Large-Scale Analysis of Plasmid Relationships through Gene-Sharing Networks

and Evolution. All rights reserved. For permissions, please e-mail: [email protected] The Author 2011. Published by Oxford University Press on behalf of the Society for Molecular Biology

Large scale analysis of plasmids relationships through gene sharing networks 1

Research article 2

Manu Tamminen1, Marko Virta1, Renato Fani2, Marco Fondi2* 3 4

1Department of Food and Environmental Sciences, P.O.Box 56, 00014 University of Helsinki, 5

Finland 6

7 2Lab. of Microbial and Molecular Evolution, Dept. of Evolutionary Biology, Via Romana 17-8

19, University of Florence, Italy 9

10

* Correspoding author: 11

Marco Fondi 12

Lab. of Microbial and Molecular Evolution, Dept. of Evolutionary Biology 13

Via Romana 17-19, University of Florence 14

Italy 15

E-mail [email protected] 16

Tel. +39 0552288248 17

Fax. +39 055 2288250 18

19

Running head: Analysis of plasmids through gene sharing networks 20

Keywords: horizontal gene transfer, antibiotic resistance, plasmid, network 21

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iblioteca di Scienze, Universit? degli studi di Firenze on January 11, 2012

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Abstract 23

Plasmids are vessels of genetic exchange in microbial communities. They are known to 24

transfer between different host organisms and acquire diverse genetic elements from 25

chromosomes and/or other plasmids. Therefore, they constitute an important element in 26

microbial evolution by rapidly disseminating various genetic properties among different 27

communities. A paradigmatic example of this is the dissemination of antibiotic resistance 28

genes that has resulted in the emergence of multiresistant pathogenic bacterial strains. To 29

globally analyze the evolutionary dynamics of plasmids, we built a large graph in which 2343 30

plasmids (nodes) are connected according to the proteins shared by each other. The analysis 31

of this gene sharing network revealed an overall coherence between network clustering and 32

the phylogenetic classes of the corresponding micro-organisms, likely resulting from genetic 33

barriers to horizontal gene transfer between distant phylogenetic groups. Habitat was not a 34

crucial factor in clustering as plasmids from organisms inhabiting different environments 35

were often found embedded in the same cluster. Analyses of network metrics revealed a 36

statistically significant correlation between plasmid mobility and their centrality within the 37

network, providing support to the observation that mobile plasmids are particularly important 38

in spreading genes in microbial communities. Finally, our study reveals an extensive (and 39

previously undescribed) sharing of antibiotic resistance genes between Actinobacteria to 40

Gammaproteobacteria, suggesting that the former might represent an important reservoir of 41

antibiotic resistance genes for the latter. 42

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Introduction 43

Plasmids are paradigmatic examples of the network-like structure of microbial evolution 44

(Brilli et al. 2008). Indeed, they are among the most important players in the evolution of 45

prokaryotes because they can be transferred between microorganisms, thus representing 46

natural vectors for the transfer of genes and the functions they code for (Norman, Hansen, 47

Sorensen 2009). Accordingly, they often provide a basis for genomic rearrangements via 48

homologous recombination, facilitating the loss and/or acquisition of genes during these 49

events, which may eventually lead to horizontal gene transfer (HGT). As a consequence, 50

plasmids possess a mosaic structure with collections of functional genetic modules, each of 51

which likely possessing an independent phylogenetic history, organized into a stable and self-52

replicating entity (Osborn et al. 2000; Toussaint, Merlin 2002; Bosi, Fani, Fondi 2011). 53

Importantly, these functional blocks often embed genes that might have a great impact on the 54

metabolic functions of the host cell, providing additional traits that can be accumulated 55

without altering the gene content of the bacterial chromosome (Fondi et al. 2010). Plasmids 56

are actually involved in many accessorial functions and constitute, together with "not 57

essential" chromosomal regions, what is referred to as the "dispensable genome" in the 58

microbial pan-genome concept (Medini et al. 2005). This, in turn, can include genes for 59

ecologically important traits such as antibiotic resistance (Crosa, Luttropp, Falkow 1975), 60

pathogen virulence (Hacker, Kaper 2000), symbiotic nitrogen fixation (van Rhijn, 61

Vanderleyden 1995) and the production of allelopathic bacteriocins (Riley, Gordon 1999). 62

Among these processes, pathogenesis and antibiotic resistance are those that have been 63

primarily explored up to now. Indeed, it has been shown that the presence of plasmids can be 64

strictly linked to the emergence of a pathogenic lineages within a given taxonomic unit 65

(Reynaud et al. 2008; Le Roux et al. 2010). Parallely, in terms of antibiotic resistance, 66

plasmids serve a central role as the vehicles for resistance gene capture and their subsequent 67

spreading (Bennett 2008; Fondi, Fani 2010). Dissemination of these features represents one of 68

the most important effects of ‘bacterial sex’, from both an evolutionary and ecological 69

viewpoint (Kohiyama et al. 2003). In this context, plasmid mobility represents an essential 70

parameter of microorganisms’ fitness and it might also be a key element to an understanding 71

of the epidemiology of these plasmid-carried traits (Smillie et al. 2010). However, despite 72

their clear biological relevance, the pathways followed by plasmids during their evolutionary 73

history remain almost obscure. 74




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Nowadays the use of massive plasmid sequencing as a routine laboratory technique (Schluter 75

et al. 2008), together with the development of bioinformatics tools enabling the visualization 76

of sequence homology relationships through similarity networks (Vlasblom et al. 2006; Brilli 77

et al. 2008), can greatly speed up studies of gene mobility among plasmids. Furthermore, 78

thanks to the expansion of network oriented representation of sequences similarity 79

relationships (Lima-Mendez, Toussaint, Leplae 2007; Brilli et al. 2008; Dagan, Martin 2009; 80

Dagan et al. 2010; Fondi et al. 2010; Fondi, Fani 2010; Halary et al. 2010), graph theory 81

measures have been applied to better describe the gene(s) flow across the diverse microbial 82

communities, paving the way to large scale comparative analyses adopting bioinformatics 83

strategies. In more detail, by adopting a gene sharing network approach, Dagan et al. (Dagan, 84

Artzy-Randrup, Martin 2008) reported the construction and the analysis of graphs capturing 85

both vertical and lateral components of evolutionary history among 539,723 genes distributed 86

across 181 sequenced prokaryotic genomes. The same authors estimated that an impressive 87

amount (almost 80% on average) of the gene content of each analyzed genome was involved 88

in lateral gene transfer at some point in evolution. More recently, Halary et al. (Halary et al. 89

2010) applied mathematical studies of the centralities of a network embedding 119,381 90

homologous DNA families. They demonstrated that plasmids, and not viruses, are likely the 91

key vectors of genetic exchange between bacterial chromosomes. Moreover, results also 92

supported a disconnected yet highly structured network of genetic diversity, revealing the 93

existence of multiple “genetic worlds”. From the analysis of the same network, the same 94

authors also inferred that DNA pools mostly circulate between vehicles (i.e. plasmids, phages 95

and chromosomes) of the same type. Finally, (Lima-Mendez et al. 2008) represented 96

relationships across the phage population as a weighted graph where nodes represented 97

phages and edges represented phage–phage similarities in terms of gene content. Their 98

approach succeeded in capturing the pervasive mosaicism of phage genomes, indicating the 99

importance of horizontal gene exchange in their evolution and also proving to be a promising 100

tool for predicting lifestyles of individual phages from sequence data. 101

By applying a computational, network-oriented pipeline we have analyzed the evolutionary 102

relationships among 2343 microbial plasmids in order to explore the role of each of them 103

within the reticulate evolutionary dynamics of this class of mobile genetic elements. 104

Moreover, we focused the attention on the proteins involved in two main biological processes, 105

that is antibiotic resistance and pathogenesis, as well as on plasmid features that might be 106

involved in ruling the overall network of plasmids-mediated HGT (e.g. plasmid mobility). 107




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Data obtained provide interesting clues in gaining a systemic interpretation of the overall 108

behaviour of plasmids within bacterial evolution and in the spreading of some key biological 109

features such as antibiotic resistance and virulence. 110

Methods 111

Datasets assembly 112

All the available complete plasmid sequences (in GenBank format) were downloaded from 113

NCBI using EFetch interface (as on July the 24th 2010). Totally, 2343 plasmids (102772 114

ORFs) were retrieved and a complete table including all their main features (their size, 115

taxonomy, accession codes etc.) is available as Supplemental Information S1. Moreover, two 116

different subsets of sequences were created starting from the whole plasmid sequences 117

dataset. On one side, we created a set of plasmid-encoded proteins that were involved in the 118

process of antibiotic resistance. This was done using each of the retrieved sequences as seed 119

in BLAST (Altschul et al. 1997) search against the Antibiotic Resistance DataBase (ARDB) 120

(Liu, Pop 2009) using the following parameters: e-value 1e-20, minimum alignment length 50 121

amino acid (aa), that is a degree of amino acid sequence identity sufficiently high to retrieve 122

all the proteins that should perform a function related to antibiotic resistance (Friedberg 2006; 123

Fondi, Fani 2010). In this way, a set of 2678 sequences putatively associated to antibiotic 124

resistance (AR) were retrieved (See Supplemental Information S2 for the complete list of 125

accession codes of the proteins used in this work). These sequences belonged to 501 different 126

plasmids. 127

The same strategy with the same parameters, was applied when searching for virulence 128

related proteins (virulence factors, VF) within the whole plasmid sequence dataset. In this 129

case the probed database was the Virulence Factor DataBase (VFDB) (Chen et al. 2005; Yang 130

et al. 2008) and a set of 7840 sequences was retrieved from this BLAST search (belonging to 131

615 plasmids). Again, all the information about these sequences is available as Supplemental 132

Information S3. 133

Network construction 134

The network construction workflow described in this paragraph has been applied to each of 135

the three assembled datasets, i.e. the one embedding all retrieved plasmids sequences 136

(hereinafter referred to as “all sequences network”), the one embedding the antibiotic 137

resistance-related sequences (the “resistance network”) and the one embedding virulence 138

factor-related sequeces (the “virulence network”). 139




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In detail, each of the sequence dataset was used in an all against all BLAST probing 140

(Altschul et al. 1997) using the Murska parallel computing cluster (Center for Scientific 141

Computing, Espoo, Finland). The BLAST output was parsed to include matches from two 142

different identity thresholds (70% and 95%) by using ad hoc implemented Python scripts. 143

Two parsed files were obtained, one embedding those sequences sharing at least 70% 144

sequence identity and another one embedding sequences sharing at least 95% identity. 145

Similarly to (Dagan, Artzy-Randrup, Martin 2008) and, later, to (Halary et al. 2010), this 146

allows to interpret the resulting networks under a molecular clock–based assumption, i.e. 147

under the hypothesis that proteins with the highest percentages of identity were likely to be 148

more recently shared than the ones with less identity. In the present context, proteins with 149

95% identity were considered more recently shared than those with 70%. 150

Subsequently each of these parsed BLAST outputs was transformed into a gene sharing 151

network and visualized using Gephi visualization program (Bastian, Heymann, Jacomy 2009). 152

Accordingly, in this network, each node represents a single plasmid and two different 153

plasmids are linked on the basis of their shared protein content. In particular, sharing is 154

defined by a BLAST match between two reading frames longer than 300 bp and 95% or 70% 155

amino acid identity, respectively, therefore representing an absolute measure. To investigate 156

the dynamics of plasmids among bacterial cells, we applied a further filter to each of the 157

obtained graph, maintaining linked only those edges sharing at least five proteins and 158

discarded all the connections linking plasmids with a lower amount of shared proteins. 159

Similarly, to investigate the dynamics of individual genes or small gene clusters among the 160

plasmid population, we applied a filter to maintain only those edges that constitute sharing 161

less than 5 genes. Altogether, we obtained 8 different networks: (70% and 95% identity 162

values for all sequences with more or less than 5 gene transfers, and sequences related to AR 163

or VF). The Gephi-formatted network files are available as Supplemental Information S4. 164

Permutation tests 165

To evaluate the statistical significance of observed preferential gene flows (see below), we 166

randomly permuted 10000 times the phylogenetic affiliation of each node, while keeping 167

intact the original degree of each node within the network (randomization with node degree 168

conservation, see (Brohee et al. 2008)). A p-value was then obtained by counting the number 169

of times the randomly assembled networks returned a number of links greater (or lower) than 170

the observed one and dividing this number for the total amount of performed permutation 171

tests. 172




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Estimation of plasmid mobility 173

The presence of genes related to plasmid mobility were identified by BLAST analysis (with 174

the following parameters: e-value 1e-20, minimum alignment length 50 aa) of the plasmid-175

encoded amino acid sequences against a tra and mob gene dataset retrieved from ACLAME 176

database (http://aclame.ulb.ac.be/, (Leplae et al. 2004)). Since tra and mob genes are 177

generally associated with plasmid mobility and conjugation, we defined plasmid as mobile if 178

it contained one or more mob or tra genes [a similar approach was recently adopted by 179

(Smillie et al. 2010)]. 180

Network centralities, statistics and visualization 181

Network centrality values for network nodes were calculated using iGraph package in R 182

(Csardi 2006). Network clustering was estimated using the Louvain algorithm implemented in 183

Gephi (Blondel et al. 2008) by maximizing modularity and minimizing number of clusters. 184

All statistical tests to investigate the differences in degree and betweenness distributions and 185

GC% content were performed using the base statistics tools in R (R Development Core Team 186

2010; http://www.r-project.org/). Data plotting was performed using ggplot2 package of R 187

(Wickham 2009). All other statistical analyses were performed using in-house developed Perl 188

and Python scripts. Visualization of network clustering and gene sharing as an ideogram was 189

performed using Circos (Krzywinski et al. 2009). 190

Estimation of the phylogenetic distances of gene sharing 191

The 16S rRNA sequences for plasmid hosts were downloaded from Ribosomal Database 192

project (Cole et al. 2007; Cole et al. 2009). The 16S rRNA sequences were aligned using the 193

NAST aligner provided by Greengenes (DeSantis et al. 2006). The distance matrix of the 194

phylogenetic distances was calculated using Phylip (Felsestein 1989) 195

196

Estimation of phylogenetic coherence in major network clusters 197

The Conclustador algorithm (Leigh et al. 2011) was applied to analyze the congruence of 198

phylogenetic trees reconstructed from the sequences of the genes shared by plasmids 199

belonging to the same cluster in a network. Gene families responsible for the connections 200

among the different plasmids were extracted from the 70% and 95% networks and aligned 201

using Muscle software (Edgar 2004). Then, for each plasmid cluster, resulting multiple 202

sequence alignments were used as input for phylogenetic coherence analysis, adopting 203




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Conclustador (Leigh et al. 2011) algorithm. Finally, SplitsTree4 (Huson, Bryant 2006) was 204

used to visualise the phylogenetic information both in each single group identified by 205

Conclustador and in all the groups all at once (and, together, responsible for the plasmids 206

interconnections shown in the networks of Figure 1). In both cases, supernetworks were 207

inferred using data available from single gene phylogenetic analyses performed with RAxML 208

tool with 1000 bootstrap replications. 209

Since for Conclustador to work properly analyzed datasets should not be too fragmented, i.e. 210

about the 80% of the overall taxa dataset must be present in each multiple alignment, not all 211

the identified plasmids clusters could be reliably analyzed. Accordingly, only the major 212

clusters in the 70% and 95% networks were analyzed (namely clusters 961, 993, 1144 and 213

1238 for 70% network and 961, 993 and 1144 for 95% network). Interestingly, the 214

widespread fragmentation found for most of the clusters in the dataset might be due to a high 215

heterogeneity of the same clusters that, in turn, might mirror a high level of horizontal transfer 216

of their embedded genes. 217

218

Results and discussion 219

Gene sharing networks 220

Gene sharing between plasmids was visualized as a network where the plasmids are 221

represented as vertices (or nodes) and gene sharing as edges (or links). Altogether 8 networks 222

were constructed based on 70% and 95% identity between the amino acid sequences and 223

different edge criteria, such as the amount of genes shared (more than or less than 5), or 224

sharing antibiotic resistance or virulence genes (Supplemental Information S6). The identity-225

based criterion introduced for links setting allows interpreting the resulting networks under a 226

molecular clock–based assumption, i.e. under the hypothesis that sequences with the highest 227

percentages of identity (e.g. 95%) were likely to be more recently exchanged than the ones 228

with less identity (e.g. 70%) [see for example (Halary et al. 2010)]. Data for the networks 229

accounting for the sharing of 5 or more genes are reported in Figure 1a and b. Overall, the 230

plasmid network of all sequences at 70% identity (Figure 1b) threshold exhibits one major 231

connected component, some minor connected components and a large number of 232

disconnected plasmids (see below). The main connected component of the network of all 233

genes (the central one in Figure 1b) embeds plasmids mainly belonging to the Proteobacteria 234

phylum (particularly from Gamma, Alpha, and Beta subdivisions). Interestingly, this 235

component also contains plasmids from Actinobacteria. A similar trend is observed in the 236




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case of 95% identity threshold network (Figure 1a) although, as it might be expected, in this 237

case the main connected component of the network is smaller. The only phylogenetically 238

uniform major component is represented by plasmids from Borrelia burgdorferi 239

(Spirochaetes, yellow nodes of Figure 1a and b). 240

241

In order to investigate the relationships between the taxonomy of represented microorganisms 242

and the evolutionary interconnections of their plasmids, we performed network clustering 243

using the Louvain algorithm implemented in Gephi [see Methods, (Blondel et al. 2008)] and 244

compared the obtained plasmids groups with the phylogenetic and habitat affiliations of their 245

constituent cells. The network clusters embedding multiple phyla and/or habitats for the 70% 246

and 95% networks of all sequencesare presented in Figure 2. According to the network 247

clustering analysis, the network clusters more typically embed members from different 248

habitats than from different phylogenetic orders. Hence, it appears that phylogenetic distance 249

is a greater barrier to gene sharing than having a different habitat. This is likely due to limited 250

horizontal gene transfer across phylogenetic classes that could result from, for example, 251

restriction or incompatible replication systems [as reviewed in (Thomas, Nielsen 2005)]. 252

Moreover, these observations are consistent with findings from microbial ecology and 253

previous in silico analyses (Baquero, Martinez, Canton 2008; Fondi, Fani 2010) and suggest 254

that there is a (more or less) high degree of mixing of microbes between unrelated 255

environments. 256

257

Gene sharing across phylogenetic classes implies at least one past HGT event, and is therefore 258

simple to detect. However, HGT could also be commonplace within phylogenetic classes. To 259

investigate this, all the major network clusters (including those reported in Figure 2) were 260

analyzed using Conclustador package to infer phylogenetically congruent and incongruent 261

gene families. Overall, obtained data (provided as Supplemental Information S5) revealed a 262

high level of incongruence among the analyzed clusters. Indeed Conclustador identified 8, 4, 263

2 and 3 different groups within 961, 993, 1144 and 1238 major plasmids clusters, 264

respectively. Similarly, in the 95% network 6, 4 and 2 distinct phylogenetic groups for 961, 265

993 and 1144 were retrieved. The construction of phylogenetic networks of the sequences 266

embedded in the groups identified by Conclustador revealed, in most cases, high levels of 267

inter- species reticulation. Overall, these data suggest the presence of potential abundant HGT 268

at lower taxonomical levels than those reported in Figure 1 and 2. 269

270




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Furthermore, in order to shed some light on the putative functions encoded by the shared 271

genes, we performed a COG-based functional annotation of the sequences embedded in each 272

plasmid cluster. Data obtained (also reported in Supplemental material S5) revealed that most 273

of the sequences responsible of the plasmids interconnections encode for proteins involved in 274

DNA transposition and recombination. This is not surprising since these functions are 275

strongly linked to the process of HGT and, consequently, to plasmids. Nevertheless, as shown 276

in Supplemental material S5, other genes are shared among the different plasmids embedded 277

in the same cluster and, importantly, their encoded functions are not directly related to the 278

process of HGT itself. This suggests that other functions, probably related to more complex 279

phenotypes, are shared by the different plasmids, including for example genes involved in 280

transcription, inorganic ion transport and metabolism and cell motility (the three most 281

abundant functional categories of plasmids cluster 961, see Supplemental material S5). 282

283

To study the sharing of resistance and virulence genes, the same procedure of network 284

construction was applied to the antibiotic resistance and virulence factor sequence datasets. 285

Results of these analyses for networks of 70% identity criterion are shown in Supplemental 286

material S6. Overall, the topology of both networks appeared to be similar to 70% and 95% 287

networks of all sequences, although some differences can be identified. Indeed, concerning 288

the antibiotic resistance network, the Proteobacterial plasmids do not form a single 289

component, but two different major components can now be identified, one embedding 290

Gammaproteobacterial and Actinobacterial plasmids and the other one embedding Beta and 291

Alphaproteobacterial sequences. This suggests that plasmids belonging to these taxonomic 292

units are not preferential transfer partners of antibiotic resistance genes for 293

Gammaproteobacteria representatives. Conversely, in the virulence network, Proteobacterial 294

plasmids form the major connected component of the graph (Supplemental material S6), 295

revealing an intense sharing of virulence-related genes among microorganisms belonging to 296

this taxonomic unit. Although some remarkable exceptions of plasmids acting as bridges in 297

connecting otherwise separate groups do exist (see below), the other clusters of virulence 298

network are overall coherent with the phylogenetic class affiliation (although intense gene 299

sharing might be present within these groups of plasmids, as shown by previous phylogenetic 300

coherence analysis). 301

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Network features and taxonomy 303




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In order to globally analyze the evolutionary relationships underlying the plasmid 304

populations, we applied graph theory measures to the gene sharing networks. In particular, the 305

networks were analyzed for node degree and betweenness. Degree is defined as the number of 306

connections a node has to other nodes. In the present context, a plasmid with a high degree is 307

a plasmid that shares a large number of genes with other plasmids. Betweenness is a centrality 308

measure that is defined as the frequency of a node to lie on the shortest path between two 309

other network nodes. In this context a plasmid with a high betweenness can transfer genes to 310

many other plasmid in the network with a low number of gene transfer events and, in other 311

words, can function as a bridge between otherwise disconnected regions of the network. 312

Accordingly, we computed centrality measures along the network, for all the classes of 313

prokaryotes present in the dataset. Results are provided in Figure 3, whose analysis revealed a 314

positive correlation between degree and betweenness that has also been observed by Halary et 315

al. 2010. However, in the network some nodes showed a much higher betweenness than most 316

nodes of the same degree (see below). Such outliers, characterized by a low degree but a high 317

betweenness, are especially important in any given network, as they can be seen as bridges 318

between smaller, more connected parts of the network (Halary et al. 2010). 319

Tables 1 and 2 report the highest degree and betweenness values, respectively, for individual 320

plasmids in the 70% and 95% identity networks of all sequences. The analysis of Table 1 321

reveals that all the plasmids possessing the highest values of degree belong to the Gamma 322

subdivision of Proteobacteria. This result can be easily explained by the oversampling of 323

plasmids from this class of bacteria. Indeed, the plasmids data used in this study is 324

unsystematically gathered from several unrelated sources and is highly biased toward human 325

pathogenic organisms (most of Gammaproteobacteria) (Wu et al. 2009). In this context, it is 326

likely that more detailed studies of individual environments would reveal several gene sharing 327

events between various phylogenetic groups that are not represented in the current data set. 328

Nevertheless, a detailed inspection of high-degree plasmids gave further support to previous 329

observations based on single plasmids sequence data. In fact, for example, plasmid pU302L 330

(see Table 1) from Salmonella enterica subsp. enterica serovar Typhimurium has already been 331

described for possessing a mosaic pattern of sequence homology with other plasmids (Chen et 332

al. 2007), suggesting, in turn, that this plasmid acquired resistance genes from a variety of 333

enteric bacteria (Chen et al. 2007). Notably, the fact that this plasmid is the best degree 334

scoring plasmid in the 95% network indicates that it acquired foreign genetic material from 335

very closely related microorganisms and/or very recently in time. Similarly, most of the other 336




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plasmids embedded in Table 1 possess a well documented history of HGT events [see for 337

example p1658/97 (Zienkiewicz et al. 2007; Yi et al. 2010) and pKF3-140 (Yi et al. 2010)]. 338

High betweenness nodes (plasmids) span over a larger taxonomic spectrum, suggesting that 339

this centrality measure is less affected by sampling biases. Indeed the plasmids with highest 340

betweenness values belong to diverse phylogenetic classes, including Bacilli, Lactobacilli and 341

Gamma, Beta, and Alphaproteobacterial representatives. As in the case of high degree 342

plasmids, mosaic-like structure of high-betweenness plasmids has been described before, for 343

example, of pCoo from Escherichia coli (Froehlich et al. 2005) and pGO1 from 344

Staphylococcus aureus (Caryl, O'Neill 2009). Hence, although the overall plasmids clustering 345

seems to agree with taxonomic classification of their source microorganisms, some plasmids 346

compact the overall network, residing in the path between plasmids that otherwise would 347

remain disconnected (Halary et al. 2010). Importantly, some of the plasmids that were found 348

to possess high degree/betweenness values (Table 1 and 2) were the same that were found to 349

be central in other gene sharing network analyses performed by Halary et al. (2010) (namely, 350

plasmids pOU7519, pU302L from Salmonella representatives, p1658/97, pIP1206 from 351

Escherichia coli, pKPN5 from Klebsiella pneumoniae, pVEF3 from Enterobacter faecium, 352

pSK41 from Staphylococcus aureus, pGdh442 from Lactococcus lactis and pTEF1 from 353

Enterococcus faecalis V583) thus confirming the key role of these DNA molecules in the 354

flow of genetic material among different microorganisms. In our opinion these plasmids 355

represent key players from an evolutionary viewpoint, contributing to the spreading of 356

potentially clinically relevant genetic determinants within the whole bacterial mobilome. 357

Several plasmids (1159 for the 70% identity network of all genes and 1369 for the 95% 358

identity network) in the data set shared less than five genes with any other plasmid and 359

therefore did not belong to any connected component. The taxonomic composition of this 360

disconnected component of the network is presented in Figure 4. Statistical randomization 361

testing (as described in Methods) was performed to evaluate the effect of sampling bias in the 362

frequency distribution. Most of the phylogenetic classes possessed between 2% and 5% of 363

disconnected plasmids, the only exception being represented by Gammaproteobacteria 364

(almost 15% of disconnected plasmids). For most classes the amount of disconnected 365

plasmids was higher than expected by random shuffling of the networks. 366

Dynamics of genes in the plasmid population 367




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In the previous sections we mainly analyzed networks in which two plasmids were connected 368

if they shared (at least) five genes, thus surely underestimating the real amount of gene 369

transfer events among plasmids. To go into greater details and to analyze the possible 370

dynamics of gene transfer among plasmids we built gene sharing networks taking into 371

account the sharing of single genes (up to four genes) among two given plasmids. Such 372

networks were constructed adopting the same computational strategy used for >= 5 networks 373

(see Methods) and, together with singlets taxonomical distribution and cross-taxa 374

interconnections, are reported in Supplemental Material S7. Overall, < 5 networks embedded 375

almost the same number of links (11458 and 5136 for 70% and 95% identity thresholds, 376

respectively) compared to >5 networks (12444 and 6777 for 70% and 95% identity 377

thresholds, respectively), suggesting the presence of an extensive amount of single gene (or of 378

relatively small gene sets) exchange among the different plasmids. 379

Louvain clustering of < 5 networks, although producing a large fraction of taxonomically 380

highly coherent groups, resulted in slightly more heterogeneous plasmid clustering compared 381

to the clustering obtained from >= 5 networks (Figure 2b and c). This suggests that when 382

considering the transfer single genes or groups of small genes, taxonomical barriers can be 383

bypassed more frequently than in the movement of larger sets of genes. In agreement with the 384

previous congruency analysis, a deeper analysis of the phylogenetic coherence (adopting the 385

coherence analysis pipeline described in Methods) of the gene families within the major 386

network clusters revealed a high amount of incongruency (data not shown). Hence, according 387

to the overall body of data presented here, it appears that the sharing of relatively small gene 388

sets is more abundant and spans over a larger phylogenetic distance than transfers of larger 389

sets of genes, although the great part of this genetic exchange still happens within the 390

boundaries of microbial phylogenetic classes. 391

392

Network comparison 393

To explore the differences among the networks, we computed Pearson product-moment 394

correlation coefficients between betweenness and degree values for each node (i.e. plasmid) 395

(Figure 5). Data obtained revealed a low positive correlation between betweenness and degree 396

in each of the networks, independently from the nucleic acid identity thresholds and/or the 397

functions shared among the different plasmids (virulence or antibiotic resistance genes). R2 398

values range between 0.25 and 0.36 for 70% networks and are slightly higher for 95% 399




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networks (ranging from 0.29 and 0.44). Accordingly, node degree does not explain all the 400

variation in node betweenness regardless the timing of the gene transfer(s) (70% vs. 95% 401

thresholds) and/or to the functions that are transferred (virulence vs. antibiotic resistance 402

determinants) – the values are most likely determined by the mobile nature of plasmids 403

themselves. 404

405

Analysis of mobilizable and conjugative plasmids 406

Conjugative plasmids have been defined as “vessels” of the communal gene pool (Norman, 407

Hansen, Sorensen 2009). Indeed, this class of plasmids possesses the ability to “visit” 408

different cells and, in principle, undergo genetic rearrangements (such as homologous 409

recombination) with other plasmids and/or other informative molecules (phage genomes and 410

chromosomes). For this reason, conjugative plasmids might be expected to possess a more 411

central position within the overall plasmid gene sharing network in respect to those that are 412

not mobilizable. To test this hypothesis all the tra- and mob-like sequences of the plasmids 413

were eliminated from the networks and the centrality measures of conjugative/mobilizable 414

plasmids were evaluated. Plasmid mobility was estimated by identifying the number of mob 415

and tra genes that they harbor (an approach similar to that adopted in (Smillie et al. 2010) and 416

described in Methods). The relationship existing between the mobility and the network 417

measures was investigated by studying the distribution of the centrality measures between the 418

mobile and non-mobile plasmids. The distributions of the centrality measures are presented in 419

Figure 6 and are significantly higher for mobilizable plasmids in the networks of all genes and 420

resistance genes (p-values according to Mann-Whitney tests are presented in Figure 6). 421

Therefore, the presence of mob or tra genes significantly promotes the gene sharing measures 422

in the networks of all genes and antibiotic resistance genes. This suggests that plasmid 423

mobility is an important mechanism in spreading various genetic traits within the plasmid 424

community, including antibiotic resistance genes. This fully agrees with the central role 425

inferred for conjugative plasmids in the context of bacterial evolution (Norman, Hansen, 426

Sorensen 2009) and gives further support to the idea that these particular plasmids act as 427

vessels of the communal gene pool. This also indicates that the high incidence of high degree 428

and betweenness values in certain phylogenetic classes (such as Gammaproteobacteria) does 429

not only result from their over-representation in current data set but are also affected by 430

genetic properties of their plasmids. 431




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Gene sharing over phylogenetic classes 432

The importance of plasmids within the complex microbial evolutionary network resides also 433

in the capability to connect microbes separated by a (more or less) long phylogenetic distance 434

and to overcome the various barriers to horizontal gene transfer (Thomas, Nielsen 2005). The 435

occurrence of gene sharing over phylogenetic classes was enumerated and visualized in 436

Figure 7. 437

Interestingly, some connections in the network span over very large phylogenetic distances. 438

For example we found connections linking Alphaproteobacteria and Cyanobacteria and in 439

particular plasmid pCC7120beta from Nostoc sp. PCC 7120 with plasmid pBBta01 from 440

Bradyrhizobium sp. BTAi1 and pCC7120gamma from Nostoc sp. PCC 7120 with plasmid 441

pNGR234b from Sinorhizobium fredii NGR234. These connections suggest the presence of 442

HGT among microorganisms inhabiting very different ecological niches (multiple and host 443

associated for Cyanobacteria and Alphaproteobacteria, respectively), involving genes linked 444

to important functions such as copper transport and transcriptional regulation, respectively. 445

Remarkably, also inter-kingdom transfers (involving chemotaxis related genes) were 446

observed: this is the case, for example, of connections linking plasmid pH308197_258 from 447

Bacillus cereus H3081.97 to plasmid pHmuk01 from Halomicrobium mukohataei DSM 448

12286. Also in this case, microorganisms belong to likely unrelated habitats (multiple and 449

specialized, respectively). 450

However, because the amount of inter-classes connections is likely strongly affected by 451

sampling biases, we performed statistical tests to investigate the significance of the observed 452

inter-class connections by performing random permutation of the original network, as 453

described in Methods. In the 70% identity network, inter-class links included connections 454

between more closely related microorganisms (e.g. connections between Alpha, Beta and 455

Gammaproteobacteria and between Bacilli and Lactobacilli) as well as connections between 456

more distantly related microorganisms (i.e. Actinobacteria and Betaproteobacteria, 457

Actinobacteria and Gammaproteobacteria, Alphaproteobacteria and Deinococci). However, 458

some closely related microorganisms possessed a lower amount of connections than expected 459

by chance (e.g. between Alphaproteobacteria and Gammaproteobacteria, p-value<1e-4), 460

possibly indicating a genetic incompability between these groups (Thomas, Nielsen 2005). As 461

it might be expected, when analyzing the 95% network, the number of observed connections 462

decreased and mainly closely related taxonomic groups were still interconnected (Bacilli-463




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Lactobacilli and Betaproteobacteria-Gammaproteobacteria (p-value<1e-4) among over-464

represented and Alphaproteobacteria-Gammaproteobacteria and Bacilli-Gammaproteobacteria 465

among under-represented (p-value<1e-4)). Notably, the connection between distantly related 466

Gammaproteobacteria and Actinobacteria also remained strong. 467

As noted in the case of gene transfers among phylogenetically incoherent groups (see 468

Supplemental Material S5), the majority of shared genes code for functions that are related to 469

the process of HGT itself and generally belong to L category in COG annotation (Figure 8). 470

Nevertheless, also other functions are exchanged as indicated by gene sharing (Figure 8), 471

underlining the key role of plasmids in spreading important biological traits throughout the 472

whole microbial world. 473

Gene transfer between Actinobacteridae and Gammaproteobacteria 474

According to the results presented in Figure 7, the gene sharing between Actinobacteria and 475

Gammaproteobacteria is spanning one of the longest phylogenetic distances within our 476

networks (Supplemental Information S8) and appears to be crucial in transferring antibiotic 477

resistance genes. Furthermore, most of the shared genes are (at least) 95% similar and 478

therefore, according to the molecular clock hypothesis, the transfer between these classes has 479

occurred recently. For this reason, we further analyzed this, apparently preferential, gene 480

flow. 481

To better characterize the gene sharing between Actinobacteria and Gammaproteobacteria, we 482

selected representative plasmids with a high amount of shared genes between 483

Gammaproteobacteria and Actinobacteria and visualized them as circular ideogram with 484

resistance-, conjugation- and transposition-related genes and gene sharing events (Figure 9). 485

The analysis of Figure 9 revealed that the antibiotic resistance genes transfer between the 486

plasmids by transposition, as most of the links connecting Actinobacteria and 487

Gammaproteobacteria fall in plasmid regions embedding antibiotic resistance and/or 488

transposition-related genes. These results indicate the presence of a clinically-important gene 489

flow between representatives of these microbial groups, although not suggesting the possible 490

direction of these gene transfers (i.e. from Actinobacteria to Gammaproteobacteria or 491

viceversa). To shed some light on this point we investigated the composition of the involved 492

plasmids under the assumption that, if the HGT events are recent (as suggested by the high 493

amino acid identity) the transferred genes are expected to have a GC content closer to the 494

donor plasmids rather than to the recipient one (Karlin 2001). Hence, the GC content of the 495




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Actinobacterial and Gammaproteobacterial plasmids and genes was calculated and compared 496

(Supplemental Information S9). The Actinobacterial plasmid GC content (mean 0.56% from 7 497

plasmids) was significantly higher (p-value = 9.4e-3 according to a Mann-Whitney test) than 498

the Gammaproteobacterial GC content (mean 0.51% from 95 plasmids). Moreover, GC 499

contents were calculated for the individual transferred genes and compared to the plasmids. 500

According to Mann-Whitney test, the transferred genes have a significantly different GC 501

content from the Gammaproteobacterial plasmids (p =7.0e-15) but are not significantly 502

different from Actinobacterial plasmids (p = 0.42). Accordingly, the whole body of data 503

presented in this section suggests that the direction of gene transfer is very likely from 504

Actinobacteria to Gammaproteobacteria. This is consistent with the knowledge that some 505

Actinobacteria are natural producers of antibiotic compounds and, therefore, a potential 506

source of antibiotic resistance genes to human pathogens (Wright 2007; Miao, Davies 2010). 507

Conclusions 508

The use of gene sharing network as a tool to investigate microbial evolutionary relationships 509

is rapidly expanding, especially when studying non-tree like structures that sometimes can 510

arise in evolution (Dagan, Artzy-Randrup, Martin 2008; Halary et al. 2010). The power of 511

such approach is demonstrated here by revealing the relationships between biological 512

properties (e.g. plasmids mobility) and network properties (e.g. plasmid centrality) in the gene 513

sharing network. Moreover, the approach applied here also revealed an extensive antibiotic 514

resistance gene sharing between Actinobacterial and Gammaproteobacterial plasmids, 515

suggesting a potential source of antibiotic resistance genes that might have led to the recent 516

emergence of antibiotic multiresistance in pathogenic organisms. 517

The plasmid sequences analysed in this study were gathered in a non-systematic manner from 518

different sequencing projects; their sampling is therefore random and likely biased towards 519

human pathogenic organisms. The bioinformatic workflow described here would be best 520

suited for single genomic sequence data sets obtained from specifically selected 521

environments. We expect such data sets to become available as the DNA sequencing costs 522

decrease and genome sequencing from single cells becomes a routine approach 523

(Stepanauskas, Sieracki 2007; Rodrigue et al. 2009). The proposed approach could then be 524

used to investigate whether the functional categories of transferred genes would reflect the 525

different selective patterns present in the given environment(s). Therefore, obtaining single 526




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genome data sets from multiple different environments would permit evaluation and 527

comparison of gene sharing patterns in response to different environmental conditions. 528

529

530

Table 1 531

Individual plasmids with highest degree measures observed in the gene sharing networks of 532

all genes. 533

Accession Number Microorganism Plasmid name Degree

N. of tra/mob genes

Conjugative (c) or mobilizable (m)

70% Network

NC_010119 Salmonella enterica subsp. enterica serovar Choleraesuis pOU7519 268

17 c

NC_006856

Salmonella enterica subsp. enterica serovar Choleraesuis str. SC-B67 pSC138 254

17 c

NC_011964 Escherichia coli pAPEC-O103-ColBM 253 8 c NC_013951 Klebsiella pneumoniae pKF3-140 247 9 c NC_013728 Escherichia coli O26:H- pO26-CRL 243 21 c NC_010488 Escherichia coli SMS-3-5 pSMS35_130 242 13 c

NC_011092

Salmonella enterica subsp. enterica serovar Schwarzengrund str. CVM19633 pCVM19633_110 241

17 c

NC_006816 Salmonella enterica subsp. enterica serovar Typhimurium pU302L 240

17 c

NC_013122 Escherichia coli pEK499 231 15 c

NC_013437 Salmonella enterica subsp. enterica serovar Typhimurium pSLT-BT 225

4 c

95% Network

NC_006816 Salmonella enterica subsp. enterica serovar Typhimurium pU302S 192

16 c

NC_010488 Escherichia coli SMS-3-5 pSMS35_130 188 13 c

NC_006856

Salmonella enterica subsp. enterica serovar Choleraesuis str. SC-B67 pSC138 187

17 c

NC_013951 Klebsiella pneumoniae pKF3-140 186 9 c NC_011964 Escherichia coli pAPEC-O103-ColBM 184 8 c

NC_010119 Salmonella enterica subsp. enterica serovar Choleraesuis pOU7519 171

17

NC_013728 Escherichia coli O26:H- pO26-CRL 168 21 c NC_013122 Escherichia coli pEK499 166 15 c




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NC_011092


17 c

NC_004998 Escherichia coli p1658/97 157 11 c 534

535

536

Table 2 537

Individual plasmids with highest betweenness measures observed in the gene sharing 538

networks of all genes. 539

Accession Number Microorganism Plasmid name Betweenness

N. of tra/mob genes

Conjugative (c) or mobilizable (m)

70% Network

NC_007635 Escherichia coli pCoo 8050 10 c/m

NC_006663 Staphylococcus epidermidis RP62A pSERP 6329

3 m

NC_007974 Cupriavidus metallidurans CH34 megaplasmid 6067

14 c

NC_011092


17 c

NC_010558 Escherichia coli 1520 pIP1206 5750 16 c

NC_009651

Klebsiella pneumoniae subsp. pneumoniae MGH 78578 pKPN5 5641

11

NC_011339 Bacillus cereus H3081.97 pH308197_258 5507 2 m NC_011655 Bacillus cereus AH187 pAH187_270 5330 7 c/m NC_012586 Rhizobium sp. NGR234 pNGR234b 5271 88 c NC_010980 Enterococcus faecium pVEF3 4700 4 m

95% Network

NC_011092


17 c

NC_005024 Staphylococcus aureus pSK41 29020 7 c NC_012547 Staphylococcus aureus pGO1 29020 9 c NC_010378 Escherichia coli pOLA52 21221 3 c




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NC_005054 Staphylococcus aureus pLW043 19209 6 c NC_009435 Lactococcus lactis pGdh442 18216 7 m

NC_004669 Enterococcus faecalis V583 pTEF1 15617

8

NC_008381 Rhizobium leguminosarum bv. viciae 3841 pRL10 15030

27 c

NC_013121 Escherichia coli pEK516 13724 11 c NC_005327 Escherichia coli pC15-1a 13073 9 c

NC_011996 Macrococcus caseolyticus JCSC5402 pMCCL2 12981

4 m

540

541

542

543




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Figure 1: 543

The gene sharing between plasmids presented as matrices (A) and networks (B) at both 70% 544

and 95% criteria. In network figures, plasmids are represented by the nodes (node size is 545

proportional to the plasmid size) and the shared genes by the links. At least five shared genes 546

are required to establish a link. 547

548

549

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549 Figure 2: 550

The major phylogenetic groups, their habitats and their clustering in A) the 70% and the 95% 551

networks for >= 5 networks and in 70% and the 95% networks for < 5 networks, B) and C), 552

respectively. The clusters that were subjected to Conclustador analysis have been indicated.In 553

D) the amount of inter- and intraphylum and inter- and intraclass clustering in the networks is 554

reported for both < 5 (low) and >= 5 (high) networks The clustering of the network has been 555

determined using the Louvain algorithm implemented in Gephi (see Methods). 556

557

558

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Intra−class Inter−habitat

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Intra−class Inter−habitat

Intra−class Intra−habitat

70%95%

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558 Figure 3: 559

Dependency of plasmid betweenness from plasmid degree for different phylogenetic classes 560

according to Pearson’s product moment correlation coefficient. 561

562

563

Degree

Betweenness

0

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Unassigned

r2 = 0.35 p-value < 2.2e-16

r2 = 0.40 p-value < 2.2e-16

r2 = 0.46 p-value < 2.2e-16

r2 = 0.52 p-value < 2.2e-16

r2 = 0.39 p-value < 2.2e-16

r2 = 0.28 p-value = 8.9e-16

r2 = 0.18 p-value = 6.6e-14

r2 = 0.35 p-value = 1.8e-6

r2 = 0.81 p-value < 2.2e-16

r2 = 0.17 p-value = 1.4e-10

r2 = 0.36 p-value < 2.2e-16

r2 = 0.67 p-value < 2.2e-16

r2 = 0.13 p-value < 2.2e-16

r2 = 0.33 p-value < 2.2e-16

r2 = 0.1 p-value = 8.2e-2

r2 = 0.41 p-value = 9.0e-8




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563 Figure 4: 564

The phylogenetic class distribution of the disconnected plasmids in the data set.A plus sign 565

(+) is used to mark the inter-class transfers that were more abundant than expected by random 566

assignment of the transfer events between plasmids (permutation test, p-value < 1e-4). A 567

minus sign (-) is used to mark the inter-class transfers that were less abundant than expected 568

by random assignment of the transfer events between plasmids (permutation test, p-value < 569

1e-4). 570

571

572

Percentage

AcaryochlorisActinobacteridae

AlphaproteobacteriaAquificales

ArchaeoglobiBacillalesBacteria

BacteroidiaBangiophyceae

BetaproteobacteriaChlamydiales

ChlorobiaChroococcales

ClostridiaCytophagia

DeferribacteralesDeinococci

DeltaproteobacteriaDikarya

EpsilonproteobacteriaErysipelotrichiFlavobacteria

FlorideophyceaeFusobacteriales

GammaproteobacteriaHalobacteria

HerpetosiphonalesLactobacillales

MethanobacteriaMethanococci

MethanomicrobiaMollicutes

MycetozoaNitrospirales

NostocalesOscillatoriales

PlanctomycetaciaSatellite Nucleic Acids

SchizopyrenidaSphingobacteria

SpirochaetalesStreptophytaThermococci

ThermomicrobialesThermoplasmata

ThermoproteiThermotogales

Unassigned

General

5% 10%

15%

Resistance

5% 10%

15%

Virulence

5% 10%

15%

Identity70%95%

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572 Figure 5. 573

Dependency of plasmid betweenness from plasmid degree for the major networks built in this 574

work according to Pearson’s product moment correlation coefficient. Networks of < 5 and >= 575

5 connections are indicated as Low and High, respectively. 576

577

578

Degree

Betweenness

0

1000

2000

3000

4000

5000

6000

0

500

1000

1500

2000

High

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7095

r2 = 0.25 p-value < 2.2e-16

r2 = 0.44 p-value < 2.2e-16

r2 = 0.36 p-value < 2.2e-16

r2 = 0.31 p-value < 2.2e-16

r2 = 0.25 p-value < 2.2e-16

r2 = 0.32 p-value < 2.2e-16

r2 = 0.35 p-value < 2.2e-16

r2 = 0.30 p-value < 2.2e-16




ownloaded from


578 Figure 6. 579

The relationship between the network centrality measures and plasmid mobility. The mobile 580

plasmids are significantly more central in the networksof all and resistance genes, as indicated 581

by the p-values (calculated with Mann-Whitney tests) embedded in the figure. 582

583

584

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Betweenness

Similarity70%95%

70% p-value 8.8e-16 95% p-value 4.8e-6

70% p-value 5.2e-3 95% p-value 1.7e-3

70% p-value 6.2e-1 95% p-value 2.1e-1

70% p-value 3.3e-19 95% p-value 1.5e-6

70% p-value 7.9e-3 95% p-value 4.9e-2

70% p-value 1.7e-1 95% p-value 2.9e-1




ownloaded from


584 Figure 7 585

The frequency of inter-class gene transfer events in the networks. A plus sign (+) is used to 586

mark the inter-class transfers that were more abundant than expected by random assignment 587

of the transfer events between plasmids (permutation test, p-value < 1e-4). A minus sign (-) is 588

used to mark the inter-class transfers that were less abundant than expected by random 589

assignment of the transfer events between plasmids (permutation test, p-value < 1e-4). 590

591

592

Acaryochloris−ChroococcalesActinobacteridae−AlphaproteobacteriaActinobacteridae−BetaproteobacteriaActinobacteridae−Deltaproteobacteria

Actinobacteridae−GammaproteobacteriaActinobacteridae−Halobacteria

Actinobacteridae−LactobacillalesActinobacteridae−NitrospiralesAlphaproteobacteria−Bacillales

Alphaproteobacteria−BetaproteobacteriaAlphaproteobacteria−Chroococcales

Alphaproteobacteria−DeinococciAlphaproteobacteria−Deltaproteobacteria

Alphaproteobacteria−GammaproteobacteriaAlphaproteobacteria−Nitrospirales

Alphaproteobacteria−NostocalesBacillales−Betaproteobacteria

Bacillales−ClostridiaBacillales−Deltaproteobacteria

Bacillales−GammaproteobacteriaBacillales−Halobacteria

Bacillales−LactobacillalesBacillales−Thermomicrobiales

Bacteria−GammaproteobacteriaBetaproteobacteria−Deinococci

Betaproteobacteria−DeltaproteobacteriaBetaproteobacteria−Gammaproteobacteria

Betaproteobacteria−NitrospiralesChroococcales−Gammaproteobacteria

Chroococcales−NostocalesDeinococci−Gammaproteobacteria

Deinococci−ThermomicrobialesDeltaproteobacteria−Gammaproteobacteria

Deltaproteobacteria−NitrospiralesGammaproteobacteria−Lactobacillales

Gammaproteobacteria−NitrospiralesNostocales−Thermomicrobiales

All sequences

0 100200300400500600

Resistance0 100200300400500600

Virulence

0 100200300400500600

Identity70%95%

Frequency

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592 Figure 8 593

COG functional annotation of the genes shared by the plasmids belonging to the different 594

taxonomical classes of the dataset. 595

596

597

Acaryochloris−ChroococcalesActinobacteridae−AlphaproteobacteriaActinobacteridae−BetaproteobacteriaActinobacteridae−Deltaproteobacteria

Actinobacteridae−GammaproteobacteriaActinobacteridae−Halobacteria

Actinobacteridae−LactobacillalesActinobacteridae−NitrospiralesAlphaproteobacteria−Bacillales

Alphaproteobacteria−BetaproteobacteriaAlphaproteobacteria−Chroococcales

Alphaproteobacteria−DeinococciAlphaproteobacteria−Deltaproteobacteria

Alphaproteobacteria−GammaproteobacteriaAlphaproteobacteria−Nitrospirales

Alphaproteobacteria−NostocalesBacillales−Betaproteobacteria

Bacillales−ClostridiaBacillales−Deltaproteobacteria

Bacillales−GammaproteobacteriaBacillales−Halobacteria

Bacillales−LactobacillalesBacillales−Thermomicrobiales

Bacteria−GammaproteobacteriaBetaproteobacteria−Deinococci

Betaproteobacteria−DeltaproteobacteriaBetaproteobacteria−Gammaproteobacteria

Betaproteobacteria−NitrospiralesChroococcales−Gammaproteobacteria

Chroococcales−NostocalesDeinococci−Gammaproteobacteria

Deinococci−ThermomicrobialesDeltaproteobacteria−Gammaproteobacteria

Deltaproteobacteria−NitrospiralesGammaproteobacteria−Lactobacillales

Gammaproteobacteria−NitrospiralesNostocales−Thermomicrobiales

Actinobacteridae−AlphaproteobacteriaActinobacteridae−Betaproteobacteria

Actinobacteridae−GammaproteobacteriaActinobacteridae−Lactobacillales

Alphaproteobacteria−BetaproteobacteriaAlphaproteobacteria−Gammaproteobacteria

Bacillales−GammaproteobacteriaBacillales−Lactobacillales

Betaproteobacteria−GammaproteobacteriaDeltaproteobacteria−Gammaproteobacteria

Gammaproteobacteria−Lactobacillales[C] Energy production and conversion

[D] Cell cycle control, cell division, chromosom

e partitioning

[E] Amino acid transport and m

etabolism

[F] Nucleotide transport and metabolism

[G] Carbohydrate transport and m

etabolism

[H] Coenzyme transport and m

etabolism

[I] Lipid transport and metabolism

[J] Translation, ribosomal structure and biogenesis

[K] Transcription

[L] Replication, recombination and repair

[M] Cell wall/m

embrane/envelope biogenesis

[N] Cell motility

[O] Posttranslational m

odification, protein turnover, chaperones

[P] Inorganic ion transport and metabolism

[Q] Secondary m

etabolites biosynthesis, transport and catabolism

[R] General function prediction only

[S] Function unknown

[T] Signal transduction mechanism

s

no functional class

70%95%

Frequency

0

100

200

300

400




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597 Figure 9 598

An ideogram of gene transfers between Actinobacterial plasmids (accession numbers 599

NC_004939, NC_004945 and NC_014167) and Gammaproteobacterial plasmids (accession 600

numbers NC_006816, NC_009141, NC_009651, NC_010488, NC_010886 and NC_011092). 601

Gene transfer events are marked using the curves in the middle of the ideogram. GC content 602

of the plasmids is plotted on the outer side of the plasmid molecules if it is above the average 603

of the GC content of the corresponding plasmid. Genes related to resistance, conjugation and 604

transposition are marked as lines on outer, middle and innermost rings, respectively, on the 605

inner side of the plasmid ring. 606

607

608




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Supplemental Information: 608

Supplemental Information S1: Detailed information for the complete sequence dataset used 609

in this work. 610

Supplemental Information S2: Complete antibiotic resistance related sequence dataset used 611

in this work. 612

Supplemental Information S3: Complete virulence factors related sequence dataset used in 613

this work. 614

Supplemental Information S4: The overall Gephi-formatted networks built in this work. 615

Supplemental Information S5: Phylogenetic coherence for 70% and 95% network clusters 616

and COG functional categories of genes shared within the major clusters. For each plasmids 617

cluster we report a) phylogenetic network built with Splitstree using as input all the different 618

ML phylogenetic trees obtained from the alignments of all the gene families shared by the 619

different plasmids included int the cluster, b) phylogenetic networks obtained using as input 620

the gene families belonging to coherent groups as assessed by Conclustador and c) the COG 621

functional annotation of the shared sequence 622

Supplemental Information S6: Networks built with antiobiotic resistance (A and B) and 623

virulence (C and D) related sequences at 70% and 95% identity thresholds 624

Supplemental Information S7: Networks built lowering the threshold of gene sharing to 625

between 1 and 5 genes at A) 70% and B) 95% identity thresholds. In C) and D) the 626

phylogenetic class distribution of the disconnected plasmids and their inter-taxa connections 627

are also reported. 628

Supplemental Information S8: Phylogenetic distances of gene sharing between 629

Actinobacterial and Gammaproteobacterial plasmids. 630

Supplemental Information S9: GC contents of Actinobacterial and Gammaproteobacterial 631

plasmids and the transferred genes. 632

633

Acknowledgements 634




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The study was financially supported by the Academy of Finland (grant number 129873) and 635

the Finnish Graduate School in Environmental Science and Technology (EnSTe). MF is 636

financed by a post-doctoral grant from “Fondazione Adriano Buzzati-Traverso”. The authors 637

would like to thank Kimmo Mattila for his kind assistance in parallel BLAST analyses. 638

639

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Large-Scale Analysis of Plasmid Relationships through Gene-Sharing Networks

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