those exposed to the external Author Summary 37The human microbiome, the collection of microbial communities that colonize the human body, is a crucial 38 component to health and disease. Two major components to the human microbiome are the bacterial and 39 viral communities. These communities have primarily been studied separately using metrics of community 40 composition and diversity. These approaches have failed to capture the complex dynamics of interacting 41 bacteria and phage communities, which frequently share genetic information and work together to maintain 42 ecosystem homestatsis (e.g. kill-the-winner dynamics). Removal of bacteria or phage can disrupt or even 43 collapse those ecosystems. Relationship-based network approaches allow us to capture this interaction 44 information. Using this network-based approach with three independent human cohorts, we were able to 45 present an initial understanding of how phage-bacteria networks differ throughout the human body, so as to 46 provide a baseline for future studies of how and why microbiome networks differ in disease states.

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Section: Discussionsupporting

confidence: 75%

Biogeography & environmental conditions shape bacteriophage-bacteria networks across the human microbiome

Hannigan

Duhaime

Koutra

et al. 2017

Preprint

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“…Host prediction results on crAssphage also highlight how the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^*$\end{document} method can be useful for hypothesis generation. It is generally recognized that viruses rarely infect hosts from multiple phyla, but a few cases have been reported (53). The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^*$\end{document} method predicted two possible host phyla for crAssphage, Bacteroidetes and Fusobacteria, which could be tested directly if and when crAssphage viruses are isolated.…”

Section: Discussionmentioning

confidence: 99%

Alignment-free $d_2^*$ oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences

Ahlgren

Ren

et al. 2016

Nucleic Acids Research

246

295

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Viruses and their host genomes often share similar oligonucleotide frequency (ONF) patterns, which can be used to predict the host of a given virus by finding the host with the greatest ONF similarity. We comprehensively compared 11 ONF metrics using several k-mer lengths for predicting host taxonomy from among ∼32 000 prokaryotic genomes for 1427 virus isolate genomes whose true hosts are known. The background-subtracting measure \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^*$\end{document} at k = 6 gave the highest host prediction accuracy (33%, genus level) with reasonable computational times. Requiring a maximum dissimilarity score for making predictions (thresholding) and taking the consensus of the 30 most similar hosts further improved accuracy. Using a previous dataset of 820 bacteriophage and 2699 bacterial genomes, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^*$\end{document} host prediction accuracies with thresholding and consensus methods (genus-level: 64%) exceeded previous Euclidian distance ONF (32%) or homology-based (22-62%) methods. When applied to metagenomically-assembled marine SUP05 viruses and the human gut virus crAssphage, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^*$\end{document}-based predictions overlapped (i.e. some same, some different) with the previously inferred hosts of these viruses. The extent of overlap improved when only using host genomes or metagenomic contigs from the same habitat or samples as the query viruses. The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^*$\end{document} ONF method will greatly improve the characterization of novel, metagenomic viruses.

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“…The previously defined Enterobacteriaceae authentic phage clusters very rarely include phages that are known to infect hosts from other bacterial taxonomic families; we have found only two exceptions. Stenotrophomonas (a member of the Xanthomonadaceae bacterial family) phage IME15 is remarkable in its inclusion within the Enterobacteriaceae T7-like phage cluster (lytic cluster 5 in Grose and Casjens, 2014), and recently several phages that belong to the EMCL-117-like cluster (lytic cluster 27) have been reported to be broad spectrum, infecting both E. coli in the Enterobacteriaceae family and Pseudomonas aeruginosa in the Pseudomonadaceae family (Malki et al , 2015). …”

Section: Introductionmentioning

confidence: 99%

Contributions of P2- and P22-like prophages to understanding the enormous diversity and abundance of tailed bacteriophages

Casjens

Grose

2016

Virology

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We identified 9371 tailed phage prophages of 20 known types in reported complete genome sequences of 3298 bacteria in the Salmonella genus. These include 4758 P2 type and 744 P22 type prophages. The latter prophage types were found in the genome sequences of 127 and 24 bacterial host genera, increasing the known host ranges of phages in these groups by 114 and 20 genera, respectively. These prophage nucleotide sequences displayed much more diversity than was previously known from the 48 P2 and 24 P22 type authentic phages whose genomes have been sequenced. More detailed analysis of these prophage sequences indicated that major capsid protein (MCP) gene exchange between tailed phage clusters or types is extremely rare and that P22 prophage-encoded tailspikes correspond perfectly with their hosts’ surface polysaccharide structure; thus, MCP and tailspike sequences accurately predict tailed phage type (and thus lifestyle) and host cell surface polysaccharide structure, respectively.

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Bacteriophages isolated from Lake Michigan demonstrate broad host-range across several bacterial phyla

Cited by 72 publications

References 23 publications

Biogeography & environmental conditions shape bacteriophage-bacteria networks across the human microbiome

Biogeography & environmental conditions shape bacteriophage-bacteria networks across the human microbiome

Alignment-free $d_2^*$ oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences

Contributions of P2- and P22-like prophages to understanding the enormous diversity and abundance of tailed bacteriophages

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