The Association of Virulence Factors with Genomic
Islands
Shannan J. Ho Sui
.
, Amber Fedynak
.
, William W. L. Hsiao
¤
, Morgan G. I. Langille, Fiona S. L. Brinkman*
Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia, Canada
Abstract
Background: It has been noted that many bacterial virulence factor genes are located within genomic islands (GIs; clusters
of genes in a prokaryotic genome of probable horizontal origin). However, such studies have been limited to single genera
or isolated observations. We have performed the first large-scale analysis of multiple diverse pathogens to examine this
association. We additionally identified genes found predominantly in pathogens, but not non-pathogens, across multiple
genera using 631 complete bacterial genomes, and we identified common trends in virulence for genes in GIs. Furthermore,
we examined the relationship between GIs and clustered regularly interspaced palindromic repeats (CRISPRs) proposed to
confer resistance to phage.
Methodology/Principal Findings: We show quantitatively that GIs disproportionately contain more virulence factors than
the rest of a given genome (p,1E-40 using three GI datasets) and that CRISPRs are also over-represented in GIs. Virulence
factors in GIs and pathogen-associated virulence factors are enriched for proteins having more ‘‘offensive’’ functions, e.g.
active invasion of the host, and are disproportionately components of type III/IV secretion systems or toxins. Numerous
hypothetical pathogen-associated genes were identified, meriting further study.
Conclusions/Significance: This is the first systematic analysis across diverse genera indicating that virulence factors are
disproportionately associated with GIs. ‘‘Offensive’’ virulence factors, as opposed to host-interaction factors, may more often
be a recently acquired trait (on an evolutionary time scale detected by GI analysis). Newly identified pathogen-associated
genes warrant further study. We discuss the implications of these results, which cement the significant role of GIs in the
evolution of many pathogens.
Citation: Ho Sui SJ, Fedynak A, Hsiao WWL, Langille MGI, Brinkman FSL (2009) The Association of Virulence Factors with Genomic Islands. PLoS ONE 4(12): e8094.
doi:10.1371/journal.pone.0008094
Editor: Niyaz Ahmed, University of Hyderabad, India
Received October 6, 2009; Accepted November 7, 2009; Published December 1, 2009
Copyright:
2009 Ho Sui et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded by grants from International Business Machines Corporation (IBM), Genome Canada/Genome BC (http://www.genomecanada.
ca), the Cystic Fibrosis Foundation (http://www.cff.org), and the SFU Community Trust Endowment Fund. WWLH and FSLB are a Michael Smith Foundation for
Health Research (MSFHR; www.msfhr.org) Trainee and Senior Scholar, respectively, and they are also Canadian Institutes for Health Research (CIHR; www.cihr-irsc.
gc.ca) Doctoral award and New Investigator award recipients, respectively. MGIL is a recipient of a MSFHR/CIHR bioinformatics training award and SJHS is a
recipient of the 2009 Canadian Cystic Fibrosis Foundation Kin Canada Fellowship (www.cysticfibrosis.ca). The funders had no role in study design, data collection
and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: brinkman@sfu.ca
¤ Current address: Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
. These authors contributed equally to this work.
Introduction
The establishment of infection is mediated by virulence factors,
which can be generally defined as bacterial products or strategies
that contribute to the ability of the bacterium to cause disease.
Most bacterial virulence factors were originally thought to be
associated with pathogens. However, as genome sequences from
non-pathogenic, commensal bacteria were obtained, it became
clear that many ‘‘classic’’ virulence factors, such as adhesions, were
also encoded in the genomes of commensal bacteria [1,2]. It has
therefore been proposed that such virulence factors should be
more generally referred to as ‘‘host-interaction factors’’ [3].
Microarray analyses also supported these findings; for example,
many of the known virulence associated genes in pathogenic
Neisseria sp., were also found to be present in the closely-related
non-pathogen Neisseria lactamica [4]. However, it is evident that
certain types of genes, such as botulinum toxin, are both necessary
and sufficient to cause disease on their own [5]. While it is
generally appreciated that microbial pathogenesis is a complex
process that reflects an interplay of pathogen, host, and
environmental factors, we wished to examine to what degree
there may be virulence factors that are so critical for disease
processes that their very presence is strongly associated with
disease, rather than simply host colonization/interaction.
With the number and diversity of bacterial genomes sequenced,
we can investigate selected observations regarding pathogenicity
and quantify them on a more global scale. In particular, it has
been noted that many virulence genes are associated with genomic
islands (GIs; clusters of genes of probable horizontal origin) [6–13].
The first GIs identified were in fact called pathogenicity islands
(PAIs) [2,14]. Since then, many others have frequently noted the
apparent association of virulence factors with such horizontally
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acquired regions (reviewed in [2,7,8,10–13,15,16]). However, no
analysis has yet been reported that examines whether this trend is
systematically true across diverse lineages of pathogens.
Such an analysis is now possible as methods for high quality GI
prediction have been developed and we have access to additional
datasets of known GIs [17,18]. In addition, a curated dataset of
virulence factors is available through the Virulence Factor
Database (VFDB) [19,20], which may be cross-referenced with
current bacterial genome datasets. The numerous genomes that
have been sequenced from both pathogenic and non-pathogenic
strains of diverse bacterial genera permit us to investigate the
degree to which there are classes of genes that may be pathogen-
specific or notably pathogen-associated. Previous analyses of
pathogen-specific genes have been limited to certain species or
genera (for example, [4,21–25]), but a large-scale analysis is now
possible. While such an analysis is still limited by the scope of
bacterial genome sequences and virulence factors currently
available, any virulence factors observed to be present in
pathogenic strains from diverse bacterial genera, with no
detectable homologs in non-pathogenic strains of the same genera,
are considered good candidates for being classified as pathogen-
associated. We set out to examine whether such genes could be
identified within a diverse bacterial genome dataset, and to
examine common features of such genes with the hypothesis that
they may play more virulence-specific roles in pathogens. Such
genes also represent targets for possible novel therapeutic strategies
that interfere with pathogen-specific traits that contribute to
pathogenesis [26,27].
For this study, we characterized the prevalence of pathogen-
associated virulence factors, and virulence factors in general, in
both whole bacterial genomes and in GIs. We show that certain
types of virulence factors are strongly associated with both
pathogens and GIs. We note that our definition of a virulence
factor simply requires that the gene be known to be involved in
virulence in one host to date. Although any given virulence factor
may be essential to pathogenesis in some hosts but not others, this
simple definition allows us to examine all genes found to be
involved in virulence and compare them to genes that have not
been found to be involved in virulence in any host to date,
providing insights into general trends that is not possible through
targeted analysis of individual pathogens or genes. The implica-
tions of our results on therapeutic development and the evolution
of pathogenicity are discussed.
Results
Virulence factors are disproportionately found in
genomic islands
Isolated studies of selected, closely related pathogenic strains
have suggested that genes involved in virulence are dispropor-
tionately associated with PAIs, a subclass of GIs [10,15].
However, to date this association has never been quantified in
a large-scale analysis encompassing multiple diverse pathogen
genomes. In order to validate this observation, we used a
dataset of 1568 virulence factors from the curated VFDB
[19,20] and quantified the occurrence of virulence factors in
GIs for an initial group of 37 pathogens (representing 32
species and 23 genera that contained virulence factors from the
VFDB and also had complete genome sequences available). To
prevent circular logic where known PAIs are defined by the
presence of virulence factors, and virulence factors are,
therefore, found predominately in PAIs, we defined GIs based
on attributes that are independent of such gene content or
prior knowledge in the literature. We used three GI prediction
methods that were used previously for other analyses of GIs
and are considered effective methods for identifying GIs on a
high-throughput scale [17,18].
For our first analysis, a GI was defined as a region consisting of
eight or more open reading frames (ORFs) with dinucleotide bias
(calculated as the frequency of dinucleotides in a cluster of ORFs
compared to the entire genome) as predicted by IslandPath-
DINUC [17,28]. This GI prediction method is noted for having
more sensitivity/recall in predicting GIs versus other methods
studied in an evaluation of GI predictors [18]. Consistent with
previous anecdotal reports, our analysis indicated that GIs indeed
contain a significantly higher proportion of virulence factors
compared to non-GIs. On average for all the pathogens studied,
5.1% of genes in predicted IslandPath-DINUC islands are
virulence factors, compared with 1.3% of genes outside of islands
(p = 1.2E-135; Table 1). The significance of this is notable, given
that such GI prediction methods tend to under-predict GIs [18].
Virulence factors were also enriched in GIs predicted using the
more stringent and more specific/precise IslandPath-DIMOB
method [17,28,29], which requires both dinucleotide bias and the
presence of one or more mobility genes in the GI region (p = 1.3E-
44; Table 1), as well as in GIs predicted using SIGI-HMM, which
is based on an analysis of codon usage that removes ribosomal
regions that may be falsely predicted as GIs (p = 4.9E-95) [30].
IslandPath-DIMOB and SIGI-HMM both have the highest
overall accuracies for sequence composition-based prediction of
GIs to date, but with lower sensitivity/recall than IslandPath-
DINUC [18]. Regardless of which criterion was used, there was
clearly a bias in terms of proportionately more virulence factors
being located in predicted GI regions.
A comparison of the virulence factors predicted to be in GIs by
the different methods showed that while IslandPath-DINUC and
SIGI-HMM agreed to a large extent (45% of virulence factors in
predicted IslandPath-DINUC GIs were also in predicted SIGI-
HMM GIs, and 67% of virulence factors in predicted SIGI-HMM
GIs were also in predicted IslandPath-DINUC GIs), they are
complementary approaches that each produce unique predictions
(Figure S1). In fact, an analysis of the accuracy of combining the
two methods together, using the approach published recently [18],
revealed that the sensitivity/recall of these GI prediction methods
combined increases notably (from 33% or 36% for SIGI-HMM or
IslandPath-DIMOB, respectively, to 48% for the combined
methods, with precision being maintained at 86%). However, for
our analyses we wished to show that, regardless of the GI
prediction method used, the results were significant; hence why we
examined results using these three different GI prediction
methods.
We then further examined the distribution of virulence
factors in GIs by genus. We found that the enrichment of
virulence factors in GIs is largely consistent with different
pathogen lifestyles (Figure 1; see Figure S2 showing results for
all three GI prediction methods). Pathogens capable of
inhabiting multiple environmental niches, such as Campylobacter,
Vibrio, Escherichia and Pseudomonas spp. exhibited the highest
proportion of virulence factor genes in IslandPath-DINUC GIs.
In comparison, for intracellular pathogens with limited
horizontal gene transfer, such as Chlamydia, Mycobacterium and
Legionella, we found no difference in the proportions of virulence
factors inside and outside of GIs. Although none of the known
Bordetella virulence factors resided in the predicted IslandPath-
DINUC GIs, SIGI-HMM did identify the region spanning the
Bordetella virulence factors (toxins and type III secretion
components) as a predicted GI, further highlighting the
complementary nature of the two approaches.
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Virulence factors in GIs tend to play more ‘‘offensive’’ roles
To study whether specific types of virulence factors are more
likely to be associated with such probable horizontally transferred
regions, we divided the virulence factors into 42 virulence-related
categories adapted from the VFDB classification scheme, and
examined the functional categories of virulence factors in GIs
versus outside of GIs (with statistical corrections for multiple
testing). We found that virulence factors over-represented in GIs
are classified as type III secretion system and type IV secretion
system components (including their corresponding effector pro-
teins), as well as toxins and adherence factors (Table 2); such
proteins comprise some of the most offensive weapons available to
pathogens. These results are consistent with previous observations
that type III and type IV secretion systems are closely associated
with PAIs [16]. Type III and type IV secretion system genes were
not, however, more significantly associated with GIs predicted
specifically by IslandPath-DIMOB. It should be noted that such
secretion systems may not have the types of mobile genes near
Figure 1. Enrichment of virulence factors (VFs) in GIs by pathogens. The proportion (%) of genes that are VFs in GIs (predicted by the
IslandPath–DINUC method) for pathogens grouped by genus is shown in red, versus the proportion of genes that are VFs outside of GIs, which is
shown in blue.
doi:10.1371/journal.pone.0008094.g001
Table 1. Genomic Islands (GIs) contain higher proportions of virulence factors (VFs) than non–GI.
GI Identification Method VF Dataset
a
No. of VFs/Total no. of
genes in GIs
b
(%)
No. of VFs/Total no. of
genes in non-GIs
b
(%) p-value
c
IslandPath-DINUC
d
(more sensitive method) All VFs 581/11437 (5.1) 1054/83161 (1.3) 1.2E-135
Pathogen-associated
g
VFs 160/10157 (1.6) 151/72201 (0.2) 2.8E-63
‘‘Common’’
h
VFs 421/11318 (3.7) 854/81791 (1.0) 6.4E-86
IslandPath-DIMOB
e
(more specific method) All VFs 217/4601 (4.7) 1246/84832 (1.5) 1.3E-44
Pathogen-associated
g
VFs 58/4030 (1.4) 217/74311 (0.3) 3.7E-20
‘‘Common’’
h
VFs 159/4559 (3.5) 979/83391 (1.2) 1.2E-29
SIGI-HMM
f
(more specific method) All VFs 387/7618 (5.1) 1039/80770 (1.3) 4.9E-95
Pathogen-associated
g
VFs 116/7029 (1.6) 149/71224 (0.2) 7.0E-51
‘‘Common’’
h
VFs 271/7616 (3.6) 890/79283 (1.1) 5.0E-51
a VFs are defined as those genes curated as being VFs according to the VFDB. Only VFs in the VFDB where GI predictions were available from IslandPath/SIGI-HMM were
included in the analysis.
b Total number of genes in GIs varies according to the number of genomes used that contain pathogen-associated, ‘‘Common’’, or both types of VFs.
c Fisher’s exact test.
d GIs are defined as 8 or more consecutive ORFs with dinucleotide bias as predicted with IslandPath-DINUC.
e GIs are defined as 8 or more consecutive ORFs with dinucleotide bias plus presence of 1 or more mobility genes within the region as predicted with IslandPath-DIMOB.
f GIs are defined based on codon usage (removing regions like ribosomal operons) as predicted with SIGI-HMM. See text regarding the complementarity of the
IslandPath-DIMOB and SIGI-HMM methods.
g Pathogen-associated VFs have homologs only in other pathogen genomes, at the similarity cut-off used (see Materials and Methods).
h ‘‘Common’’ VFs have homologs in both pathogens and non-pathogens (e.g. certain iron uptake systems, etc.) at the similarity cutoff used (see Materials and Methods).
doi:10.1371/journal.pone.0008094.t001
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Table 2. Classification of virulence factors (VFs) in Genomic Islands (GIs) and non–GIs.
VFDB Classification
a
VFs in GIs
b
(#)
Proportion of
genes in GIs
c
(%) VFs in non-GIs (#)
Proportion of
genes in non-GIs
d
(%) p-value
e
Unclassified
g,h
(NA) 162 1.49 116 0.14 1.31E-75*
Type IV secretion system
f
(O) 51 0.47 24 0.03 1.03E-28*
Type III secretion system
e,h
(O) 97 0.89 154 0.19 1.12E-26*
Adherence
g,h
(O) 107 0.98 195 0.24 8.17E-26*
Iron uptake (NS) 31 0.28 60 0.07 1.81E-07*
Intracellular survival
g,h
(NS) 8 0.07 4 0.00 8.15E-05*
Toxin
g.h
(O) 25 0.23 63 0.08 1.14E-04*
Capsule
g
(D) 4 0.04 0 0.00 1.00E-03*
Protease
g
(D) 5 0.05 4 0.00 8.77E-03*
Antiphagocytosis (D) 18 0.17 67 0.08 3.89E-02*
Immune evasion
h
(D) 3 0.03 8 0.01 4.99E-01
Actin-based motility (O) 1 0.01 1 0.00 7.75E-01
Secretion system (other) (NS) 16 0.15 98 0.12 8.22E-01
Invasion (O) 2 0.02 7 0.01 8.22E-01
IgA1 Protease (D) 1 0.01 2 0.00 8.22E-01
Magnesium uptake (NS) 1 0.01 2 0.00 8.22E-01
Motility (NS) 7 0.06 67 0.08 1.00E+00
Exoenzyme (NS) 2 0.02 31 0.04 1.00E+00
Endotoxin (NS) 3 0.03 29 0.04 1.00E+00
Regulation (R) 3 0.03 26 0.03 1.00E+00
Type II secretion system (NS) 0 0.00 22 0.03 1.00E+00
Stress protein (D) 1 0.01 11 0.01 1.00E+00
Cellular metabolism (D) 0 0.00 8 0.01 1.00E+00
Enzyme (NS) 0 0.00 8 0.01 1.00E+00
Cell wall (NS) 1 0.01 6 0.01 1.00E+00
Biofilm formation (D) 0 0.00 4 0.00 1.00E+00
Molecular mimicry (D) 0 0.00 4 0.00 1.00E+00
Intracellular growth (NS) 0 0.00 3 0.00 1.00E+00
Plasminogen activator (O) 0 0.00 3 0.00 1.00E+00
Serum resistance (D) 0 0.00 3 0.00 1.00E+00
Biosurfactant (NS) 0 0.00 2 0.00 1.00E+00
Pigment (O) 0 0.00 2 0.00 1.00E+00
Proinflammatory effect (NS) 0 0.00 2 0.00 1.00E+00
Anti-proteolysis (D) 0 0.00 1 0.00 1.00E+00
Bile resistance (D) 0 0.00 1 0.00 1.00E+00
Complement Protease (D) 0 0.00 1 0.00 1.00E+00
Complement resistance (D) 0 0.00 1 0.00 1.00E+00
Heat-shock protein (NS) 0 0.00 1 0.00 1.00E+00
Manganese uptake (NS) 0 0.00 1 0.00 1.00E+00
Nutrient acquisition (NS) 0 0.00 1 0.00 1.00E+00
Peptidase (D) 0 0.00 1 0.00 1.00E+00
Resistance to antimicrobial peptides (D) 0 0.00 1 0.00 1.00E+00
TOTALS 549 1045
a VFs are defined as those genes curated as being VFs according to the VFDB. Only those VFs in the VFDB where GI predictions were available from IslandPath were
included in the analysis. VFs are also categorized, according to the VFDB, as O = Offensive; D = Defensive; NS = Nonspecific; R = Regulation; NA = Not Available.
b Number of VFs in GIs predicted with IslandPath-DINUC (more sensitive method).
c Proportion of genes in GIs that are VFs.
d Proportion of genes in non-GIs that are VFs.
e Fisher’s exact test with Benjamini-Hochberg correction for multiple testing. Asterisks indicate statistical significance (p-value,0.05).
e Includes Type III secretion system genes and Type III translocated proteins.
f Includes Type IV secretion system genes and Type IV secretory proteins.
g Categories of VFs that were also statistically significant with the IslandPath-DIMOB dataset.
h Categories of VFs that were also statistically significant with the IslandPath-SIGI-HMM dataset.
doi:10.1371/journal.pone.0008094.t002
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them that the stringent DIMOB-based method detects, and that
both IslandPath-DIMOB and SIGI-HMM, though more specific,
have sensitivity in the ,35% range [18]. Proteases and proteins
involved in adherence, iron uptake, intracellular survival, capsule
formation and antiphagocytosis were also preferentially associated
with GIs (Table 2). The significant enrichment of ‘‘Unclassified’’
genes in GIs reconfirmed previous observations of a proposed
large, novel gene pool that is associated with GIs [17].
We used the VFDB division of virulence factors among four
categories – ‘‘offensive’’, ‘‘defensive’’, ‘‘nonspecific’’, and ‘‘regula-
tory’’ – to quantitatively test our hypothesis that virulence factors
in GIs play more offensive roles. It is notable that regardless of the
GI detection method used, virulence factors classified as offensive
by the VFDB (i.e. involved in active invasion of the host or that
directly cause damage to the host) were very significantly
associated with GIs (p = 3.5E-37; using IslandPath-DINUC). In
contrast, defensive virulence factors (i.e. involved in passive
defense/evasion of the host) were associated with GIs to a much
lesser degree using the IslandPath-DINUC data set (p = 0.04), and
the association was not statistically significant using the DIMOB
and SIGI-HMM data sets (Table 3). Nonspecific virulence factors
(i.e. neither offensive nor defensive, or both depending on context)
were also significantly associated with GIs. Notably, no functional
classes of virulence factors, according to the VFDB classification
system, were more prevalent outside of GIs at a statistically
significant level.
Both pathogen-associated virulence factors and
‘‘common’’ virulence factors (having homologs in both
pathogens and non-pathogens) are associated with GIs
We hypothesized that virulence factors found predominately in
pathogens are more directly involved in pathogenicity (i.e. directly
cause damage to the host and/or are sufficient to cause disease),
and that, in contrast, virulence factors with identifiable homologs
in both pathogens and non-pathogens are more likely to facilitate
host interactions. To examine this further, we performed a
sequence similarity search against 631 completely sequenced
bacterial genomes for 298 pathogens and 333 non-pathogens to
classify each virulence factor in VFDB as ‘‘pathogen-associated’’ –
having homologs only in pathogens – or ‘‘common’’ – having
homologs in both pathogens and non-pathogens (see Materials
and Methods). Of 2285 virulence factors, 515 (23%) were
pathogen-associated and 1770 (77%) were ‘‘common’’.
We investigated the relationship between GIs and pathogen-
associated virulence factors by quantifying the proportion of
pathogen-associated virulence factors and ‘‘common’’ virulence
factors in GIs relative to all genes in GIs. This analysis was
performed using only those virulence factors from organisms with
completely sequenced genomes. Regardless of the GI prediction
criteria used (IslandPath-DINUC, IslandPath-DIMOB or SIGI-
HMM), both pathogen-associated and ‘‘common’’ virulence
factors were present in higher proportions in GIs than outside of
GIs for the pathogens examined (Table 1). While pathogen-
associated virulence factors might be expected to be associated
with GIs, since many are new genes that have been recently
acquired, it is notable that ‘‘common’’ virulence factors with
potentially older evolutionary origins have also been retained in
GIs across multiple genera.
Our classification of virulence factors as pathogen-associated or
‘‘common’’ allowed for some notable observations regarding
common mechanisms of virulence. Analysis of VFDB functional
classes indicated that pathogen-associated virulence factors are
disproportionately toxins or involved in type III and type IV
secretion systems. It is noteworthy that some classes of toxins were
restricted to pathogens yet were present in multiple diverse genera.
Some examples include toxins with adenylate cyclase activity
(anthrax toxin edema factor from Bacillus anthracis and exoenzyme
Y from Pseudomonas aeruginosa, which can be found in four genera)
and toxins with ADP-ribosyltransferase activity (pertussis toxin,
cholera toxin, and P. aeruginosa exoenzyme S and exoenzyme T, all
present in four or more genera). In contrast, ‘‘common’’ virulence
factors were involved in motility, antiphagocytosis, iron uptake,
endotoxin, and type II secretion system functions (see Table 4 for a
listing of the subset of categories that were statistically significant;
Table S1 for a complete list of categories). Overall, pathogen-
associated virulence factors were significantly disproportionately
classified as offensive by VFDB (p = 1.77E-22), while ‘‘common’’
virulence factors tended to have defensive or nonspecific functions
(p = 2.07E-08 and p = 7.88E-07, respectively).
In an extended analysis examining all genes in the available 631
bacterial genomes, we determined that 14% of genes in pathogen
genomes were pathogen-associated, and 19% of genes in non-
pathogen genomes had homologs exclusively in non-pathogens
using our criteria (see Materials and Methods). Both pathogen-
associated and non-pathogen-associated genes occurred signifi-
cantly more frequently in GIs than non-GIs (p<0 for both,
regardless of which prediction criteria were used). This supports
our previously published observation that species or family-specific
genes tend to be more commonly found in GI regions because of a
probable larger gene pool associated with such mobile elements
[17]. It should not distract, however, from our earlier observation
that virulence factors in general were clearly disproportionately
associated with GIs, including those with homologs in non-
pathogens.
Collectively, these results confirm previous anecdotal reports
that virulence factors are more common in GIs, supporting the
important role of GIs in pathogen evolution. The results also
Table 3. Virulence factors (VFs) in genomic islands (GIs) play more ‘‘offensive’’ roles.
VF Type
Proportion
of genes in
DINUC GIs
(%)
Proportion of
genes in non-
DINUC regions
(%) p-value
a
Proportion
of genes in
DIMOB GIs
(%)
Proportion of
genes in non-
DIMOB regions
(%) p-value
a
Proportion of
genes in
SIGI-HMM GIs
(%)
Proportion of
genes in non-
SIGI-HMM
regions (%) p-value
a
Offensive 2.53 0.97 3.50E-37 1.64 1.15 4.98E-03 5.51 1.01 6.49E-144
Defensive 0.26 0.16 3.69E-02 0.18 0.17 8.52E-01 0.22 0.17 3.17E-01
Nonspecific 0.96 0.34 1.60E-16 0.57 0.41 1.16E-01 1.26 0.40 2.81E-19
Regulation 0.03 0.04 7.93E-01 0.00 0.04 1.70E+01 0.05 0.04 5.45E-01
a Fisher’s exact test.
doi:10.1371/journal.pone.0008094.t003
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further suggest that offensive and pathogen-associated (i.e.
potentially more virulence-specific) virulence factors are more
likely to have been recently acquired (in the time scale detected by
GI analysis), versus those involved in more defensive or passive
host-association functions.
CRISPRs are associated with GIs
Clustered regularly interspaced short palindromic repeats
(CRISPRs) are genetic elements that have been identified in
approximately 40% and 90% of Bacteria and Archaea genomes,
respectively [31]. A CRISPR consists of several identical repeats,
separated by non-identical spacer sequences [32]. These repeat
and spacer sequences typically range in size from 25–40 base pairs
long, while the number of repeats in a single CRISPR varies
widely from 2 to 250 [31]. Recent research has shown that these
elements, along with CRISPR associated (CAS) genes, are
involved in a silencing mechanism that can provide protection
against phage and possibly other mobile elements [33,34].
Furthermore, the phylogenetic profiles of CAS genes suggest that
CRISPR systems could be primarily transferred by horizontal
gene transfer [35–37].
Although CRISPRs have been identified on ten megaplasmids
[35] and within two prophages in Clostridium difficile [38], a large
scale analysis of CRISPRs and GIs has not been conducted. To
evaluate if an association exists, we obtained 1043 predicted
CRISPRs for 355 species from the CRISPRdb (http://crispr.u-
psud.fr/crispr/) [31]. In our analysis, CRISPRs were found to be
over-represented within GIs predicted by all three methods, with
approximately twice as many CRISPRs located in GIs than
expected (Table 5). Furthermore, the over-representation of
CRISPRs within GIs was statistically significant using chi-squared
analysis.
Given the relationship between phage and CRISPRs, we
investigated the contribution of phage genes to GIs in a separate
study examining all GIs predicted by IslandPick (a comparative
genomics-based GI prediction method [18,29]), SIGI-HMM or
IslandPath-DIMOB (the two most accurate sequence composition-
based methods which could be more widely applied to genomes
since they do not require comparative genomes). The frequency of
genes in GIs with the word ‘‘phage’’ occurring in the annotation
was enumerated (henceforth referred to as ‘‘phage genes’’) and
compared to the number of phage genes outside of GIs. GIs in
prokaryotic genomes were significantly enriched for genes with a
phage annotation (6990 observed; 1264 expected; p<0), support-
ing the idea that a large number of GIs are prophage regions. GIs
that contained CRISPRs also showed over-representation of
phage genes (p = 5.7E-05).
Sampling bias in the currently available dataset of
bacterial genomes is not a major contributing factor to
our observations
One potential source of bias with the functional category
analysis is that the taxonomical distribution of the genomes
sequenced to date is uneven. In particular, some pathogens are
over-represented by multiple strains while certain, predominately
non-pathogenic, taxa are sparsely represented. To reduce
redundancy and bias in the whole genome dataset, we selected a
subset of pathogen and non-pathogen genomes with a minimum
evolutionary distance (substitutions/site) of 0.05 (adapted from a
recent phylogenetic analysis [39]). This essentially reduced the
Table 4. Statistically significant categories of virulence factors (VFs) that are Pathogen-associated or ‘‘Common’’ to both
pathogens and non-pathogens.
VFDB Classification
a
Pathogen-associated
b
(%) ‘‘Common’’
c
(%) p-value
d
Categories with a higher percentage of Pathogen-associated VFs
Toxin (O) 79 (15.28) 58 (3.27) 1.84E-18*
Type III secretion system (O) 117 (22.63) 175 (9.87) 1.02E-11*
Type IV secretion system (O) 32 (6.19) 51 (2.88) 4.77E-03*
Categories with a higher percentage of ‘‘Common’’ VFs
Motility (NA) 0 (0) 75 (4.23) 9.95E-08*
Antiphagocytosis (D) 6 (1.16) 105 (5.92) 1.13E-05*
Iron uptake (NS) 5 (0.97) 92 (5.19) 2.51E-05*
Endotoxin (NS) 0 (0) 32 (1.80) 2.98E-03*
Type II secretion system (NS) 0 (0) 22 (1.24) 4.24E-02*
a VFs are defined as those genes curated as being VFs according to the VFDB. VFs are also categorized, according to the VFDB, as O = Offensive; D = Defensive;
NS = Nonspecific; R = Regulation; NA = Not Available.
b Pathogen-associated VFs have homologs only in other pathogen genomes, at the similarity cut-off used (see Materials and Methods).
c ‘‘Common’’ VFs have homologs in both pathogens and non-pathogens (e.g. certain iron uptake systems, etc.) at the similarity cutoff used (see Materials and Methods).
d Fisher’s exact test. Only those categories with statistical significance (p-value,0.05) are listed.
doi:10.1371/journal.pone.0008094.t004
Table 5. Over-representation of CRISPRs in GIs.
IslandPath-
DINUC
IslandPath-
DIMOB SIGI-HMM
Number of bacterial genomes
a
245 237 213
Number of GIs 23889 6158 7529
Proportion of genome in GIs (%) 11.0 4.2 3.1
Total number of CRISPRs 684 661 607
Expected number of CRISPRs in GIs 75 28 19
Observed number of CRISPRs in GIs 145 66 43
p-value
b
1.4E-17 6.5E-14 1.4E-08
a Number of bacterial genomes for which both CRISPRs and GIs could be
predicted.
b Chi-squared test includes number of observed and expected CRISPRs outside
of islands (data not shown).
doi:10.1371/journal.pone.0008094.t005
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number of pathogen genomes that were highly similar (e.g.
multiple strains of a pathogen) and thus reduced sampling bias.
When this less-biased genome dataset was analyzed again using
the same classification schemes and methods as described above,
no significant differences in results were observed (data not shown),
with still highly significant p-values for our statistics, indicating that
the sampling bias in sequenced genomes is not a major
contributing factor to our observations.
Discussion
Collectively, our data provide large scale, quantitative mea-
surements regarding trends in virulence across multiple genera
that are either newly identified or have been previously stated for
selected pathogens. In our study, we confirm previous studies of
closely related species reporting that virulence factors are
commonly found in GIs, and we find that these virulence factors
are disproportionately involved in more offensive versus defensive
functions. The association of virulence factors with GIs holds true
regardless of whether we use the more sensitive IslandPath-
DINUC method for GI prediction, or more specific methods, such
as IslandPath-DIMOB and SIGI-HMM. It should be emphasized
that the methods used will not detect some GIs – such as those that
have been acquired from genomes with similar sequence
composition, or more ancient GI acquisition events that may
have ameliorated to the host genome sequence composition over
time. Therefore, these GI prediction methods will tend to under-
predict GIs. However, even with this under-prediction, we notably
never observe a statistically significant association of virulence
factors with regions outside of GIs for any virulence factor class.
These results also hold true for GIs defined using whole-genome
comparative methods. We analyzed an alternate set of GIs defined
by Vernikos and Parkhill (2008) as genomic regions with limited
phylogenetic distribution consistent with recent acquisition [40],
and found that there are indeed more virulence factors in this set
of GIs compared to outside of such GIs (p = 9.5E-160) (See Text
S1 and Table S2). These data strongly support the important role
of GIs in pathogen evolution.
We used the set of virulence factors in the VFDB. This is a well-
established, published set based on experimentally demonstrated
virulence factors extracted from the literature and supplemented
with comparative genomics. Prior to performing this study, we
evaluated several virulence factor repositories, including the
PRINTS database (http://www.jenner.ac.uk/BacBix3/PPprints.
htm), the Toxin and Virulence Factor Database (TVFac) at Los
Alamos National Laboratory, MvirDB (http://mvirdb.llnl.gov)
[41], and looked also at COG [42] classifications for virulence
factors. VFDB was found to be the most comprehensive and had
the highest quality with its curated dataset and virulence-guided
classification system. To further verify our results using an
independently derived set of virulence factors, we examined
virulence proteins from Swiss-Prot [43] and found their association
with genomic islands to also be significant (p,8.4E-04 for all GI
prediction methods) (See Text S1 and Table S3). Using the VFDB
classification scheme, we note that pathogen-associated virulence
factors have more offensive functional roles compared to
‘‘common’’ virulence factors, which are likely to be so-called
‘‘host interaction factors’’ with nonspecific or defensive functions.
In particular, type III and type IV secretion components and
toxins tend to be over-represented in GIs. The type III secretion
system is a well-studied virulence mechanism; type IV secretion
systems are implicated in conjugation of DNA as well as the
delivery of effector molecules to host cells [44], once again
highlighting the contextual nature of virulence. These observations
suggest that pathogenicity, as opposed to host interaction, is more
often a recently developed phenomenon in a species (on an
evolutionary time scale detected by GI analysis).
GIs appear to provide a critical flexible mechanism that allows a
bacterium to adapt and develop increased, invasive infection in the
host. Several evolutionary models have been proposed to explain
how virulence factors are maintained and these models are
consistent with the importance of GIs (and related phage) in
pathogen evolution. Smith [45] proposed that in a pathogen
population, there are a small number of ‘‘cheaters’’ that
themselves do not possess certain extracellularly-acting virulence
factors but benefit from the effect of these virulence factors
released by the non-cheater strains. Without the virulence factors,
the cheater strains are metabolically more fit than the non-
cheaters, and therefore their number would increase in the
population over time. However cheaters, due to their lack of
virulence factors, have decreased infectiousness and Smith
proposed that horizontal gene transfer was a possible mechanism
to minimize ‘‘cheater’’ strains and restore infectiousness in the
pathogen population. As a result, certain virulence factors are
maintained on mobile elements, including GIs, which are thought
to be related to phage [17]. It is worth noting that PSORTb
analysis of subcellular localization [46] indicates that predicted
extracellular proteins are over-represented in pathogen-associated
genes for Gram-positives (there is better prediction of extracellular
factors for Gram-positives versus Gram-negatives) (data not
shown). In a second proposed model, Sokurenko and colleagues
adopted the classical source-sink model of population genetics to
describe virulence evolution [47]. For opportunistic pathogens, the
environmental reservoir represents a self-sustainable source
whereas the opportunistic infection represents a venture into a
sink. They proposed that acquisition of PAIs as a mechanism to
facilitate adaptation of the source to sink transition whereas the
loss of PAIs accompanies the sink to source transition. However,
since possessing a PAI can significantly increase the pathogen’s
fitness in the sink, which in turn increases the back flow of PAI-
possessing strains into the source population, virulence factors in
PAIs can be maintained despite the negative fitness value of
virulence factors in the environment. It is notable that, in our
study, many of the over-represented virulence factors in GIs are
involved in active invasion that harm the host in some way and
there is no obvious functionality for these virulence factors outside
of the host environment. Therefore, maintaining these virulence
factors on GIs, and likely other mobile elements like phages and
plasmids, appears to provide important evolutionary flexibility for
these pathogens.
Our investigation of putative pathogen-associated virulence
factors reveals several universal strategies adopted by pathogens to
gain access to and colonize privileged sites in hosts, i.e. the use of
toxins and host contact-dependent secretion mechanisms. These
strategies appear to be relatively absent in non-pathogenic strains
which also typically do not elicit a strong inflammatory response
[48]. The recent publication of the Hamiltonella defensa genome
highlights the complexity inherent in virulence [49]. H. defensa is an
endosymbiont that protects its aphid host from attack by parasitoid
wasps. Numerous homologs of known virulence factors, including
genes for toxins, effector proteins and two type III secretion system
components are present in its genome, resulting in speculation that
the encoded virulence factors play a role in symbiosis. However, it
should be noted that the type III secretion system did not appear
to be active based on proteomics analysis of intact H. defensa cells
from whole insects – only one of the type III secretion proteins
were recovered and none of the effectors were expressed [49]. We
therefore argue that H. defensa’s type III secretion system may be
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required for its role as a wasp pathogen rather than having new
roles related to symbiosis.
Several inactivated toxins (toxoids) such as pertussis toxin and
diphtheria toxin have been used successfully as vaccines against
the associated pathogen [50]. Epidemiological evidence has
indicated that these vaccines, which induce antitoxin immunity,
resulted in a significant reduction in the virulent form of these
pathogens [51,52]. These results, combined with the observation
that many of these virulence factors are not part of the core
pathogen genomes, suggest that if we put selection pressure on
virulent specific antigens, we may be able to effectively reduce the
number of pathogens carrying these genes, and hence provide
selection for pathogens to evolve into less virulent forms. Our
study supports observations that several virulence factors used in
successful vaccinations are indeed specific to, or associated with,
pathogens. Additional review of the pathogen-associated virulence
factors we have identified in this analysis shows that some are
protective either on their own or in combination with other
pathogen-associated virulence factors in an animal model of
infection. However, not all have been tested and clearly it would
be prudent to examine the efficacy of other strongly pathogen-
associated genes that have not yet been investigated for their
effectiveness in vaccines. Antigens that are common to both
pathogens and commensals may, on average, be less likely to elicit
strong immunogenic responses. Our study provides lists of
pathogen-associated genes that may encode good candidates for
vaccine development or anti-virulence drug development [53,54].
In addition, we provide supporting evidence that CRISPRs are
over-represented within GIs and therefore, are likely being
horizontally transferred. Several studies have shown that
CRISPRs can derive from both viruses and plasmids [34,55].
We provide evidence indicating that some GIs containing
CRISPRs are likely to be prophages, lending further support to
observations that phages can carry CRISPR sequences within
their genomes. CRISPRs have been proposed to be beneficial to
bacteria, facilitating defense against viral infections. However, this
work indicates that phage more directly may be hosting these
repeat elements, to help avoid additional phage entering a genome
that already hosts a given prophage. Most of this data is
speculative, but it does suggest that understanding the association
of CRISPRs with islands and phages is important, given the
association of GIs with virulence and other microbial adaptations
of medical and industrial importance.
It should be noted that our study has several limitations. We are
significantly limited by the number and diversity of genome
sequences and known virulence factors currently available.
However, we felt that the diversity of species whose genome
sequences were available was now sufficient to provide an early
sense of the degree to which certain genes were pathogen-
associated since multiple well studied pathogens, with closely
related non-pathogenic relatives, had complete genomes available
from diverse phyla. We also repeated our analyses involving
hundreds of genomes, taking into account the phylogenetic
distance between species to reduce the redundancy of the genomes
dataset in order to reduce potential sampling biases. We obtained
similar results, with the same statistically significant observations,
with this pared down dataset. Regardless, clearly this analysis, or a
similar type of analysis, bears repeating as the number of genome
sequences available increases. Future analyses will need to
increasingly account for non-pathogens that may have recently
evolved from pathogens and that may still contain remnants of
virulence factors. Also, there is some uncertainty regarding
whether a given organism (such as a novel, poorly understood
marine microbe) is a pathogen or not, since any host interactions
may be unknown. The contextual nature of pathogenicity (for
example, how an organism can be a pathogen in one species and
not in another) complicates analysis and will need to be further
considered. Of course, exceptions to these trends will also always
be found. However, by examining many diverse species as a
group, we do somewhat overcome uncertainty or pathogen
classification errors in a few cases by the sheer numbers of
organisms we analyzed and the diversity of phyla examined. Our
investigations were also limited by the cutoffs used in the analysis
of similarity between sequences. We chose cutoffs for similarity
that did not produce a notably different result from cutoffs slightly
above or below it (data not shown). However, any hard cutoff is
not perfect and so we encourage, and are performing ourselves,
further manual inspection of results for a given gene identified as
pathogen-associated before pursuing further in depth analysis of
the gene of interest. It should also be taken into consideration that
some proteins, such as type III secretion system effectors, may
appear to be more pathogen-associated simply because there are
less constraints on their sequence and they have diverged in
sequence more rapidly. Finally, we also investigated the utility of
different gene function classification systems in this analysis, like
COG, SUPERFAMILY, PRINTS, and the VFDB. It became
clear over the course of this study that general classification
systems such as COG do not perform well in detecting trends in
virulence since the classification system does not include most
virulence factors at all and does not have virulence-associated
categories. The VFDB, with its curated dataset and virulence-
guided classification system, was found to be the most effective.
However, there are still some VFDB classifications that could
benefit from more curation – for example the Type III secretion
system component classification could be improved further. Even
though virulence is a complex phenomenon, more effort should be
made to build upon such efforts and develop a high quality
ontology that is relevant to virulence, to complement other
ontology efforts.
Even with all of the limitations in our analysis described above,
the criteria we used clearly identify genes and gene categories that
have a notable pathogen association. Genes that are present in
multiple pathogens of different genera, but not present in non-
pathogens of these same genera, are certainly worthy of being
described as being pathogen-associated. Such genes warrant
further study as part of the efforts to develop more anti-infective
therapies and vaccines, as well as for their role in virulence in
general. Of particular interest are the many conserved hypothet-
ical genes shared across multiple diverse genera that were
identified as pathogen-associated. Our analyses of GIs likewise
provide strong evidence that these genomic regions can, on
average, play a critical role in virulence. Further examination of
the origins of such pathogen-specific genes and GIs, their
relationship to phage, and how their products integrate with the
existing cellular network, could provide enlightening insight into
global trends in the evolution of virulence.
In conclusion, our analyses of a curated dataset of virulence
factors and pathogen-associated genes suggest that such genes are,
on average, more associated with GIs versus non-GI regions. Our
collective results also further suggest that offensive and virulence-
specific virulence factors in bacterial pathogens are more likely to
be associated with GIs, versus virulence factors with homologs in
non-pathogens that tend to be involved in more passive host-
association functions. Though there are certain bacteria (such as
obligate intracellular pathogens) that are exceptions, this work
clearly demonstrates the strong role of GIs in the evolution of
virulence and provides the first systematic analysis of this trend
across diverse genera. We provide evidence that certain types of
Genomic Islands and Virulence
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virulence factors, such as components of host contact-dependent
secretion systems and certain classes of toxins, are quite selectively
pathogen-associated. Additionally, we provide whole genome
datasets of pathogen-associated genes in a set of completely
sequenced bacterial genomes. Such pathogen-associated genes, in
particular those found in diverse pathogens but not in non-
pathogens in the same genera, warrant further study for their
potential role in virulence, as well as for their potential as anti-
infective drug targets or vaccine components.
Materials and Methods
Virulence factors and pathogen-associated genes
A dataset of 2293 virulence factors (from 37 pathogenic
bacterial species) was obtained from the VFDB (http://www.
mgc.ac.cn/VFs/) [19,20] in March 2008. Each virulence factor
from the VFDB dataset was identified as pathogen-associated
(found predominately in pathogens), or ‘‘common’’ (found in both
pathogens and non-pathogens) through a BLAST similarity search
against the deduced proteomes of 298 pathogenic and 333 non-
pathogenic sequenced prokaryotic genomes obtained from the
National Center for Biotechnology Information (NCBI) FTP site
in March 2008. Both chromosome and plasmid replicon types
were included in the analysis. An E-value cutoff of 10
-7
was
selected to exclude distant homologs. In an initial investigation we
examined more and less stringent cutoffs of 10
212
and 10
25
,
respectively, and found that the vast majority of trends analyzed
still hold when these other cutoffs were used. Pathogen, non-
pathogen, and host-associated status for each genome were
initially obtained from the summary page of ‘‘Complete Microbial
Genomes’’ at NCBI (http://www.ncbi.nlm.nih.gov/genomes/
lproks.cgi) [56] and then manual curation for data quality and
overall completeness was performed on this dataset. We also
identified pathogen-associated, ‘‘common’’, and non-pathogen-
associated (genes found predominately in non-pathogens) genes for
each gene in the sequenced genomes in a similar manner as
described above. These data sets are available for download
(http://www.pathogenomics.sfu.ca/pathogen-associated/). Addi-
tionally, to reduce redundancy and bias in this whole genome
dataset (multiple genome sequences from a particular genera or
species), we repeated the analysis using a subset of genomes with a
minimum evolutionary distance (substitutions/site) of 0.05 (based
on a recent phylogenetic analysis [39]).
Virulence factors and pathogen-associated genes in GIs
To quantify the number of virulence factors in GIs, we used a
subset of virulence factors from the VFDB (described above) for
which fully-sequenced genomes are available. A total of 1565
virulence factors were used from 28 different genomes. We
quantified the occurrence of virulence factors in GIs, where GIs
were defined as either 1) IslandPath-DINUC: a region consisting
of 8 or more ORFs with dinucleotide bias (a more sensitive
method for GI detection), or 2) IslandPath-DIMOB: a region of 8
or more ORFs with dinucleotide bias plus the presence of one or
more mobility genes (a more specific method of GI detection [17]),
or 3) SIGI-HMM: DNA regions showing atypical codon usage
based on HMM analysis [30]. The IslandPath software application
[28] was subjected to slight modifications to improve predictive
accuracy [18], and GI predictions are available for IslandPath-
DIMOB and SIGI-HMM methods through IslandViewer [29].
Predictions were made for chromosomal and plasmid DNA. Note
that there are many more genes in general outside of GIs than in
GIs for any genome, so we normalized the proportions of
virulence factor genes inside or outside islands as a function of the
total number of genes inside and outside of such GI regions. The
occurrence of pathogen-associated genes in GIs for sequenced
prokaryotic genomes were counted in a similar manner as
described in the above section, again examining the proportions
of such genes as a function of the total number of genes in GI or
non-GI regions.
Characterization of features and functional classes of
virulence factors and pathogen-associated genes
The VFDB uses keywords describing virulence-related functions
to assign virulence factors to one of four broad classes: ‘‘offensive’’,
‘‘defensive’’, ‘‘regulation’’ and ‘‘nonspecific’’. We adapted and
curated the VFDB classification, assigning unclassified keywords
(and their related virulence factors) to one of the four classes, and
reclassifying virulence factors in cases where we disagreed with the
current annotation. For example, in the VFDB, lipopolysaccharide
(LPS) from Pseudomonas aeruginosa is mapped to the terms
‘‘endotoxin’’ and ‘‘adherence’’. Since ‘‘adherence’’ is considered
an ‘‘offensive’’ keyword, LPS is listed as an ‘‘offensive’’ virulence
factor. We reclassified LPS to be nonspecific in keeping with its
general function. The revised classification is available in Table 2.
Statistical analyses
Statistics for over-representation of virulence factors in GIs were
performed by tabulating the number of virulence factors in GIs,
total number of genes in GIs, number of virulence factors outside
of GIs, and total number of genes outside of GIs in a 262
contingency table, and then using the Fisher’s Exact Test. Similar
statistical analysis was done for functional classification of genes in
islands, where the number of genes in each VFDB category was
used in the calculation. Over- or under-representation of VFDB
functional classifications of pathogen-associated and ‘‘common’’
virulence factors was done by comparing the number of pathogen-
associated genes in a given category against ‘‘common’’ genes in
the same category. Since multiple categories are examined
simultaneously, the Benjamini and Hochberg False Discovery
Rate correction for multiple testing was performed for all
functional category analyses. We considered p-values smaller than
0.05 to be significant. All statistics were calculated using the R
statistics package.
Over-representation of CRISPRs within GIs
Predicted CRISPRs were obtained from the CRISPRdb
(http://crispr.u-psud.fr/crispr/) [31]. This database contained
1043 CRISPRs for 355 species (306 Bacteria and 49 Archaea).
The coordinates of bacterial CRISPRs were searched among GIs
in species for which GIs could be predicted by IslandPath-
DINUC, IslandPath-DIMOB, or SIGI-HMM. We tabulated the
number of CRISPRs in GIs and compared it to the expected
number based on genome size and CRISPR frequency. To
approximate the contribution of phage to GIs, the frequency of
genes in GIs with ‘‘phage’’ occurring in the annotation (referred to
as ‘‘phage genes’’) was calculated and compared to the frequency
of phage genes outside of GIs. We further extended this analysis to
determine the over-representation of phage genes in GIs
containing CRISPRs compared to non-GI regions. The signifi-
cance of over-representation was determined using a chi-squared
test.
Supporting Information
Figure S1 Venn diagram showing the overlap of virulence
factors in GIs predicted using three methods: IslandPath-DINUC,
IslandPath-DIMOB, and SIGI-HMM.
Genomic Islands and Virulence
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Found at: doi:10.1371/journal.pone.0008094.s001 (0.22 MB TIF)
Figure S2 Proportion of genes (%) that are virulence factors
(VFs) inside versus outside of (A) IslandPath-DINUC, (B)
IslandPath-DIMOB, and (C) SIGI-HMM GIs. Pathogens having
GI predictions are grouped by genus.
Found at: doi:10.1371/journal.pone.0008094.s002 (1.22 MB TIF)
Table S1 Complete list of VFDB functional classifications of
pathogen-associated and ‘‘common’’ virulence factors from the
VFDB. Only statistically significant categories are shown in
Table 3.
Found at: doi:10.1371/journal.pone.0008094.s003 (0.07 MB
DOC)
Table S2 Analysis of VFDB virulence factors in a set of GIs
derived from whole-genome comparisons by Vernikos and Park-
hill (2008).
Found at: doi:10.1371/journal.pone.0008094.s004 (0.06 MB
DOC)
Table S3 Enrichment of Swiss-Prot-derived virulence proteins in
GIs.
Found at: doi:10.1371/journal.pone.0008094.s005 (0.03 MB
DOC)
Text S1 Supplemental methods.
Found at: doi:10.1371/journal.pone.0008094.s006 (0.03 MB
DOC)
Acknowledgments
We thank Chris Stubben and Jian Song from the Los Alamos National
Laboratory, Los Alamos, NM, USA, for providing virulence factor
datasets; and we also thank our colleague Raymond Lo from Simon
Fraser University for critical reading of the manuscript.
Author Contributions
Conceived and designed the experiments: SJHS AF WWLH MGIL FSLB.
Performed the experiments: SJHS AF. Analyzed the data: SJHS AF.
Contributed reagents/materials/analysis tools: WWLH MGIL. Wrote the
paper: SJHS AF FSLB.
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