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Tracking the Rules of Transmission and Introgression with Networks

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  • Authors: Chloé Vigliotti*1, Cédric Bicep*2, Eric Bapteste3, Philippe Lopez4, Eduardo Corel5
  • Editors: Fernando Baquero6, Emilio Bouza7, J.A. Gutiérrez-Fuentes8, Teresa M. Coque9
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Sorbonne Université, CNRS, Institut de Biologie Paris Seine (IBPS), Laboratoire Evolution Paris Seine, F-75005 Paris, France; 2: Sorbonne Université, CNRS, Institut de Biologie Paris Seine (IBPS), Laboratoire Evolution Paris Seine, F-75005 Paris, France; 3: Sorbonne Université, CNRS, Institut de Biologie Paris Seine (IBPS), Laboratoire Evolution Paris Seine, F-75005 Paris, France; 4: Sorbonne Université, CNRS, Institut de Biologie Paris Seine (IBPS), Laboratoire Evolution Paris Seine, F-75005 Paris, France; 5: Sorbonne Université, CNRS, Institut de Biologie Paris Seine (IBPS), Laboratoire Evolution Paris Seine, F-75005 Paris, France; 6: Hospital Ramón y Cajal (IRYCIS), Madrid, Spain; 7: Hospital Ramón y Cajal (IRYCIS), Madrid, Spain; 8: Complutensis University, Madrid, Spain; 9: Hospital Ramón y Cajal (IRYCIS), Madrid, Spain
  • Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.MTBP-0008-2016
  • Received 14 April 2017 Accepted 24 April 2017 Published 12 April 2018
  • Chloé Vigliotti, [email protected]
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  • Abstract:

    Understanding how an animal organism and its gut microbes form an integrated biological organization, known as a holobiont, is becoming a central issue in biological studies. Such an organization inevitably involves a complex web of transmission processes that occur on different scales in time and space, across microbes and hosts. Network-based models are introduced in this chapter to tackle aspects of this complexity and to better take into account vertical and horizontal dimensions of transmission. Two types of network-based models are presented, sequence similarity networks and bipartite graphs. One interest of these networks is that they can consider a rich diversity of important players in microbial evolution that are usually excluded from evolutionary studies, like plasmids and viruses. These methods bring forward the notion of “gene externalization,” which is defined as the presence of redundant copies of prokaryotic genes on mobile genetic elements (MGEs), and therefore emphasizes a related although distinct process from lateral gene transfer between microbial cells. This chapter introduces guidelines to the construction of these networks, reviews their analysis, and illustrates their possible biological interpretations and uses. The application to human gut microbiomes shows that sequences present in a higher diversity of MGEs have both biased functions and a broader microbial and human host range. These results suggest that an “externalized gut metagenome” is partly common to humans and benefits the gut microbial community. We conclude that testing relationships between microbial genes, microbes, and their animal hosts, using network-based methods, could help to unravel additional mechanisms of transmission in holobionts.

  • Keywords: networks; evolution; similarity networks; microbiome; metagenomes; bipartite graphs; graph theory

  • Citation: Vigliotti* C, Bicep* C, Bapteste E, Lopez P, Corel E. 2018. Tracking the Rules of Transmission and Introgression with Networks. Microbiol Spectrum 6(2):MTBP-0008-2016. doi:10.1128/microbiolspec.MTBP-0008-2016.

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/content/journal/microbiolspec/10.1128/microbiolspec.MTBP-0008-2016
2018-04-12
2018-12-16

Abstract:

Understanding how an animal organism and its gut microbes form an integrated biological organization, known as a holobiont, is becoming a central issue in biological studies. Such an organization inevitably involves a complex web of transmission processes that occur on different scales in time and space, across microbes and hosts. Network-based models are introduced in this chapter to tackle aspects of this complexity and to better take into account vertical and horizontal dimensions of transmission. Two types of network-based models are presented, sequence similarity networks and bipartite graphs. One interest of these networks is that they can consider a rich diversity of important players in microbial evolution that are usually excluded from evolutionary studies, like plasmids and viruses. These methods bring forward the notion of “gene externalization,” which is defined as the presence of redundant copies of prokaryotic genes on mobile genetic elements (MGEs), and therefore emphasizes a related although distinct process from lateral gene transfer between microbial cells. This chapter introduces guidelines to the construction of these networks, reviews their analysis, and illustrates their possible biological interpretations and uses. The application to human gut microbiomes shows that sequences present in a higher diversity of MGEs have both biased functions and a broader microbial and human host range. These results suggest that an “externalized gut metagenome” is partly common to humans and benefits the gut microbial community. We conclude that testing relationships between microbial genes, microbes, and their animal hosts, using network-based methods, could help to unravel additional mechanisms of transmission in holobionts.

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Image of FIGURE 1
FIGURE 1

This figure presents different transmissions between holobionts, and between a holobiont and its environment. The holobiont is composed of two parts: the host and its microbial communities (microbiome from gut, skin, oral, etc.). When a host gives birth to another, the parent brings to its offspring mitochondria, cytoplasm, and genes (pink arrows). The transmission is from the parent to the progeny. In some cases, like in mammals, the mother gives microbiota and microbiome to her children (pink dashed arrow). A host from a holobiont may also bring to the microbial community of another holobiont microbes, MGEs (viruses and plasmids), genes, or metabolites (black arrow between holobionts). Otherwise, a holobiont may exchange with the environment MGEs (plasmids and viruses) or microbes (black arrows between holobiont and environment). Then, in a microbial community there are transmissions between the different elements: transmissions between microbes (for example, by reproduction [purple arrows] or lateral gene transfer [green arrows]) or transmission between microbes and MGEs (red arrows). In this case we talk about externalization.

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.MTBP-0008-2016
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FIGURE 2

(i) Sequence similarity network (SSN). The SSN is built by comparing ORFs from all lizards’ gut microbiomes, using an all-against-all BLASTP. The SSN contains five CCs, also called gene families. Nodes are ORFs and are colored depending on their taxonomic annotation (see legend). If two nodes are similar at a determined percentage of identity (e.g., 95%), then they are linked by an edge. (ii) Bipartite graph of lizards-gene families. Type I nodes are lizards, colored depending on their diet, and type II nodes are the five gene families described in part i. There is an edge between a type I node and a type II node if in the gene family you can find a sequence that is contained in the lizards’ gut microbiomes of the type I node. (iii) Bipartite graph of lizards-microbial classes. Type I nodes are lizards, and type II nodes are microbial classes. If in one lizard’s gut microbiome a microbial class is found, then there is a link between the type I node associated and the type II node associated. Type I nodes are colored depending on the diet of the lizard, and type II nodes are colored depending on the microbial class of ORFs. (iv) Bipartite graph of microbial classes-gene families. Type I nodes are microbial classes, and type II nodes are the five gene families described in part i. There is an edge between a type I node and a type II node if at least one ORF of the gene family of the type II node is from the microbial class of the type I node.

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.MTBP-0008-2016
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FIGURE 3

Functional distributions were plotted for the three classes of clusters; resident clusters are in black, potentially externalized clusters in gray, and potentially highly externalized clusters in white.

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.MTBP-0008-2016
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FIGURE 4

Each cluster was assigned a COG annotation. RNA processing and modification; chromatin structure and dynamics; energy production and conversion; cell cycle control and mitosis; amino acid metabolism and transport; nucleotide metabolism and transport; carbohydrate metabolism and transport; coenzyme metabolism; lipid metabolism; translation; transcription; replication and repair; cell wall/membrane/envelope biogenesis; cell motility; posttranslational modification, protein turnover, chaperone functions; inorganic ion transport and metabolism; secondary structure; general functional prediction only; function unknown; signal transduction; intracellular trafficking and secretion; Defense mechanisms; Extracellular structures; cytoskeleton. Functional distributions were plotted for the three classes of clusters; resident clusters are in black, potentially externalized clusters in gray, and potentially highly externalized clusters in white. For each class of clusters, significantly enriched functional categories ( < 0.01; hypergeometric test, after adjusting for multiple testing) are identified by # (resident), + (potentially externalized), and * (potentially highly externalized).

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.MTBP-0008-2016
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FIGURE 5

Resident, potentially externalized, and highly externalized clusters are colored in black, gray, and white, respectively. The three distributions are not significantly different (Mann-Whitney-Wilcoxon test, < 0.01). ratios of >2 were pooled to simplify the display.

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.MTBP-0008-2016
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FIGURE 6

(Left) Distribution of clusters across microbial host genera; (right) distribution of clusters across microbial host phyla. Clusters are color-coded as above. Potentially externalized clusters have a significantly broader host range than resident clusters, and potentially highly externalized clusters have a significantly broader host range than resident clusters and potentially externalized clusters (Mann-Whitney-Wilcoxon test, < 0.01). Taxonomic diversities of >4 were pooled to simplify the display.

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.MTBP-0008-2016
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FIGURE 7

Clusters are color-coded as above. Potentially externalized clusters have a significantly broader host range than resident clusters, and potentially highly externalized clusters have a significantly broader host range than resident clusters and potentially externalized clusters (Mann-Whitney-Wilcoxon test, < 0.01).

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.MTBP-0008-2016
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FIGURE 8

Combinations of MGEs and of their direct hosts, e.g., the gut microbial cells, as well as of the human body, itself hosting gut microbes, form an integrated, multilevel, multipartite, dynamic biological system, also referred to as a holobiont. For each individual human, the black square represents a close-up of its gut microbiota (with gut microbial cells in brown and MGEs in purple) and of its gut microbiome (the genes contained within these microbial cells and MGEs). Genes are represented by colored rectangles; genes with the same color belong to the same gene family. The process of HGT, mediated by MGEs, is responsible for mobilizing genes between microbial cells. We demonstrated that gene families carried by a larger diversity of MGEs are more widely shared not only between gut microbes but also between individual human hosts, as shown for the red gene family. Thus, HGT is a key process for introducing genetic similarity at multiple consecutive host levels within and between holobionts.

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.MTBP-0008-2016
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FIGURE 9

In this tripartite graph, type I nodes are the hosts (lizards) and are colored depending on their diet. Middle nodes are microbial classes, and type II nodes are gene families. Type II nodes colored in red are gene families shared by insectivorous lizards only. Type II nodes colored in blue are gene families shared by omnivorous lizards only, and then those colored in purple are shared by insectivorous and omnivorous lizards. The tripartite graph allows division of gene families specific to a diet in two categories: gene families that are shared by microbial classes specific from a diet (group 1: gene families 1 and 2, encircled in red) and gene families that are shared by microbial classes nonspecific from a diet (group 2: gene families 3 and 4, encircled in red). In the first group, gene families are not necessarily involved in the diet of the lizards; they are in insectivorous lizards because all the microbial classes that contain them are specific from insectivorous lizards. In the second group, gene families are more likely involved in the diet because they are present in microbial classes that are not exclusive to insectivory. These two groups correspond to two different ways of transmission: the first group is a microbial transmission, whereas the second group is a gene transmission.

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.MTBP-0008-2016
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