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Chapter 4.1.1 : Phylogenomic Networks of Microbial Genome Evolution

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Abstract:

Phylogenomics is aimed at studying functional and evolutionary aspects of genome biology using phylogenetic analysis of whole genomes. Current approaches to genome phylogenies are commonly founded in terms of phylogenetic trees. However, several evolutionary processes are non tree-like in nature, including recombination and lateral gene transfer (LGT). Phylogenomic networks are a special type of phylogenetic networks reconstructed from fully sequenced genomes. The network model, comprising genomes connected by pairwise evolutionary relations, enables the reconstruction of both vertical and gene transfer events. Reconstructing microbial genome evolution in the form of a network enables the use of an extensive toolbox developed for network research. The structural properties of phylogenomic networks open up fundamentally new insights into gene and genome evolution.

Citation: Dagan T, Popa O, Klösges T, Landan G. 2016. Phylogenomic Networks of Microbial Genome Evolution, p 4.1.1-1-4.1.1-18. In Yates M, Nakatsu C, Miller R, Pillai S (ed), Manual of Environmental Microbiology, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818821.ch4.1.1
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Image of FIGURE 1
FIGURE 1

Lateral gene transfer mechanisms mechanisms. (a) Transfer of raw DNA during transformation. (b) Plasmid transfer during conjugation. (c) DNA transfer by phages during transduction. (d) LGT by gene transfer agents (GTAs). (adapted from ) doi:10.1128/9781555818821.ch4.1.1.f1

Citation: Dagan T, Popa O, Klösges T, Landan G. 2016. Phylogenomic Networks of Microbial Genome Evolution, p 4.1.1-1-4.1.1-18. In Yates M, Nakatsu C, Miller R, Pillai S (ed), Manual of Environmental Microbiology, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818821.ch4.1.1
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Image of FIGURE 2
FIGURE 2

Intercellular DNA transfer. (a) Intercellular bridges connecting cells (adapted from 45). (b) Nanotubes connecting (PY79) and (MG1655) cells (adapted from ). doi:10.1128/9781555818821.ch4.1.1.f2

Citation: Dagan T, Popa O, Klösges T, Landan G. 2016. Phylogenomic Networks of Microbial Genome Evolution, p 4.1.1-1-4.1.1-18. In Yates M, Nakatsu C, Miller R, Pillai S (ed), Manual of Environmental Microbiology, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818821.ch4.1.1
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FIGURE 3

Outer membrane vesicles. (a) A cell in the process of releasing OMVs by budding (adapted from ). (b) an OMV released by the archaebacterium . doi:10.1128/9781555818821.ch4.1.1.f3

Citation: Dagan T, Popa O, Klösges T, Landan G. 2016. Phylogenomic Networks of Microbial Genome Evolution, p 4.1.1-1-4.1.1-18. In Yates M, Nakatsu C, Miller R, Pillai S (ed), Manual of Environmental Microbiology, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818821.ch4.1.1
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FIGURE 4

An introduction to networks. (a) A network composed of vertices (circles) and edges (lines). (i) An unweighted network of n vertices can be fully defined by a matrix, A = []n*n, with = 1 if an edge is connecting between vertex and vertex , and = 0 otherwise. Vertex centrality () is calculated as the sum of vertices linked to the vertex. (ii) A weighted matrix representation of the network. Cells of connected vertices and contain the edge weight linking the vertices. Vertex connectivity () is the sum of edge weights of the edges connected to the vertex. (b) A directed network comprising vertices and directed edges. (i) In the matrix representation of an unweighted directed network of n vertices, = 1 if a directed edge is pointing from vertex to vertex , and = 1 if a directed edge is pointing from vertex to vertex Vertex IN degree is the sum of vertices connected to the vertex. Vertex OUT degree is the number of vertices to which the vertex is connected. (ii) A matrix representation of a weighted directed network. Cells of edges directed from vertex to vertex contain the edge weight. Vertex IN degree is the sum of edges connected to the vertex. Vertex OUT degree is the sum of edges connecting the vertex to other vertices. (c) Network modularity describes the vertices’ clustering behavior, and vertex degree measures the vertex connectivity within the network. Vertices those are more densely connected to each other than with the rest of the network may be clustered into modules (Module A and B, gray and green clouds). The degree centrality is presented near the vertices ( = 2, = 3, etc.). Vertex color corresponds to the betweenness centrality that is calculated by the frequency of the vertex occurrence along shortest paths between nodes in the network. Vertices having higher betweenness centrality are usually connected to two or more distinct modules. (Adapted with modification from .) doi:10.1128/9781555818821.ch4.1.1.f4

Citation: Dagan T, Popa O, Klösges T, Landan G. 2016. Phylogenomic Networks of Microbial Genome Evolution, p 4.1.1-1-4.1.1-18. In Yates M, Nakatsu C, Miller R, Pillai S (ed), Manual of Environmental Microbiology, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818821.ch4.1.1
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FIGURE 5

Splits networks. (a) An illustration of the simplest splits graph that is not a tree. The graph “contains” the splits in trees T1 and T2 but not T3 (adapted from 101). (b) A splits network Escherichia genomes reconstructed from a concatenated alignment of 1,910 universal single copy (USC) genes. The zoom on the left highlights the two major splits connecting with the pathogenic and C-I group (red) or and clade (green) (adapted with modification from ). doi:10.1128/9781555818821.ch4.1.1.f5

Citation: Dagan T, Popa O, Klösges T, Landan G. 2016. Phylogenomic Networks of Microbial Genome Evolution, p 4.1.1-1-4.1.1-18. In Yates M, Nakatsu C, Miller R, Pillai S (ed), Manual of Environmental Microbiology, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818821.ch4.1.1
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FIGURE 6

Phylogenomic networks reconstructed from gammaproteobacterial genomes. (a) A matrix representation of a phylogenomic shared genes network. Protein families were reconstructed under the constraint of 30% (left) and 70% (right) amino acid identities. The species are sorted by an alphabetical order of the order and genus. The color scale of cell in the matrix indicates the number of shared protein families between genomes and The matrix representation of the phylogenomic shared genes network reconstructed from gammaproteobacterial genomes clearly shows groups of highly connected species having many genes in common. These groups usually comprise closely related species. Examples are 14 species (Alteromonadales order) at the top left corner, and 6 species (Xanthomonadales order) at the bottom right corner of the matrix, intraconnected species corresponding to (top to bottom) 12 species, 7 species, 6 species, and 12 species, which have many genes in common. Applying a higher protein similarity cutoff (right) yields a shared genes network of conserved genes only. The network shows a clear phylogenetic signal with most genes shared among closely related species. (b) A phylogenomic network of laterally shared genes reconstructed by the minimal lateral network (MLN) approach. Vertical edges (tree branches) are indicated in gray, with both the width and the shading of the edge shown proportional to the number of inferred vertically inherited genes along the edge (see scale on the left). The lateral network is indicated by edges that do not map onto the vertical component, with the number of genes per edge indicated in color (see scale on the right). Edges of weight <10 are excluded (adapted from ). (c) A dLGT network. The nodes correspond to contemporary or ancestral species that are connected by directed edges of LGT. The edges point from the LGT donor to the recipient. Node color corresponds to species taxonomic classification (see legend at the bottom). A cluster of connected bacilli (marked with a star) is enlarged to exemplify the network underlying data. Species names are shown next to the nodes. Gene identifier and protein annotation of detected recent gene transfers are noted next to the corresponding edge. The lateral acquisition of genes for sucrose utilization in from has been suggested before ( ). (strain PSU-1)—which is associated with malolactic fermentation in wine—is connected with three different species as donor and recipient. Bon et al. ( ) showed recently that gene acquisition from various donors, especially lactic acid bacilli, contributes to genome plasticity in this species and suggested LGT as a mechanism to enhance tolerance for harsh wine conditions. doi:10.1128/9781555818821.ch4.1.1.f6

Citation: Dagan T, Popa O, Klösges T, Landan G. 2016. Phylogenomic Networks of Microbial Genome Evolution, p 4.1.1-1-4.1.1-18. In Yates M, Nakatsu C, Miller R, Pillai S (ed), Manual of Environmental Microbiology, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818821.ch4.1.1
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FIGURE 7

Network reconstruction workflow. Bioinformatic pipelines for the construction of phylogenomic networks from genomic data. Alternative combinations of the steps in the analysis yield different phylogenomic networks. For example, pipelines 1 and 2 can be used to construct phylogenomic networks from self-made protein families. The two pipelines differ in the species reference tree used for the LGT inference that is reconstructed wither from USC genes (pipeline 1) or 16S rRNA genes (pipeline 2). Pipelines 3 and 4 demonstrate the use of ready-made clusters. Pipelines 4, 5 result in a dLGT using a USC or 16S species reference tree. (1) Genomic database preparation. Collect microbial genome/proteome sequence data for the strains of interest. These could be either self-sequenced or publicly available genomes (e.g., NCBI RefSeq). Draft genome sequences including annotated contigs could be used as well although these should be used with caution as missing proteins due to misassembly and/or misannotation can bias the results. (2) Construction of orthologous protein families. Phylogenomic networks are reconstructed within the protein family framework. Clusters of orthologous genes are reconstructed from an all-versus-all genome comparison by sequence similarity. In the following description, the term genome is related to a multifasta format containing all protein sequences encoded within the genome of a specific strain (i.e., sequenced from a pure culture). (2.1) Genomic comparison. Orthologous genes are identified by the reciprocal best BLAST hit procedure (rBBH) ( ). There are several approaches to perform this stage; here we describe two of those. The first approach includes a pairwise BLAST of all genomes. This includes the construction of a BLAST database for each genome using and running the blast command for all genome pairs where one genome constitutes the query and the other genome serves as the database, and the other way around. In the second approach, all genomes are grouped to construct a single BLAST search database. The total proteins are then BLASTed on that single database either one by one or genome by genome. (2.2) Inference of orthologous genes. Orthologous sequences are defined as pairs of proteins encoded in two different genomes that reciprocally found each other as the best BLAST hit (BBH). In the first BLAST approach this is a straightforward application. In the second approach one has to make sure that the BBH is indeed the best hit from among all hits of the sequence within the same genome. Pairs of putative orthologs have to be sorted by the significance of their rBBH result. Using the commonly used threshold only those BLAST results having an -value <1E-10 are included. In principle, the clustering into protein families (step 2.3) can be performed using the -value or the percentage (%) sequence identity as extracted directly from the reciprocal BBH procedure. However, to remove biases in the analysis as a result of local sequence similarity it is advisable to sort out putative orthologs whose global sequence similarity is low. For this purpose, sequence pairs are aligned with a global alignment algorithm (e.g., that is included in the EMBOSS package; ) and the proportion of identical amino acids is calculated (e.g., using a PERL script). The commonly used threshold for orthologous proteins is global identical amino acids ≥30% (e.g., 15). (2.3) Clusters of orthologous protein families. The resulting pairs of orthologous genes are summarized into a protein similarity matrix that is used for the clustering into protein families. The basic similarity matrix is defined as = []*, where as the total number of genes in the genomic database, and is calculated as the sequence similarity between rBBH gene pairs as measured either from a global pairwise sequence alignment or the BLAST results (see step 2.2). Matrix cells of gene pairs that were not found as rBBH are set to 0. The protein similarity matrix is the input for the clustering software. The Markov clustering algorithm—MCL ( )—is commonly used in such applications (e.g., , Dagan:2007ju, Halary:2010bq). A typical output of a clustering algorithm will include an arbitrary protein family number and the list of member protein sequences. Singletons will appear in the output as single-member clusters. Multiple paralogs are often clustered into the same protein family due to the limitation of sequence similarity information for the distinction between orthologs and paralogs. Hence a protein family may include more than one representative gene for certain genomes (i.e., strains). (2.4) Ready-made protein families. As an alternative to the full construction of protein families, one could also use any of the ready-for-use clusters in a number of public databases (e.g., OMA ( ) or IMG ). (3) Phylogenomic network of shared genes. Clusters of orthologous genes supply the required information for the construction of a shared genes network. The network is defined by a matrix = []*, where is the number of genomes in the data set, and is calculated as the number of common proteins families to genomes and . A visual presentation of the matrix is enabled by various tools, for example using function in MATLAB, or function in . (4) Reference species tree. The reference tree can be reconstructed from a single marker gene (e.g., 16S rRNA) or a group of genes. (4.1) Species tree from 16S rRNA sequences. Sequences of the 16S rRNA should be extracted from the genome (annotated genomes in NCBI contain a specific file for rRNA genes only) and stored in a multifasta file. For closely related species having similar 16S rRNA it is advisable to repeat this step for the 5S and 23S genes. Their inclusion in the phylogenetic tree will help to improve the splits resolution. (4.2) Species tree from universal single copy (USC) genes. A second approach for the reconstruction of a species tree is to use the phylogenetic information in all USC genes. These are protein families (from step 2.3) that are encoded in all genomes in the dataset as a single copy. The inclusion of single copy genes is aimed to reduce the risk for orthology misspecification. Furthermore, single copy genes are less attributed to LGT (16) that may bias the vertical phylogenetic signal. Each of the protein families has to be formatted into a multifasta file containing all protein sequences for the genome. (4.3) Multiple sequence alignment. Progressive alignment algorithms can be applied using ClustalO ( ) or MUSCLE ( ). Another possibility is to use the fast Fourier transformation alignment approach using MAFFT ( ), which has been shown to produce somewhat better alignments in comparison to other approaches ( ). When multiple genes are used for the tree reconstruction, these should be aligned first and then concatenated into a single alignment (e.g., a concatenated alignment of all rRNA genes or all USC protein families). It is advisable to test for multiple sequence alignment quality of each protein family using, for example, the GUIDANCE tool ( ). (4.4) Species phylogenetic tree. The species phylogenetic tree is reconstructed from a marker gene alignment (e.g., 16S rRNA) or a concatenated alignment (e.g., rRNA genes or USC genes). The reconstruction can be performed using the neighbor-joining approach (NJ) ( ) applying the PHYLIP package ( ) programs or and then . Alternatively, the maximum-likelihood approach may be applied using, for example, PHYML ( ). It is advisable to calculate bootstrap support in addition. When USC genes are used for the reconstruction, it is worthwhile to calculate in addition a consensus tree that will assist in the detection of weak phylogenetic signal due to recombination and LGT (e.g., ). For this purpose one has to reconstruct all gene trees and find the consensus topology using, for example, the PHYLIP program . (5) Species SplitsTree. The collection of USC genes can be used to reconstruct a species SplitsTree that is helpful for the study of recombination events ( Fig. 6 ). The SplitsTree can be reconstructed either from the USC genes concatenated alignment or from the total USC gene trees using SplitsTree software ( ). (6) Minimal lateral network. The MLN is reconstructed from the clusters of orthologous genes (step 3.3) and the reference species tree (step 4). The MLN approach is based on three assumptions (15): (i) All gene trees are perfectly compatible with the same reference tree. (ii) Gene loss is unpenalized. (iii) All within-genome duplications for each gene family are assumed to have occurred subsequent to the last divergence for each lineage. For the MLN reconstruction, the presence and absence patterns (PAPs) of protein families are superimposed on the reference tree to infer laterally shared genes. The PAPs can be defined as a matrix = []*, where is the number of genomes in the data set, is the number of protein families, and is set to 1 if genome encodes for at least one copy of protein family or 0 otherwise. Gene gain and loss events are inferred using a parsimonious algorithm (15) and the MLN is reconstructed by connecting the gain events within protein families having more than a single gene origin ( ). ( ) Phylogenomic LGT network. The construction of a phylogenomic LGT network from trees requires the reconstruction of gene trees for all protein families using one of the multiple sequence alignment algorithm (see step 4.3) and phylogenetic tree reconstruction approach (see step 4.4). Protein families that are represented in <4 genomes may be excluded from the analysis. All resulting trees are compared to the species reference tree and branches (i.e., splits) that are found in significant disagreement with the reference tree are considered as LGT events (see for details). The network can be presented by summarizing the discordant splits (i.e., LGT events) between the main taxonomic groups ( ), or similarly to the MLN where discordant splits are superimposed on the reference tree. The interpretation of discordant branches of paralogs may be problematic when using this approach. Multiple gene copies can be pruned out according to their rBBH results (see ), which results in a considerable loss of data. Alternatively, the network can be reconstructed from only single copy families only. (8) Inference of directed LGT (dLGT) events. The reconstruction of a dLGT network includes the identification of the recipient and donor in the gene transfer event ( ). (8.1) Recent LGT event. The detection of recently acquired genes is performed by comparing the percent of G and C nucleotides (%GC) in all genes to the distribution of the gene-%GC for the total genome (see ( ) for details). Those genes whose %GC is significantly different from that of the genome are considered as putative laterally transferred genes. (8.2) Donor inference. The identification of donors in the LGT event includes the reconstruction of a phylogenetic tree for each detected gene acquisition and a comparison of the gene tree to the reference tree. The main stages in the tree reconstruction include the identification of orthologs using rBBH procedure (step 2.1), a multiple sequence alignment of the putative acquired gene with the orthologs using ClustalW ( ), and a phylogenetic tree reconstruction using PHYML ( ). Putative donors are defined as genes (i.e., species) that branch with the acquired gene in the gene-tree but not in the reference species tree. Putative acquisitions of closely related orthologs (as defined by the species reference tree) are grouped into a single acquisition event that occurred at the ancestral strain, hence internal nodes in the reference tree (i.e., hypothetical taxonomic units) may also result as putative donors and recipients (see for details). (8.3) Directed LGT network. The directed network of LGT is defined as an asymmetric matrix = []*, where is the number of nodes in the reference tree (both internal and external) and is the number of detected laterally transferred genes from tree node (the donor) to tree node (the recipient). The network can be presented similarly to the MLN where detected LGT events are superimposed on the reference tree or by using Cytoscape ( ). doi:10.1128/9781555818821.ch4.1.1.f7

Citation: Dagan T, Popa O, Klösges T, Landan G. 2016. Phylogenomic Networks of Microbial Genome Evolution, p 4.1.1-1-4.1.1-18. In Yates M, Nakatsu C, Miller R, Pillai S (ed), Manual of Environmental Microbiology, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818821.ch4.1.1
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