REVIEW ARTICLE
Lateral genetic transfer and the construction of genetic exchange
communities
Elizabeth Skippington & Mark A. Ragan
ARC Centre of Excellence in Bioinformatics, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Qld, Australia
Correspondence: Mark A. Ragan, ARC
Centre of Excellence in Bioinformatics,
Institute for Molecular Bioscience, The
University of Queensland, Brisbane, Qld,
Australia. Tel.: 161 7 3346 2616;
fax: 161 7 3346 2101;
e-mail: [email protected]
Received 25 August 2010; accepted 3
December 2010.
Final version published online 21 January 2011.
DOI:10.1111/j.1574-6976.2010.00261.x
MICROBIOLOGY REVIEWS
Editor: Fernando Baquero
Keywords
antibiotic resistance; lateral genetic transfer;
horizontal genetic transfer; genetic exchange
communities.
Abstract
Lateral genetic transfer (LGT) is a major source of phenotypic innovation among
bacteria. Determinants for antibiotic resistance and other adaptive traits can
spread rapidly, particularly by conjugative plasmids, but also phages and natural
transformation. Each successive step from the uptake of foreign DNA, its genetic
recombination and regulatory integration, to its establishment in the host
population presents differential barriers and opportunities. The emergence of
successive multidrug-resistant strains of Staphylococcus aureus illustrates the
ongoing role of LGT in the combinatorial assembly of pathogens. The dynamic
interplay among hosts, vectors, DNA elements, combinations of genetic determinants and environments constructs communities of genetic exchange. These
relations can be abstracted as a graph, within which an exchange community
might correspond to a path, transitively closed set, clique or near-clique. We
provide a set-based definition, and review the features of actual genetic exchange
communities (GECs), adopting first a knowledge-driven approach based on
literature, and then a synoptic data-centric bioinformatic approach. GECs are
diverse, but share some common features.
Introduction
It has long been known that phenotypic features can be
transmitted between unrelated strains of bacteria. This year
marks the one-hundredth anniversary of Schmitt’s report
that the human paratyphoid bacillus can take on the
agglutination properties of the calf paratyphoid bacillus
during in vivo passage through a calf (Schmitt, 1911).
Although this and other early experiments admit to other
possible explanations (Gurney-Dixon, 1919), Griffith (1928)
demonstrated that a nonvirulent strain of Streptococcus
pneumoniae could be rendered virulent by a heat-stable
substance from a virulent strain, and Avery et al. (1944)
identified this transforming substance as DNA. The ability
of bacteria to accept and express genetic material transmitted not only from parent to offspring (vertical transfer)
but also from sources external to the cellular lineage (lateral
or horizontal transfer) remains a cornerstone of experimental molecular genetics and biotechnology.
Lateral genetic transfer (LGT) is equally significant outside the laboratory. Since the mid-twentieth century, genetic
determinants specifying resistance to successive antimicroFEMS Microbiol Rev 35 (2011) 707–735
bial drugs have been spreading in strongly selective environments, notably among pathogenic bacteria in hospitals, but
also in the community and along the commercial food
chain, with obvious implications for public health. The
spread of antibiotic resistance has become the poster-child
of LGT, as it is well documented clinically, relatively well
understood at the molecular level, of undeniable societal
concern in developing as well as developed countries and
easily translated into headlines (superbugs).
Over the last decade, it has become increasingly apparent
that LGT is widespread among bacteria and drives metabolic
innovation well beyond the context of antibiotic resistance
(Ochman et al., 2000; Woese, 2000; Jain et al., 2003;
Nakamura et al., 2004). Strains within a species typically
share a set of core genes, but can differ substantially in their
inventories of variable genes, the presence or absence of
which is due principally to LGT and gene loss (Lerat et al.,
2005). Twenty sequenced strains of Escherichia coli, for
example, share a common core of about 1976 orthologous
genes, while each strain individually possesses, in addition, a
further 2092–3403 genes from a collective pool of some
15 862 distinct genes comprising the variable set. Altogether,
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the gene repertoire of E. coli – the species pan-genome –
totals some 17 838 genes (Touchon et al., 2009).
Although certain issues remain open (Ragan & Beiko,
2009), the succession of steps comprising a successful
instance of LGT is now adequately known. Exogenous
genetic material is presented to a cell as free DNA in the
environment, as a plasmid or similar element or packaged in
a phage. Once inside the cell, if the DNA survives cellular
defence mechanisms and becomes established either via
recombination into the main chromosome or on an extrachromosomal element, depending on the fortunes of its new
host, it may become abundant in the host-cell population.
Through mutations, its expression may be further regulated
and tuned, and the encoded protein(s) better integrated into
cellular networks. As a determinant is selected and spreads
in a population, other traits linked to it – other resistance
genes, for example, or virulence – become prevalent at the
same time. Each of these steps throws up differential
opportunities and differential barriers that, together with
environmental heterogeneity and situational contingencies,
dynamically construct the diverse microbial geneticexchange communities around us.
In this review, we consider the role of LGT in the spread
of antibiotic resistance. In the second section, we present a
graph-theoretic framework that allows us to define genetic
exchange communities (GECs) with flexibility, rigour and
precision. In the third section, we consider how genetic
material is transferred from one bacterial lineage to another,
calling attention to the differential opportunities and barriers presented by the mechanisms of DNA transmission,
recombination and system-level integration that together
give rise to the diversity of GECs. Finally, in the fourth
section, we survey some actual GECs, first adopting a
knowledge-based approach and then introducing data-driven methods based on large-scale sequence comparison and
bioinformatics.
What is a GEC?
Exchange communities
Although perhaps implicit across the literature on bacterial
population structure, recombination and LGT, so far as we
can determine, the term exchange community was first
introduced in the context of lateral transfer by Jain et al.
(2003), who defined it as ‘a collection of organisms that can
share genes by (LGT), but need not be in physical proximity’.
These investigators examined the association of eight types
of factors (temperature, oxygen level, pH, salinity, pressure,
genome size, G1C content, mode of carbon utilization)
with LGT as indicated by the patterns of topological
discordance among phylogenetic trees inferred for gene
families represented in eight ecologically and phyletically
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E. Skippington & M.A. Ragan
diverse prokaryotes. All factors were found to associate with
genetic exchange, with the ‘internal’ determinants genome
size and G1C content most strongly associated, followed by
mode of carbon utilization; external factors including pressure, pH, salinity and growth temperature were the least
strongly associated. As the strongly associated factors are
features of the microorganisms themselves and not of their
habitats (and indeed can vary widely within actual microbial
communities), Jain et al. (2003) concluded that exchange
communities are not limited by physical proximity; instead,
it is primarily internal parameters that ‘affect (LGT) and
thereby delineate exchange community boundaries’. Jain
and colleagues described these communities as potentially
extending across major phyletic and ecological barriers at a
complete ecosystem scale, some 1028 individuals.
Their formulation captures key concepts, notably that
exchange communities can extend across location, habitat
and taxon identities, and are actively constructed by LGT. It
bears further consideration, however, whether we should
restrict membership in exchange communities to cellular
organisms only, and whether and how to integrate vertical
inheritance. Further, as we demonstrate in the following
section, the Jain and colleagues formulation is operationally
ambiguous. We may need not a single definition, but rather
a framework for thinking precisely and operationally about
GECs, together with a series of settings appropriate for
different problems.
GECs: conceptual framework and parameters
Many problems in genetics and molecular bioscience can be
abstracted as graphs; trees (e.g. Darwin’s tree of life) and
networks are special cases of graphs. As discussed below
(GECs: data-driven approach), vertical and LGT, and the
description of exchange communities are well suited to
graphical treatment. Graphs contain nodes (vertices) connected by edges (arcs). Here, nodes depict entities that carry
and can potentially exchange genetic material, and edges
represent the transmission of genetic material between
them. If we could take a snapshot of a habitat (say, of a
specific biofilm or rumen) and abstract its gene-sharing
relationships as a graph, individual microorganisms might
be the nodes and the most recent instances of LGT the edges.
This formulation immediately raises several issues. It is
usually (although not always) impractical to observe microbial cells individually; more typically, we aggregate many
millions of individuals from a strain or a clone for study
(e.g. by genome sequencing), and our inferences about
vertical and lateral transmission integrate over a series of
temporal snapshots potentially corresponding to many
millions of generations (Fig. 1). For example, we infer that
a region of genetic material G has been transferred laterally
between strains A and B if both contain a copy of G so
FEMS Microbiol Rev 35 (2011) 707–735
709
Lateral genetic transfer and genetic exchange communities
Fig. 1. Representing exchange communities. Cellular or genomic
lineages (depicted here as tubes) evolve in time (here, left to right), with
genetic regions continually gained and lost (for clarity, not shown here).
The orthogonal planes represent successive temporal shapshots. Within
each plane, the intersections with lineages represent biological entities as
they existed at that point in time; thus, in the right-hand-most plane, A, B
and C represent groups of bacteria, and P and V types of plasmids or
phage, as they exist and are delineated today. A region of DNA 100%
identical in sampled members of A, B and P and inferred to be of lateral
origin is likely to have been transferred very recently, as represented by
solid lines. A region 80% identical in A and B (and/or C) might be inferred
to have been transferred at some point in the past (the dashed line in the
middle plane) into or between A 0 and B 0 , earlier biological groups that,
especially due to LGT, may have been quite different from present-day A
and B (and/or C) and should not (without cause) be labelled with presentday strain designations. Alternatively, a region of 80% identity in
present-day A and B might have been transferred to both very recently
from an unknown source not available to our analysis; the situation in C
might favour one of these scenarios. In general, precision and density of
sampling are helpful in reconstructing pathways of LGT and in delineating GECs.
similar in sequence that it could not have arisen by vertical
descent from their common ancestor (even more, if G is
borne on a phage or a plasmid, or has characteristics of a
mobile element). If the two instances of G are 100%
identical, G was almost certainly transferred between A and
B (or from an unknown third entity C to both A and B) very
recently, and if G is widely distributed among the close
relatives of A, but not among the relatives of B, then G was
likely transferred from A to B, not vice versa. On the other
hand, if the two instances of G are only 80% identical, then
either an ancestral version of G was transferred between the
ancestors of A and B many generations ago, or alternatively,
the donor was an unknown source not in our analysis. It is
appropriate to abstract the former case (100% identity) as a
graph with nodes labelled as present-day strains A and B
connected by an edge representing the transfer of G, even
though we may not be able to rule out the possibility that an
unknown C lies along this edge. In the latter case (80%
identity), neither present-day strain A nor B is the donor,
and particularly where we cannot infer directionality of
transfer, it is better to label the nodes not with the presentday strain, but more generally (e.g. by species).
FEMS Microbiol Rev 35 (2011) 707–735
Further complications arise because LGT itself makes it
difficult to extrapolate genome contents back in time within
bacterial lineages. Node labels typically imply membership
in more-inclusive node-label classes, for example node
E. coli MG1655 simultaneously belongs to Escherichia,
Gammaproteobacteria and Bacteria, but given the temporal
and strain-to-strain variation in gene content, it may be
impossible to reconstruct the ancestral gene content and we
may need to use the appropriate pan-genome (Medini et al.,
2005). In contrast to cellular chromosomes, individual
plasmids are temporally shallow and at some temporal
depth merge indistinguishably into a pool of plasmid-borne
genetic material. We may wish to annotate the nodes of our
graph further, for example as an enterohaemorrhagic pathogen or a soil bacterium, terms that likewise may belong to a
more inclusive class within an ontology. Hooper et al.
(2009) caution that habitat annotations may be suspect, as
microorganisms can have broader environments than we
currently appreciate.
Delineation of nodes must be informed by a problemspecific perspective. It may matter (e.g. for clinical diagnosis)
that antibiotic resistance is being transferred among identifiable bacterial strains or isolates, in which case nodes are
labelled accordingly and interpreted to include the genetic
material normally inherited vertically within those strains or
isolates, i.e. the chromosomal genome with prophages, plus
stable plasmids. We term this perspective cell-centric. Alternatively, the problem may concern the flow of genetic
material through each type of vehicle (chromosomes,
phages, plasmids), in which case nodes could represent
specific vehicles even when these happen to be colocated
within a cell. We discuss examples of this vehicle-centric
perspective in GECs: data-driven approach. Finally, it may
be possible to adopt a purely DNA-centric perspective without reference to residence in a particular cell or vehicle,
although it is not immediately clear that smallish stretches of
DNA can be said to exchange genetic material, and we
suspect that labelling of nodes and/or edges would in
practice largely reduce this to one of the other two perspectives.
Edges depict the transfer of genetic material between
nodes. An edge must be supported by evidence, to which
we can sometimes apply a statistical threshold and/or a
quantitative estimate of confidence. Edges can be signed if
the donor–recipient relationship is known and stable, or
unsigned. Signed edges can be unidirectional (in direct
exchange, one node always donates) or bidirectional (each
node can both donate to, and accept from, the other). A
signed edge is typically drawn as an arrow pointing from
donor to recipient. Edges may be annotated by mechanism
(e.g. conjugative transfer of plasmid) and/or by frequency of
transfer. As GECs are constructed by differential frequencies
of transfer, annotation by frequency is highly relevant and
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E. Skippington & M.A. Ragan
could support computational analysis and modelling. Finally, it is important to emphasize that, in general, there is little
reason to imagine that intact genes are typically the units of
transfer and recombination (see Recombination per se); for
this reason, we refer to lateral genetic (not gene) transfer
(Chan et al., 2009).
Graphs are analysed by carrying out operations that
probe their structure, for example their connectedness and
modularity. Of particular relevance here are paths or walks
(series of nodes connected by edges, following any arrows),
transitively closed sets (sets of nodes, each member of which
is reachable from every other node in the set), cliques (sets of
nodes, each of which shares an edge with every other
member of that set) and near-cliques (Fig. 2). Any of these
four structures could in principle define a GEC. We dis-
Unidirectional
transfer
Transitively
closed set
unidirectional
A
B
B
A
favour GEC-as-path because exchange implies both donating and receiving, while given current methods for probing
LGT in natural communities, cliques and near-cliques may
set too high an evidentiary standard. Thus, operationally, we
recommend the following definition: a GEC is a set of
entities, each of which has over time both donated genetic
material to, and received genetic material from, every other
entity in that GEC, via a path of lateral transfer.
Abstracting genetic-exchange relationships as a graph
thus provides a framework for thinking about and delineating GECs. Each of the four structures we identify, and
indeed others, can be defined rigorously, has well-understood properties and opens for us a rich algorithmic and
computational toolkit. GECs as defined can overlap each
other, and can be contained in larger GECs; depending on
Bidirectional
transfer
A
B
Transitively
closed set
bidirectional
A
B
C
C
Clique
A
B
Biclique
A
C
D
C
D
B
Near-clique
Bridge node (E)
A
B
C
D
A
B
C
D
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E
E
F
G
H
Fig. 2. Graph operations and structures for
delineating genetic exchange communities.
FEMS Microbiol Rev 35 (2011) 707–735
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Lateral genetic transfer and genetic exchange communities
the question, it may be useful to work with only maximal
GECs. Requiring each entity (over time) to have donated
and accepted genetic material delineates GECs much more
tightly than in the ecosystem-wide exchange communities of
Jain et al. (2003). Here, we do not specifically limit the
period of time during which this exchange must have taken
place, although the quality of evidence tends to diminish as
we look farther back in time. Against these advantages must
be weighed risks, for example that graphical abstraction may
flatten out the diversity of GECs within which different
genes circulate (Gogarten & Townsend, 2005), in the same
way that molecular-interaction network diagrams tend to
lose touch with cell type- and tissue-specific interactions.
Nonetheless, a graphical framework allows GECs to be
identified, enumerated, analysed and perhaps situated within a more global map of LGT that might depict the complete
spectrum of exchange relationships, from active mutualexchange communities to the underlying gossamer of oneoff transformations by environmental DNA.
Constructing GECs
Groups of bacteria traditionally recognized as species, except
the most rigorously clonal, will fall within GECs, but GECs
are potentially much larger than species. Definitions of
biological species in general, and of prokaryotic species in
particular, lie beyond the scope of this review. Models that
emphasize descent with modification within clones and
periodic selective sweeps (Levin, 1981) fail to account for
genetic exchange and recombination within groups such as
Helicobacter pylori, Neisseria meningitides or S. pneumoniae
(Maynard Smith et al., 1993; Feil et al., 2003). On the other
hand, so-called biological species concepts map uncomfortably into situations in which recombination is relatively
infrequent, uncoordinated with replication or cell division,
integrates genetic material from phyletically diverse sources
(including plasmids, phages and environmental DNA) unpredictably into different regions of the chromosome and
occasionally results in drastic modification of phenotype.
Opinions differ on how much LGT might be required to
render ideas of species (indeed the tree-like nature of
descent and hierarchical classification systems in general)
meaningless in the prokaryotic context (Doolittle, 1999,
2009; Kurland, 2000; Berg & Kurland, 2002; Gogarten et al.,
2002), and whether and how some or all of these concepts
might be rescued (Gevers et al., 2005; Cohan, 2006; Doolittle
& Zhaxybayeva, 2009). Even among eukaryotes, reproductive isolation can be incomplete or contingent: evolution
remains a work in progress. Bacteria that are members of a
species by virtue of genetic recombination will for the same
reason belong to a common GEC, but GECs are potentially,
and often in reality, much broader than species.
FEMS Microbiol Rev 35 (2011) 707–735
LGT complicates species definitions, but constructs GECs.
GECs are actively fashioned (and continually refashioned)
by the complex ongoing interplay among habitats, donors,
vectors, recipients, mechanisms, sequences, population
structures and selection. In this sense, GECs are analogous
to ecological niches: except perhaps in the broadest sense,
niches do not exist a priori in the physical world, but are
constructed dynamically by organisms through diverse
physical, chemical and biological interactions with their
environment and with each other. Microorganisms similarly
construct GECs, in the process altering the genomes and
physiologies of their interaction partners and reciprocally
being altered by them (including the ability to differentially
accommodate or resist LGT). The recombinant microorganisms may then alter their physical environment or spread to
a new one.
In the following section, we review how each step in
lateral transfer, recombination, integration and establishment of genetic material offers opportunities for the establishment of GECs involving some, but not other exchange
partners, vectors and genes. In keeping with the theme of
this issue, we focus on antibiotic (antimicrobial) resistance.
For the purposes of this review, bacteria should be read to
include archaea where indicated by context, although the
latter are not recognized as human pathogens (Eckburg
et al., 2003) and to our knowledge do not harbour antibiotic-resistance determinants. We refer to exchange where
genetic material can (potentially) move in both directions.
Lateral (LGT) and horizontal (HGT) genetic transfer are
synonyms.
LGT and the construction of GECs
By the time we detect antibiotic resistance in a previously
susceptible strain, the DNA conferring that resistance has
come into contact with a host cell, entered its cytoplasm,
evaded the host defences, become established typically by
recombination into the host genome and integrated itself
sufficiently into host systems to express one or more protein
products, with the result that recombinant cells are now
abundant in the population. We examine these steps in turn,
focusing on the opportunities and barriers that can be
differentially exploited to construct GECs.
Availability of DNA to the potential host cell
Pathogens and their nonpathogenic relatives often share a
common habitat that encompasses DNA in the environment, agents of DNA mobilization and packaging, sites of
concentration, and vectors such as insects that can intermediate between the microbial and the eukaryotic worlds.
Brüssow (2009) and Norman et al. (2009) encourage us to
include viruses, conjugative plasmids and other mobile
genetic elements in our view of the living world. In this
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broader ‘eco-evo’ perspective, distinctions between pathogen and nonpathogen, and between factors of virulence and
colonization, become less clear-cut (Pallen & Wren, 2007).
Antibiotic-resistance genes occur naturally among some
bacteria and fungi, quite apart from antibiotic production
and use by humans (Allen et al., 2010). Humans encounter
this broader microbial world at distinct interfaces, and from
our perspective, describe regions of it as reservoirs, for
example of antibiotic resistance.
DNA can be surprisingly abundant in the natural environment. Free DNA is released into soil, water and other
natural environments by live bacteria (Lorenz & Wackernagel, 1994; Niemeyer & Gessler, 2002; Moscoso & Claverys,
2004) and plant root cells, pollen, dying bacteria and
decomposing biomass (Levy-Booth et al., 2007; Nielsen
et al., 2007), and can persist there for days to years (Vlassov
et al., 2007), indeed under some circumstances (e.g. permafrost), very much longer. Similarly, DNA can persist in
natural water bodies and deep-sea sediments (Vlassov
et al., 2007), with some polymers exceeding 10 kb in size
(Corinaldesi et al., 2005). The theoretical upper limit of
persistence of PCR-able DNA has been estimated at
400–600k years (Willerslev et al., 2004). In these environments, it is available for natural transformation (Lorenz &
Wackernagel, 1994; Paget & Simonet, 1994).
In humans, DNA enters the blood by active secretion
from cells or from cell death. Blood plasma can contain
DNA of high molecular weight (21–80 kb); although partly
resistant to DNase degradation, it is reasonably quickly
taken up by macrophages and cleared by the liver. Small
plasmids may be more stable in the blood than large
plasmids or chromosomal DNA (Rozenberg-Arska et al.,
1984). DNA bound to proteins on the cell surface likewise shows molecular weights 4 20 kbp (Morozkin et al.,
2004).
Viruses too are abundant and diverse in the natural
environment (Brabban et al., 2005; Breitbart & Rohwer,
2005). Estimates point to 1030–1032 phages mediating
4 1016 gene transfer events per second (Brabban et al.,
2005; Rohwer et al., 2009). Intact viruses and/or viral DNA
can move rapidly among biomes including soil, sediments,
freshwater and marine waters (Breitbart & Rohwer, 2004;
Sano et al., 2004). Although a global picture of viral host
specificity has not yet emerged, cross-infecting viruses may
not be uncommon (Breitbart & Rohwer, 2005). The human
gastrointestinal tract may host between 160 and 1200
distinct viral genome types (Chibani-Chennoufi et al.,
2004). Lambdoid coliphages themselves are mosaics (Juhala
et al., 2000), resulting from genetic exchange both within the
lambdoid phage family and with other families, and involving not only homologous but also extensive nonhomologous recombination (Canchaya et al., 2003a, b; Brüssow,
2009).
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E. Skippington & M.A. Ragan
Potential host cells also encounter other cells of the same
or different species that represent potential LGT donors.
Some bacteria, including members of Streptococcus, use
quorum sensing of a competence-stimulating peptide to
ensure that most LGT is within species. In Plasmids and
conjugation, we consider some of the diverse genetic elements that, under certain conditions, can be mobilized for
lateral transfer including plasmids, conjugative transposons,
other integrative conjugative elements (ICEs) and other
chromosomal regions. There is growing realization that
commensal and environmental bacteria can serve as important reservoirs of antibiotic resistance (Boerlin & ReidSmith, 2008), and in particular that ‘Gram-negative bacteria
have access to the gene pool of Gram-positive cocci’
(Courvalin, 1994). Strains commensal in animals have been
identified as reservoirs for the spread of antibiotic resistance
to humans via integrons located on plasmids and mobilized
by transposons in E. coli (Singh et al., 2005) and via phage
transduction in Salmonella (Brabban et al., 2005). Similarly,
the rhizosphere (Berg et al., 2005) and the environment
more generally (Henriques Normark & Normark, 2002; Jang
et al., 2008) can serve as a reservoir for opportunistic
pathogens in humans.
Specialized environments that bring together potential
donors, recipients, vectors and selective pressure favouring
LGT are known as recombination hotspots. Examples include
the digestive tracts of insects, the cytoplasm of amoebae,
surfaces in soil or around bodies of water, leaf and root
surfaces of plants, and within decomposing biomass (Davison, 1999; Crawford et al., 2005; Akhtar et al., 2009; Ragan &
Beiko, 2009; Moliner et al., 2010), and for antibiotic
resistance, the rumen of cattle, milk products, various foodstuffs, biofilms on food-processing equipment and the oral
cavity (reviewed by Kelly et al., 2009b).
Biofilms are a particularly important type of recombination hotspot. In a biofilm, large numbers of bacteria are
enclosed in a hydrated polymeric matrix composed of
polysaccharides, proteins, double-stranded DNA (dsDNA)
and single-stranded RNAs (ssRNAs). Bacteria release DNA
into this matrix not only upon death but also actively via a
type IV secretion system (Hall-Stoodley et al., 2004; Vlassov
et al., 2007), and this helps to stabilize the biofilm (Whitchurch et al., 2002). The efficiency of gene transfer by bacterial
conjugation is enhanced in biofilms; the E. coli F-conjugative plasmid encodes factors that induce biofilm development and, more generally, LGT enhances the stability of
biofilm structure (Ghigo, 2001; Molin & Tolker-Nielsen,
2003). All these factors enhance biofilms as hotspots for
LGT. The Staphylococcus aureus biofilm-associated protein
Bap induces an alternative method of biofilm production
and can be transferred laterally among Staphylococcus species (Tormo et al., 2005). Not only may antibiotic-resistant
bacteria flourish inside, but the biofilm structure itself offers
FEMS Microbiol Rev 35 (2011) 707–735
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Lateral genetic transfer and genetic exchange communities
a measure of physical protection against antibiotics. Biofilms are ubiquitous in natural environments, on and in
living bodies (e.g. dental plaque), and on artificial surfaces
including contact lenses, sutures, catheters, prostheses,
artificial heart valves and medical devices (Fux et al., 2005;
Sørensen et al., 2005; Ong et al., 2009).
Individual humans too can be LGT hotspots. Salmonella
enterica can transfer plasmid-borne antibiotic-resistance
genes with high frequency inside human epithelial cells
(Ferguson et al., 2002), and evidence is accumulating that
the intestinal tract can be an LGT hotspot and reservoir for
multidrug resistance (Salyers et al., 2004; Kelly et al., 2009b).
In populations of the highly recombinogenic obligate human pathogen Neisseria gonorrhoeae, antibiotic resistance
can spread at a high frequency via natural transformation,
potentially from strain to strain in mixed infections (Ohnishi et al., 2010).
Entry of DNA into the host cytoplasm
DNA and transformation
Diverse bacteria are naturally competent (able to take up
DNA from their environment), or can be stimulated to
become so (Nielsen et al., 1998; de Vries & Wackernagel,
2005; Johnsborg et al., 2007), including clinical isolates of
Campylobacter, Cardiobacterium, Haemophilus, Helicobacter,
Legionella, Moraxella, Neisseria, Pseudomonas, Staphylococcus, Streptococcus and Vibrio (Lorenz & Wackernagel, 1994;
Johnsborg et al., 2007). Competence-related genes are even
more broadly distributed (Ambur et al., 2009). Because
ssDNA enters the cytoplasm, plasmids must be reconstituted
as dsDNA (Chen & Dubnau, 2004) by interaction with
complementary sequence, for example on a preexisting copy
of the same plasmid, another newly arrived copy or (in the
case of multimeric plasmids) itself. As a consequence,
natural transformation with plasmids tends to be inefficient
and relatively unimportant as an entry mechanism leading
to the lateral transfer of antibiotic-resistance genes.
Uptake of DNA is usually sequence-independent,
although in some species (e.g. Haemophilus influenzae, N.
gonorrhoeae), it involves the recognition of short nucleotide
motifs that are interspersed along the chromosomal DNA.
This mechanism not only biases uptake in favour of
transformation by DNA of the same species, but the motifs
moreover promote subsequent homologous recombination
into the chromosome (Ambur et al., 2007).
Phage and transduction
Bacteriophages can serve as vectors for the lateral transmission of antibiotic-resistance genes among bacteria. The
mechanism of phage transduction is well understood (BrabFEMS Microbiol Rev 35 (2011) 707–735
ban et al., 2005): following infection of a host cell by a
temperate phage, phage DNA integrates into that of the host
at a specific point, or less specifically, depending on the type
of phage. The lysogenic conversion that often results may
render the host bacterium less susceptible to invasion by
other phages. The integrated phage genome (prophage) is
then transmitted vertically within the host lineage until the
lytic cycle is induced, during which an adjacent region of the
host genome is sometimes excised and packaged together
with that of the phage (specialized transduction). More
rarely, a nonadjacent region of host DNA is packaged and
delivered to a new bacterial host (generalized transduction).
Lytic viruses, which do not integrate into the host genome,
can similarly be agents of generalized transduction.
Several lines of evidence point to a role for temperate
phages in the assembly and spread of antibiotic resistance
within Salmonella species (Brabban et al., 2005). Schmieger
& Schicklmaier (1999) documented the transduction of
ampicillin, chloramphenicol and tetracycline resistance
among strains of S. typhimurium DT104. Genes specifying
resistance to five drug classes are clustered in a genomic
island (GI) that contains both phage- and plasmid-related
genes (Cloeckaert & Schwarz, 2001; Hermans et al., 2006).
Zhang & LeJeune (2008) demonstrated phage-mediated
transfer of the extended-spectrum cephalosporin-resistance
gene blaCYM 2 and tetracycline-resistance genes tet(A) and
tet(B) from a multidrug-resistant Salmonella to an antibiotic-susceptible S. typhimurium. Inducible phages have been
observed in 75% of antimicrobial-resistant Salmonella,
compared with 53% of non-antimicrobial-resistant isolates
(Zhang & LeJeune, 2008). Phages are likewise involved in the
transduction of multidrug resistance in Pseudomonas aeruginosa (Blahova et al., 2001).
In enteric bacteria, virulence factors such as the Shiga-like
dysentery toxins Stx 1 and Stx 2 are often encoded in
prophages. Prophages can be abundant in a bacterial genome, with different strains containing different types and
unique combinations. Temperate phages encoding Stx1 or
Stx 2 have been isolated from the environment and show
considerable variability in sequence, host range and infection characteristics (Brabban et al., 2005). Very recent lateral
acquisitions into E. coli genomes (although not into Shigella) are highly enriched in phage-related genes (Touchon
et al., 2009), reinforcing the picture of frequent, ongoing
LGT. Frequent deletions and point mutations have reinforced the view that prophages are ‘dead’ genetic elements.
However, most of the 18 prophages in the Sakai strain of
E. coli O157 are spontaneously inducible, can excise themselves, replicate, be packaged and released; moreover, they
can supply virion proteins for the assembly of other
lambdoid phages, and even recombine among themselves
(and likely with other lambdoid phages) to generate new
transducing phages able to spread virulence determinants to
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other strains (Asadulghani et al., 2009). Stx 1 production in
the enteric bacteria Citrobacter freundii and Enterobacter
cloacae (Herold et al., 2004) suggests transduction beyond
genus Escherichia.
Treatment with certain antibiotics can induce prophages
to enter the lytic phase (Brabban et al., 2005). Thus,
antibiotic use can not only act as a selective factor that
favours resistant strains, but may increase the number of
transduction events, thereby promoting LGT. This has been
demonstrated for transfer of the S. aureus pathogenicity
island (Úbeda et al., 2005; Maiques et al., 2006).
LGT is an important driver of evolution among viruses as
well. Comparative studies, both within individual families of
viral genes or proteins and at the genome scale, reveal
limited vertical descent within a broader picture of molecular diversity, multiple origins and recombination (Brüssow,
2009). Viruses sharing a common habitat can undergo
extensive recombination (Marston & Amrich, 2009). Where
present, verticality may arise from geographical separation
(Lee et al., 2007) or follow barriers (e.g. restriction-enzyme
specificities) imposed by their host genera (Goerke et al.,
2009). The canonical bacteriophages are considered to
have narrow host specificities, but this may be less true of
phages assembled from proteins of heterogeneous origin
(Asadulghani et al., 2009). Tailed dsDNA phages and the
corresponding prophages may all share, albeit nonuniformly, a common gene pool (Hendrix et al., 1999; Fraser
et al., 2006).
Plasmids and conjugation
Plasmids encode a range of phenotypic traits and are
important agents of LGT among bacteria (Frost et al., 2005;
Thomas & Nielsen, 2005; Schlüter et al., 2007). Plasmids are
the most common vectors of acquired resistance to antibiotics (Barlow, 2009) and indeed to many other factors.
Some antibiotic-resistance determinants appear to have
resided on plasmids for millions of years (Barlow & Hall,
2002), whereas others are mobilized from chromosomes
perhaps at an increasing rate (Barlow et al., 2008). As a class
of mobile genetic elements, plasmids are defined by three
key features: the capacity to exist and replicate extrachromosomally, the ability to be transferred between distinct
hosts and absence of genes essential to their hosts. Plasmids
are highly diverse in size, structure, transmission, evolutionary histories and accessory phenotypes (Slater et al.,
2008; Carattoli, 2009). This diversity is due in part to the
successive layering of LGT events, resulting in size variation
and deeply mosaic structures (Mellata et al., 2009). Plasmids
carrying the newly recognized NDM-1 resistance to carbapenem, for example, range in size from 50 to 500 kb
(Kumarasamy et al., 2010). Rather than providing a comprehensive review of plasmid diversity, here we focus on
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E. Skippington & M.A. Ragan
features of plasmids and their transfer that contribute to or
constrain their movement within GECs.
Plasmids are typically mobilized by conjugation. While
not all plasmids encode the functions essential for cell-tocell DNA transfer, nonconjugative plasmids can be mobilized by coresident conjugative plasmids. Not only plasmids but also ICEs are transferred from donor to recipient
cells by conjugation. Unlike conjugative plasmids, which
can be maintained and replicate autonomously in their
host, ICEs can be maintained only through integration into
the host replicon (Wozniak & Waldor, 2010) and thus are
not considered to be plasmids (Norman et al., 2009). We
discuss ICEs in more detail in Mobile genetic elements and
LGT. Partial or even entire chromosomes can also be
transferred via conjugation if the interbacterial junction is
stable for long enough – up to an hour or more (Thomas &
Nielsen, 2005). Transfer of large chromosomal blocks via
conjugation, driven by origins of transfer in mobile GIs,
has been proposed for Streptococcus agalactiae strains
(Brochet et al., 2008) and Clostridium difficile (He et al.,
2010).
The remarkable ability of conjugation to mediate plasmid
transfer between taxonomically and genetically unrelated
bacterial hosts facilitates gene sharing within broad GECs
(Ochman et al., 2000). Conjugation commonly crosses
species and genus boundaries (Davison, 1999) and, as we
discuss further in GECs: knowledge-driven approach, can
extend across biological domains (Buchanan-Wollaston
et al., 1987; Heinemann & Sprague, 1989). Because of the
existence of general mechanisms such as exclusion, which
constrain the conjugative transfer of plasmids, not all strains
or species within a community are equally efficient as
transfer donors. Some subpopulations of bacteria, including
bacteria hosting plasmids bearing antibiotic-resistance
genes, have high donor activity; these so-called amplifiers
(Dionisio et al., 2002) can accelerate the spread of plasmids
within their GEC.
Successful conjugation requires donor and recipient cells
to be compatible, as determined by surface proteins on the
recipient (Thomas & Nielsen, 2005). Donor cells encode
proteins involved in processes that comprise a conjugation
event. In the case of highly complex systems such as the large
self-transmissible plasmids of Gram-negative bacteria that
use a type IV secretion apparatus to produce a pilus
(Juhas et al., 2008), these processes include pilus assembly
and retraction, identification of compatible recipient cells
and signalling the commencement of DNA processing and
transfer. In some Gram-positive enterococci, identification
of compatible recipient cells is pheromone-activated and
mediated by an activator–antagonist relationship, ensuring
donor–recipient specificity (Hirt et al., 2002). The frequency
of conjugation, however, likely depends primarily on the
donor bacterium; recipient E. coli cells, for example, contain
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715
Lateral genetic transfer and genetic exchange communities
no nonessential genes essential for conjugation (PérezMendoza & De La Cruz, 2009).
Bacteria have nonetheless evolved strategies to inhibit
conjugation, and thus limit the production of transconjugants. Exclusion mechanisms limit conjugative transfer into
bacterial cells in which plasmids encoding similar transfer
systems already reside. The F plasmid, for example, does
this by two mechanisms, surface and entry exclusion,
mediated by plasmid genes traT and traS, respectively
(Achtman et al., 1977). The outer membrane protein TraT
hinders contact between the surface of the cell and the F
pilus about 10-fold, and the inner membrane protein TraS
impedes DNA entry about 100-fold (Frost et al., 1994).
Mechanisms of entry exclusion are widely distributed
and may be an essential feature of conjugative plasmids
(Garcillán-Barcia & de la Cruz, 2008). While exclusion can
create an effective barrier against conjugative transfer, it is
by no means impermeable. Among F plasmids, for example,
extensive interplasmid recombination suggests that some
plasmids have evaded exclusion mechanisms to enter cells
that carried closely related elements, and were subsequently
able to replicate and be stably maintained (Boyd et al.,
1996).
Plasmids can be classed into incompatibility (Inc) groups
(Novick, 1987). Incompatibility has been described as a
manifestation of relatedness: plasmids that utilize common
mechanisms for replication or inheritance cannot proliferate
in the same cell line (Carattoli et al., 2005). Inc groups thus
constrain plasmid host range. Plasmids must nonetheless
adapt to unfavourable hosts if they are to persist long term
within a GEC. While some plasmids can be maintained only
in one or a few bacterial hosts, others replicate in diverse
bacterial genera. Broad-host-range plasmids have evolved
diverse replication strategies including versatile replication
systems, self-sufficiency in encoding proteins necessary to
establish the replisome after conjugation and multiple
replicons (Kramer et al., 1998; Toukdarian, 2004). Plasmids
of the self-transmissible incompatibility group IncP-1 carry
a wide range of resistance genes and are found in environmental (particularly wastewater) as well as clinical settings;
they not only utilize mechanisms for transfer, replication
and maintenance in diverse Gram-negative hosts, but can
also mobilize the transfer of other plasmids into an even
broader range of hosts (Schlüter et al., 2007).
Broad-host-range plasmids may not be equally stable in
all hosts, particularly as their ability to persist in a bacterial
population is determined in part by host-encoded traits (De
Gelder et al., 2007). Under certain selective conditions,
plasmids can expand their host range, often via a relatively
small number of genetic changes (De Gelder et al., 2008).
Plasmids in the same plasmid family can exhibit very
different host ranges (Wu & Tseng, 2000), suggesting that
broad-host-range plasmids can probably arise selectively
FEMS Microbiol Rev 35 (2011) 707–735
from those of narrower host ranges (Thomas & Nielsen,
2005).
Fondi et al. (2010) introduced the concept of the panplasmidome, the set of all plasmids harboured by members
of a taxonomic group. Based on analysis of 493 proteincoding sequences, the 29 plasmids found in strains of genus
Acinetobacter were separated into two main groups: the
pKLH family of eight plasmids from a number of Acinetobacter species and a group of 15 plasmids from Acinetobacter
baumannii strains. Six further plasmids fell within neither
group. Patterns of identity reveal extensive gene sharing
within group (more so within the pKLH plasmid family),
but less sharing between group; thus, in this case, as
presumably in many others, plasmids mediate preferential
flow of genetic information within and between GECs.
Evasion of host defence systems
Bacteria mount multilayered defences against foreign DNA
(Horvath & Barrangou, 2010). In transformation, conjugative transfer and transduction by ssDNA phages, DNA enters
the bacterial cytoplasm in a single-stranded form and as
such is available for recombination into the host chromosome. Host defences against these processes must therefore
target ssDNA. Restriction–modification systems act only
against dsDNA, and thus in principle provide defence only
against reconstituted plasmids and dsDNA phages.
Restriction–modification systems identify and destroy foreign DNA: the modification component recognizes a specific short oligonucleotide sequence and methylates a defined
nucleotide within it, protecting that site from cleavage by a
restriction endonuclease (Wilkins, 2002). If matters remained so simple, host DNA would be protected while
reconstituted foreign plasmids would be prevented from
gaining a foothold. Plasmids can, however, counteract host
defence systems in several ways. Some plasmids encode
proteins that are rapidly expressed upon entry and inhibit
host restriction–modification systems; in certain other cases,
not only plasmids but also plasmid-encoded inhibitory
proteins enter the host during conjugation (Wilkins, 2002).
Many plasmids encode distinct restriction–modification
systems that protect both themselves and their new host.
Yet another strategy is seen with IncP-1 plasmids, from
which most restriction–modification sites have been eliminated by selection (Wilkins et al., 1996). As the success of
restriction–modification as a barrier is approximately proportional to the number of target sites (Thomas & Nielsen,
2005), these mechanisms will have greater or lesser success
against foreign dsDNA from one host–plasmid system to
another. Differential outcomes likewise arise from the
diversity of host–plasmid interactions that determine replicability and copy-number control (Thomas & Nielsen, 2005).
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716
Most human strains of S. aureus can be placed into one of
10 clonal complexes (Feil et al., 2003) characterized inter alia
by unique combinations of surface and virulence genes and
by a unique specificity of the main restriction–modification
system SauI (Lindsay et al., 2006). Genetic material is
exchanged within each complex, but DNA originating from
a different complex is recognized as foreign and cleaved
(Waldron & Lindsay, 2006). Staphylococcus phages have
evolved a degree of specificity for host complex group
(Goerke et al., 2009) and conversely, strains in each complex
accept only a few phage types. The SauI system likewise
presents a (partial) barrier to conjugative transfer into S.
aureus from enterococci (Waldron & Lindsay, 2006).
More recently, an ‘immune system’ based on clustered,
regularly interspaced, short palindromic repeat (CRISPR)
loci, together with CRISPR-associated (cas) proteins, has
been recognized in diverse bacteria (including human
pathogens) and many archaea. CRISPR loci interfere with
infection by foreign phages and plasmids by specifying small
RNAs, which, mobilized in a Cas complex, target complementary sequences on foreign DNA, with the result that the
foreign DNA is degraded. Each CRISPR locus contains an
array of direct repeats separated by sequences that originate
from viral and plasmid genomes ‘captured’ by other components of the system. The small RNAs generated by transcription are thus complementary to foreign DNA previously
presented to the cytoplasm, enabling the CRISPR/Cas
system to present a programmable barrier to LGT analogous
in this respect to the mammalian immune system (Horvath
& Barrangou, 2010; Marraffini & Sontheimer, 2010a, b).
Much remains to be learned about Cas complexes in diverse
bacteria, including whether they can target ssDNA and/or
RNA as well as dsDNA (Hale et al., 2009; Horvath &
Barrangou, 2010); thus, it is not yet known whether
CRISPR/Cas offers protection against transformation (Marraffini, 2010). There is evidence that phages can circumvent
the CRISPR/Cas system by mutation or deletion of genomic
regions complementary to the CRISPR spacers (Horvath &
Barrangou, 2010).
CRISPR sequences have been recognized not only in
genomes of diverse prokaryotes but also in bean mitochondria (Mojica et al., 2000) and the ocean metagenome
(Sorokin et al., 2010), where they ‘retain the memory of the
local virus population and a particularly ocean location’. The
system is absent from S. pneumoniae and H. pylori, but is
found in some other readily transformable bacteria (Marraffini, 2010). The disjunct phyletic distributions of some
CRISPR loci, and phylogenetic trees inferred from sequences
of Cas proteins, provide compelling evidence for lateral
dispersion of the CRISPR/Cas system itself (Horvath &
Barrangou, 2010) mediated by plasmids, megaplasmids and
possibly prophage (Sorek et al., 2008). Diversity can be
substantial within species: four main groups of CRISPR loci,
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E. Skippington & M.A. Ragan
and one minor group, have been recognized among 100
diverse strains of E. coli (Dı́ez-Villaseñor et al., 2010).
Not only CRISPR/Cas but also restriction–modification
systems can spread by LGT (Kobayashi et al., 1999; Sibley &
Raleigh, 2004; Zhao et al., 2006), again illustrating the
potential for the ongoing dynamic construction of GECs.
Recombination
Recombination per se
Following the physical transfer of exogenous DNA into a new
host cell, foreign DNA can be integrated into the recipient
genome through homologous recombination, illegitimate
recombination or a combination of the two. Escherichia coli
can also integrate exogenous DNA into its chromosome or
plasmids via an end-joining repair mechanism known as
‘alternative end-joining’ (Chayot et al., 2010) that does not
involve recombination. Nonhomologous sequences, including those specifying antibiotic resistance, can be integrated in
the absence of large-scale sequence similarity: three to eight
consecutive nucleotides are required for ligation. Here, we
focus on mechanisms of integration that involve recombination into the host genome.
The incorporation of foreign DNA via homologous recombination is a highly efficient process that occurs at an
appreciable frequency and contributes to the propagation
of alleles in a population (Lawrence & Retchless, 2009). To
be recombined by this process, incoming sequences must
contain a region of sufficient length (typically 25–200 bp)
and similarity to the recipient genome (Thomas & Nielsen,
2005). In Bacillus subtilis, E. coli, Pseudomonas stutzeri and S.
pneumoniae, the frequency of homologous recombination
has been shown to decrease with increased sequence divergence in a log-linear relationship (Zawadski et al., 1995;
Vulić et al., 1997; Majewski et al., 2000; Meier & Wackernagel, 2005).
In contrast, nonhomologous or illegitimate recombination
results in the incorporation of less-similar genetic material,
i.e. from distantly related donors, although at a lower
frequency (Ochman et al., 2000). Its adaptive benefits can
differ from those of homologous recombination (Vos,
2009). Homology-facilitated illegitimate recombination combines the features of both homologous and illegitimate
recombination (Meier & Wackernagel, 2003). Introduction
of a low-similarity sequence into a recipient genome can be
stimulated up to 105-fold if it contains a high-similarly
region that can initiate recombination and anchor its
extension into the adjacent lower-similarity segment (de
Vries & Wackernagel, 2002; Prudhomme et al., 2002).
Although homologous DNA can be integrated with considerable efficiency, the frequency of recombination is very
low at an overall genome sequence divergence 4 25%
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Lateral genetic transfer and genetic exchange communities
(Matic et al., 1997; Vulić et al., 1997; Majewski & Cohan,
1998; Majewski et al., 2000; de Vries et al., 2001). Evolutionarily unrelated species do nonetheless exchange genetic
material (Matic et al., 1996). Mismatch repair systems
represent the principal barrier to recombination between
divergent species, but this barrier is permeable to some
genetic fragments (Gogarten et al., 2002) and can itself be
subject to LGT (Denamur et al., 2000; Lin et al., 2007).
The efficiency of recombination correlates not only with
sequence divergence but also with the length of the transferred region and with the genomic location of integration.
This was illustrated particularly clearly by a genome-wide
study of genetic gain and loss events across 20 E. coli strains
(Touchon et al., 2009). These investigators found that the
length of acquired regions varies widely; only 8% of gains
involve 4 10 genes in a single event. The latter include
pathogenicity islands and prophages. Recombination and
genetic loss preferentially occur at sites that are highly
conserved across E. coli genomes (integration hotspots),
and 61% of all synteny breakpoints identified across these
genomes fell within 133 integration hotspots, implying that
these are hotspots of genomic rearrangement as well.
Recombination need not involve the integration of regions exactly delineated by gene boundaries. In many gene
families, regions sufficiently conserved to promote homologous or homology-facilitated illegitimate recombination
occur internal to the sequence (Gogarten et al., 2002),
providing the possibility that portions of genes might be
integrated. Indeed, evidence has been presented for LGT of
DNA regions ranging in length from seven nucleotides
(Denamur et al., 2000) to entire bacterial chromosomes
(Lin et al., 2008), encompassing partial and entire genes,
intergenic regions, multigene clusters, transposable elements, prophages and GIs (Ragan & Beiko, 2009). Chan
et al. (2009) reported that among 1462 sets of orthologous
genes, 286 (19.6%) show clear evidence of at least one
recombination breakpoint inside the ORF; they found some
evidence that recombination breakpoints avoided gene
regions encoding protein structural domains (domons) if
the domains were small, whereas domons corresponding to
large domains were interrupted by recombination breakpoints uniformly at random.
Mobile genetic elements and LGT
The reassortment of resistance genes from different donors
to create multidrug-resistant strains is a clear example of
how recombination can allow bacterial populations to adapt
to selective pressure over the short term (see Box 1). Here,
we review the molecular mechanisms and elements that
contribute to LGT through recombination with a host
genome, focusing on how these elements contribute to the
FEMS Microbiol Rev 35 (2011) 707–735
Box 1. LGT and the emergence of antibiotic resistance in Staphylococcus aureus
Staphylococcus aureus has become known for developing resistance to
antibiotics. Chambers & DeLeo (2009) describe the emergence of
multidrug-resistant S. aureus strains as a series of epidemic waves that
have successively given rise to epidemic penicillin-resistant S. aureus in
hospitals and the community, methicillin-resistant S. aureus (MRSA) in
hospitals, community-associated MRSA, vancomycin-intermediate
S. aureus (VISA) and fully vancomycin-resistant MRSA (VRSA). LGT has
played a central role in most or all of these waves. The penicillin
resistance that appeared in the mid-1940s was specified by a plasmidborne b-lactamase with narrow specificity for penicillin. MRSA was first
seen in 1960, shortly after methicillin was introduced (Barber, 1961;
Jevons, 1961). The origins of mecA, which encodes a protein that binds
to and inactivates b-lactam antibiotics generally, are uncertain
(Hiramatsu et al., 2001) although a mecA homologue 80% identical at
the amino acid level occurs in methicillin-sensitive Staphylococcus sciuri
and represents a potential precursor (Couto et al., 1996). mecA occurs
in a mobile chromosomal cassette (SCCmec) that also encodes genes
specifying recombination and excision; variants of SCCmec differ in
recombination potential and hence in the ability to spread. Communityassociated MRSA strains seem not to have arisen directly from hospital
strains, but have been (and are being) assembled laterally based on
different combinations of the SCCmec cassette, plasmids, prophages,
pathogenicity islands and transposons carrying a variety of resistance
and virulence determinants (Chambers & DeLeo, 2009). Whereas
resistance in VISA appears to be chromosomally mediated, VRSA strains
have emerged via plasmid-mediated conjugational transfer of the vanA
operon into MRSA from a vancomycin-resistant Enterococcus faecalis
(Showsh et al., 2001; Lowy, 2003; Weigel et al., 2003).
Methicillin and vancomycin are widely used in clinical practice, but
resistance determinants have spread so far only within certain S. aureus
lineages (Lindsay, 2010). Robinson & Enright (2003) estimated that
methicillin-resistance determinants had at that point been transferred
into S. aureus about 20 times. Similarly, the lateral dissemination of
vancomycin resistance has been relatively slow; only about 10 VRSA
strains have been isolated exclusively in health-care settings (Chambers
& DeLeo, 2009), a modest number given the high incidence of
vancomycin-treated patients who harbour both vancomycin-resistant
enterococci and S. aureus (Weigel et al., 2003). The slow spread of
these resistance determinants so far may be due in part to specific
genetic mechanisms within the S. aureus lineage that constrain both
intra- and interspecies exchange.
Waldron & Lindsay (2006) characterized Sau1, a type I restriction
modification system composed of the hsdR (restriction) gene and two
copies of the hasM (modification) and hsdS (sequence specificity)
genes. The system recognizes and digests foreign DNA entering the
cell, providing a barrier to the acquisition of foreign DNA by S. aureus
isolates. The hsdS genes, responsible for recognizing specific foreign
DNA sequences, differ significantly among S. aureus of different
lineages. These differences constrain within-species transfer and have
contributed to the emergence of the clonal structure of MRSA (Lindsay,
2010). More recently, Corvaglia et al. (2010) identified and
characterized in clinical S. aureus a type III-like restriction endonuclease,
a previously unidentified barrier that prevents transformation by DNA
from other species. Critically, some clinical MRSA strains are deficient
in this system, potentially rendering them more susceptible to
acquisition of DNA from other bacterial species; this system is of
particular clinical relevance because it may represent the primary barrier
to acceptance of vancomycin resistance from E. faecalis. A clinical
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Box 1. Continued.
isolate of Staphylococcus epidermidis has been found to have a CRISPR
locus identical in sequence to a region of the nickase gene nes found in
all sequenced staphylococcal conjugative plasmids including those in
MRSA and VRSA (Marraffini & Sontheimer, 2008); this locus is not
known in strains of S. epidermidis in ATCC.
Not only do S. aureus strains limit participation in genetic exchange,
they also encode mechanisms that promote the uptake of DNA. For
example, S. aureus produce peptides that induce enterococci to enter
the aggregative state, and this facilitates cross-generic exchange
(Clewell et al., 2002; Flannagan & Clewell, 2002). Genetic exchange
involving the S. aureus lineage appears to be tightly regulated by a
balance between mechanisms that limit or promote transfer.
spread and emergence of new combinations of chromosomally encoded resistance.
Mobilization of DNA within genomes (transposition)
plays an important role in the intracellular and intercellular
movement of genes. Transposons have long been associated
with the dissemination of antibiotic resistance (Bennett,
2008). While their structure and genetic relatedness varies
widely, in general, they are composed of a central DNA
sequence flanked by inverted insertion sequences (IS) or
other elements involved in transposition.
Recently, a new class of recombination system requiring
only one insertion element for gene mobilization has been
recognized (Toleman et al., 2006a, b). These mobile elements,
known as insertion sequence common regions (ISCRs), transpose by a rolling-circle mechanism that is distinct from that
used by transposons (Tavakoli et al., 2000). ISCRs lack the
terminal repeats present in most IS elements, and instead are
bounded by terminal sequences called oriIS and terIS for the
orgin and the termination of replication, respectively. Recognition of the terIS has been shown to be somewhat inaccurate
(Tavakoli et al., 2000), with the outcome that sequences
adjacent to an ISCR, including antibiotic-resistance determinants, can be mobilized (Bennett, 2008). Numerous families
of ISCR elements are now recognized, many associated with
antibiotic-resistance genes or other selectable determinants
(Toleman et al., 2006b). The tendency of ISCR elements to
mobilize sequence beyond the terIS may be involved in the
construction of complex class 1 integrons that harbour new
assemblies of resistance gene arrays not seen in classical
integrons (Toleman et al., 2006a, b).
Integrons are elements that capture mobile gene cassettes
by site-specific recombination rather than by transposition
(Hall & Collis, 1995; Mazel, 2006). Integrons share a
common recombination system composed of a gene (intI)
that encodes a site-specific recombinase (IntI) and an
adjacent primary recombination site (attI) into which gene
cassettes can be integrated. IntI-catalysed recombination
events move gene cassettes within and between integrons,
allowing these cassettes to be sorted into novel combina2011 Federation of European Microbiological Societies
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c
E. Skippington & M.A. Ragan
tions. While one or more gene cassettes are found in most
integrons, they are not a definitive part of the integron
structure, and indeed, integrons lacking cassettes have been
found in the environment (Bissonnette & Roy, 1992).
Integrons can be chromosomally encoded and are not
independently mobile; they are, however, often mobilized
on plasmids and transposons, in this way moving among
diverse genomes and taking on their observed broad phyletic
distribution (Hall et al., 1999; Boucher et al., 2007; Nemergut et al., 2008). Movement of gene cassettes encoding
antibiotic resistance within and between integrons, together
with clonal expansion, plays an important role in the
emergence of multidrug resistance (Krauland et al., 2009).
ICEs are self-transmissible mobile genetic elements that
are increasingly recognized as contributing to lateral genetic
flow within GECs. In particular, the role of ICEs in the
dissemination of antibiotic resistance has been recognized in
pathogens (Hochhut et al., 2001b; Whittle et al., 2002;
Mohd-Zain et al., 2004). The term ICE was introduced by
Burrus et al. (2002) to encompass all mobile genetic
elements with conjugative or integrative properties, independent of their mechanism of integration or conjugation
(Burrus & Waldor, 2004a; Burrus et al., 2006; Wozniak &
Waldor, 2010). ICEs resemble conjugative plasmids in that
they disseminate via conjugation, but unlike plasmids, may
not be able to replicate autonomously, although this is still
under investigation (for discussion, see Wozniak & Waldor,
2010). ICEs cross-circulate among a diverse range of hosts:
Tn916 for example, one of the first ICEs recognized, has
been identified in Proteobacteria, Actinobacteria and Firmicutes (Roberts & Mullany, 2009). Another well-studied
example is the Vibrio cholerae-derived ICE known as SXT,
which, together with related elements such as R391, have
now been identified in Gammaproteobacteria worldwide
(Burrus et al., 2006). In the laboratory, the conjugative range
of SXT includes V. cholerae and strains of E. coli (Waldor
et al., 1996).
To date, there has been limited exploration of the mechanisms that regulate and control the dissemination of ICEs
within such diverse GECs. However, recent comparative
analyses of the genomes of 13 SXT/R391 elements provide
evidence not only of extensive recombination among ICEs
but also of recombination between ICEs and other mobile
elements such as plasmids and phage (Garriss et al., 2009;
Wozniak et al., 2009). The reassortment of genetic regions
encoding site-specific integration mechanisms among ICEs
and other mobile elements may explain the expansion of ICE
host ranges (Wozniak & Waldor, 2010).
SXT ICEs carry a conserved core set of sequences shared
among all SXT elements, as well as highly mosaic sets of
sequences shared only within subsets of these elements.
Antibiotic-resistance genes are encoded in the latter and
appear to undergo significant flux into and out of the
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Lateral genetic transfer and genetic exchange communities
elements (Burrus et al., 2006). This extensive swapping of
constituent genetic regions among SXT ICEs may arise from
their ability to form tandem arrays of closely related
elements, as this would offer increased opportunities for
homologous recombination (Hochhut et al., 2001a; Burrus
& Waldor, 2004b). Garriss et al. (2009) assert that this
inherent ability to reassort their variable gene content makes
it possible for ICEs to contribute to the dissemination of
new combinations of antibiotic-resistance genes.
GIs are large chromosomal regions that have been acquired by LGT. Unlike ICEs, they are no longer (or may never
have been) self-transmissible (Hentschel & Hacker, 2001).
GIs encode their own excision and integration, and can be
mobilized by plasmids, phages and ICE conjugation systems
(Dobrindt et al., 2004; Boyd et al., 2008; Novick et al., 2010).
They typically exhibit an aberrant base composition vis-à-vis
the host bacterial genome, a feature of recently introgressed
regions (Lawrence & Ochman, 1997; Ragan, 2001b).
Although otherwise diverse, GIs encode an integrase (and
often other mobility determinants, for example enzymes for
excision and transposition) and primary attachment sites,
and are flanked by direct perfect or near-perfect repeats.
Phylogenetic analysis of integrase proteins (Boyd et al., 2008)
reveals GIs to be an evolutionarily distinct class: integrase
proteins encoded by GIs and prophages (IntG and IntP,
respectively) form distinct, divergent subtrees within the
overall integrase tree. The deeply branched IntG subtree is
in general agreement with accepted organismal relationships
among Proteobacteria, implying that GIs have been stably
associated with proteobacterial lineages over evolutionary
time. On the other hand, S. aureus pathogenicity islands can
be transduced to Listeria monocytogenes (Chen & Novick,
2009) and ‘it is probably only a matter of time’ until they are
recognized more broadly among Firmicutes and perhaps
even archaea (Novick et al., 2010).
The earliest-recognized GIs encoded virulence factors
(Blum et al., 1994; Hacker et al., 1997; Dobrindt & Reidl,
2000; Hacker & Kaper, 2000), but the term now encompasses regions encoding genes of diverse functions including
antibiotic resistance, superantigens, transporters and metabolic genes (Juhas et al., 2009). Salmonella genomic island 1
(SGI1) illustrates the role of GIs in the dissemination of
resistance (Boerlin & Reid-Smith, 2008). SGI1, first described in strains of epidemic multidrug-resistant S. enterica
serovar Typhimurium phage type DT104, contains a cluster
of genes encoding resistance to ampicillin, chloramphenicol/florfenicol, streptomycin/spectinomycin, sulphonamides and tetracyclines (Boyd et al., 2001; Mulvey et al.,
2006). This cluster is essentially a complex integron composed of adjacent tetracycline and chloramphenicol–florfenicol resistance genes of plasmid origin, flanked by two class
1 integrons (Boyd et al., 2001). Although SGI1 itself does not
encode self-mobilization, Doublet et al. (2005) demonFEMS Microbiol Rev 35 (2011) 707–735
strated in vitro that it is readily transferrable via a helper
plasmid during conjugation between Salmonella strains and
from Salmonella to E. coli. The discovery of SGI1 in
Salmonella serovars other than Typhimurium (Velgea et al.,
2005) strongly suggests that the association of SGI1 with
other mobile genetic elements has contributed to the spread
of antibiotic resistance within Salmonella.
Integration into host-regulatory and molecular
interaction networks
If laterally acquired genetic material is to persist in its new
host, its expression must be regulated and the gene products
it encodes must interact successfully with host systems.
Newly introgressed DNA must therefore avoid being silenced, and over time establish appropriate interaction
partners by recruiting transcriptional regulators (Navarre
et al., 2007; Wellner et al., 2007; Lercher & Pál, 2008),
recognizing the host’s signals for transcription, translation,
folding and assembly (Lercher & Pál, 2008), and otherwise
undergoing changes that fine-tune the kinetic and thermodynamic interactions of the encoded protein. Inappropriate
expression would likely impose a significant upfront cost on
host competitive fitness; enteric bacteria limit these costs by
silencing the expression of foreign genes via the DNAbinding protein H-NS (Dorman, 2007; Navarre et al., 2007).
H-NS preferentially recognizes and silences sequences
that have a G1C content lower than that of the host
genome; this includes many lateral sequences, particularly
those formerly resident in phage (Daubin & Ochman, 2004;
Lucchini et al., 2006; Oshima et al., 2006; Navarre et al.,
2007). Indeed, approximately 90% of H-NS-repressed genes
in Salmonella show evidence of lateral origin (Navarre et al.,
2007). Nonetheless, some laterally acquired genes confer
relatively immediate functionality (e.g. for antibiotic resistance), suggesting that bacteria can counteract transcriptional downregulation by H-NS, where the newly
introgressed DNA confers selective advantage. Amelioration
to host G1C content and recruitment of antagonists of
H-NS action have been put forward as strategies by which
host organisms ‘fight back’ against H-NS silencing (Navarre
et al., 2007). Different strains within a species may be
differentially able to counteract H-NS silencing (Sankar
et al., 2009), potentially yielding a competitive advantage
under some circumstances.
While there is a growing understanding of the mechanisms that underpin the acquisition of antibiotic-resistance
genes via LGT (Boerlin & Reid-Smith, 2008), the network
and evolutionary dynamics that allow their efficient stoichiometric participation in cellular networks remain relatively unexplored. Genes encoding antibiotic resistance,
especially single-function resistance determinants such as
b-lactamases and aminoglycoside-modifying enzymes, tend
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to belong to simple sets of functional networks, and
probably for this reason, have received limited attention in
the network literature. In particular, little is known of the
evolutionary histories of antibiotic-resistance genes resident
on plasmids, making it difficult to predict how readily they
(and their products) might join and interact with specific
cellular networks. Plasmids carry resistance determinants
against practically every type of antimicrobial agent, but
except for genes encoding replication and transfer functions
(Fernández-López et al., 2006; Cevallos et al., 2008), these
determinants are diverse in sequence and structure, rendering traditional phylogenetic approaches ineffective (Bentley
& Parkhill, 2004).
According to the complexity hypothesis (Jain et al., 1999),
the likelihood that a gene can be established successfully in a
new host is inversely correlated with the number of partners
with which the corresponding protein must interact. The
extended complexity hypothesis postulates that genes that
encode proteins with many interaction partners are relatively less likely to be under adaptive evolution (ArisBrosou, 2005). Thus, genes encoding proteins that function
in large complexes, including those responsible for translation and transcription, are infrequently of lateral origin,
whereas proteins that function more autonomously (e.g. blactamases) can immediately affect phenotype and confer a
selective advantage to the host. Genes newly recombined
into the chromosome may or may not have to establish
transcriptional regulation: those replacing (part of) a preexisting similar gene via homologous recombination, for
example, may be successfully controlled by existing promoters and require only fine-tuning, for example to optimize
codon usage. Another strategy is seen with the IC element
SXT-R391, which encodes a regulatory module that modulates its gene expression in response to environmental
stimuli (Wozniak & Waldor, 2010).
More generally, genes of lateral origin exhibit more
complex regulation (tend to be regulated by more regulators) than native genes (Price et al., 2008). Lercher & Pál
(2008) found that transferred genes are generally integrated
into the regulatory network of their host over millions of
years. The rapid and broad dissemination of resistance
determinants clearly occurs very much more quickly than
this (see Box 1), suggesting a weaker integration into hostregulatory networks.
Selection and spread in the population
Antibiotic resistance is clinically significant to the extent
that it allows pathogens to evade therapeutic intervention.
Under a selective regime (e.g. the application of antibiotics),
resistant cells remain reproductively successful while their
susceptible counterparts do not. If the selective pressure
persists, resistant cells will eventually become predominant
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E. Skippington & M.A. Ragan
in the population. This simple scenario is complicated by
the costs that the capture, integration, maintenance and
expression of antibiotic-resistance determinants may impose on host fitness; by linkage among selectable traits; by
the genetic structure and dynamics of bacterial populations;
and of course by contingencies of an individual selective
regime for example its dose, uniformity and duration. Here,
we consider the cost–benefit equation with regard to host
fitness, and the effect of multiple selectable traits.
The cost--benefit equation for LGT
It has long been established that the presence of phages
(Lenski, 1988a) or plasmids (Godwin & Slater, 1979; Helling
et al., 1981) can reduce the reproductive fitness of the
bacterial host. Plasmids that carry antibiotic resistance
usually impose a fitness cost, although its magnitude can
vary over at least two orders of magnitude (De Gelder et al.,
2008; Andersson & Hughes, 2010), while chromosomal
resistance mutations may impose a cost (Gagneux et al.,
2006) or be largely ‘free’ (Böttger et al., 1998). Novel DNA of
lateral origin might likewise reduce host fitness through
increased mutational load, direct and indirect metabolic
costs of replication and expression, and/or increased regulatory overhead. While dissecting these factors below, we bear
in mind that selection acts at the level of organism or local
population.
Mutational load refers, inter alia, to the interruption
of genes or control regions for example by transposon
insertion, suboptimal codon usage, deviation from replichore balance (Darling et al., 2008), pleiotropic detuning
of regulatory and molecular-interaction networks, and
reduced catalytic efficiency. It is inherently risky to express
a foreign gene: experience from microbial genomesequencing projects shows that many genes cannot be
cloned in E. coli, often due to the toxicity of the protein
product (Sorek et al., 2007). Perhaps not coincidentally,
most genes unclonable in E. coli were single copy in their
original genome. This risk may be mitigated by gene
silencing, for example via the H-NS system (Dorman, 2007;
Navarre et al., 2007).
Gene transmission and expression incur metabolic
costs
Direct costs of substrate utilization, measured as phosphate
groups per residue, can be under strong selective pressure
(Nogueira et al., 2009). Costs might arise indirectly via
slower replication or the requirement for larger cells (Kurland, 2005). In an E. coli–pBR322 system, fitness was
reduced only if the resistance protein (later characterized as
a tetracycline-H1 antiporter) was expressed in active form;
expression of defective protein did not noticeably reduce
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Lateral genetic transfer and genetic exchange communities
fitness, perhaps because the cost arises from localization or
activity in the membrane, i.e. not from substrate or energy
costs per se (Lee & Edlin, 1985).
Are these costs significant in natural environments? Gene
content can vary substantially within closely related groups
(e.g. species) of bacteria: among 20 strains of E. coli isolated
from mammalian intestinal or urinary tracts, for example,
gene number per genome ranges from 4627 to 5129, of
which only 1976 are core in the sense of being represented in
all strains. A discrete cluster of several hundred marine
Vibrio isolates 4 99% identical by 16S rRNA gene sequence
exhibits extreme heterogeneity of genome size, genotype
and Hsp60 allele type; as their environment, averaged over
time, is essentially homogeneous at the cellular scale, these
differences must be selectively neutral (Thompson et al.,
2005). Among the genes present in both E. coli and S.
enterica, those lacking a significant match elsewhere among
prokaryotes (ORFans) show a Ka/Ks ratio (0.19 0.030)
consistent with weak purifying selection, whereas those with
sporadic matches exhibit a Ka/Ks only slightly greater
(0.08 0.005) than that of core genes (0.05 0.001). Genes
in the two former categories are likely of lateral origin, the
ORFans probably via phage transduction (Daubin & Ochman, 2004).
Some costs to fitness may be transient
Two classes of mutations that confer resistance to phage
have been recognized, reducing fitness by 15% and 45%,
respectively, via maladaptive pleiotropic effects (Lenski,
1988a); however, these effects were largely compensated by
subsequent mutations over 400 generations (Lenski, 1988b).
In Mycobacterium tuberculosis, resistance to rifampin is
mediated by missense mutations in rpoB, the gene encoding
the b subunit of RNA polymerase (Gagneux et al. 2006);
most mutations conferring resistance incur a cost (assessed
by competition assays) in the range 10–40%, although
S531L has little to no cost, and clinical S531L mutants have
4 4% higher competitive fitness than their rifampinsusceptible ancestors. Many instances are known in which
the fitness costs of antibiotic resistance are subsequently
mitigated by compensatory mutations (Andersson & Levin,
1999; Johnsen et al., 2009; Andersson & Hughes, 2010), and
strains with lower-cost mutations will tend to be selected in
populations (Gagneux et al., 2006).
Costs also arise from regulatory overhead. Gene expression must be regulated if genes and their products are to act
in concert. Biologically plausible models of network growth
require regulator number to scale quadratically with number of genes, in the case of prokaryotes constraining
genomes to encode no more than about 10 000 gene
products (Gagen & Mattick, 2005). Such models imply that
antibiotic-resistance genes of lateral origin should be either
FEMS Microbiol Rev 35 (2011) 707–735
(semi-)autonomously regulated (e.g. as in prophages) or
only weakly connected to the cellular network (Integration
into host regulatory and molecular interaction networks).
The cost–benefit equation extends to the population level.
Genes of lateral origin, and/or those associated with mobile
genetic elements, are more likely than others to specify
proteins that are secreted and modulate cooperative traits.
Nogueira et al. (2009) suggest that laterally mobile elements
such as plasmids, ICEs and temperate phages are so prevalent in bacterial populations because they code for factors
that are ‘powerful generators of microbial social networks’.
These social networks, in turn, can promote the stability of
biofilms (Xavier & Foster, 2007), which, as we have seen, can
be hotspots of lateral transfer and recombination.
Linkage
Antibiotic-resistance determinants are often physically
proximate to genes that specify other selectable traits
including resistance to heavy metals or detergents, transmission between hosts, colonization of substrates or production
of biofilms and can thereby spread in populations even in
the absence of antibiotic use. Given the recombination
frequencies typical of bacteria (Gogarten & Townsend,
2005), simple co-occurrence on a bacterial chromosome
offers considerable linkage. Plasmids likewise often specify
multiple selectable traits, and moreover can be maintained,
in the absence of ongoing selection, in host populations by
stability systems such as PSK (Thomas, 2000; Kroll et al.,
2010). Linkage to other selected traits, together with compensatory mutations that reduce the carrying cost of
resistance, are presumably largely responsible for the maintenance of antibiotic resistance in communities for years
after the use of that antibiotic has ceased (Bean et al., 2005;
Johnsen et al., 2009).
Linkage of multiple selectable traits to virulence genes can
prove particularly problematic, as selection pressure from
different fronts can then drive the spread of virulent clones.
For example, S. pneumoniae serotype 14, commonly responsible for invasive diseases including necrotizing pneumonia
and haemolytic uremic syndrome, has acquired two large
conjugative transposons and a resistance island. The larger
conjugative transposon is a composite of three other transposons, and carries five genes specifying resistance to
chloramphenicol, erythromycin (two genes), streptothricin
and kanamycin. The smaller conjugative transposon is
likewise a composite of two transposons, and carries
genes specifying tetracycline and erythromycin resistance.
Two of 16 other S. pneumoniae genomes contain variants
of both composite transposons, although with some variation in specific gene content. The resistance island carries
another chloramphenicol-resistance gene, a site-specific
recombinase and several IS elements. Further to these three
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regions, the genome encodes penicillin-binding proteins, a
multidrug-resistance efflux pump, two b-lactam-resistance
factors, three metallo-b-lactamases, a bacitracin-resistance
protein, various heavy-metal resistance proteins and a large
number of virulence determinants associated with the
capsule and cell surface, lytic activity, hydrogen peroxide
production, lantibiotic synthesis and other functions. Together, these present a worrisome picture of stepwise lateral
transfer, recombination and gene deletion events combining
to yield a multidrug-resistant, highly virulent pathogen
(Ding et al., 2009).
Other species in which stepwise lateral transfer and
recombination involving different mobile genetic elements
has produced multidrug-resistant, virulent strains include
enterohaemorrhagic E. coli (Venturini et al., 2010) and
Legionella pneumophila (Cazalet et al., 2008; D’Auria
et al., 2010). Stepwise LGT involving multiple plasmids and
integrons has led to the emergence of multidrug resistance
in a clinical isolate of Vibrio fluvialis (Rajpara et al., 2009),
while stepwise acquisition of some 137 genes, including
virulence determinants, has been mediated by phages and
plasmids in the pathogen M. tuberculosis (Veyrier et al.,
2009).
GECs
In LGT and the construction of GECs, we surveyed the
diverse opportunities and barriers that can be differentially
exploited to construct GECs. Here, we consider features of
actual GECs, first adopting a knowledge-driven approach
based on the scientific and medical literature. Although each
report is necessarily local (e.g. to a host–pathogen system,
marker set, vector type, analytical method and/or time
scale) and contingent, from many such fragmentary and
disconnected glimpses we can hope to infer the general
properties of actual exchange communities. Thereafter
(GECs: data-driven approach), we describe data-centric
bioinformatic approaches that aim for a more-synoptic view
of LGT across the biosphere. A third approach, based on
experimental laboratory or field biology, may be possible in
principle, if perhaps impractical at the scale necessary:
leading references are Gamage et al. (2004) for laboratorybased investigation; Sørensen et al. (2005) and Babić et al.
(2008) for direct visualization; and Ragan (2001a), van Elsas
et al. (2003) and Kelly et al. (2009b) for field or mesocosm
studies.
GECs: knowledge-driven approach
GECs can be a single species, strain or clonal
complex in a single type of host
This is the model for the exchange of an antibiotic-resistance
determinant within a bacterial population, and may be
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E. Skippington & M.A. Ragan
combined with selective sweeps (e.g. encounters with antibiotic) that strongly disadvantage nonresistant individuals.
Near-identity of donor and host genome sequences, and of
genomic environment more generally, facilitate homologous
recombination, expression and subsequent regulation (Zawadski et al., 1995; Majewski & Cohan, 1998; Didelot &
Maiden, 2010). As described above, within S. aureus the SauI
restriction–modification system limits exchange of genetic
material across the clonal complexes that comprise the
species as recognized. The limiting case is provided by
certain obligate bacterial symbionts, which, depending on
lifestyle, may only rarely encounter foreign DNA (Bordenstein & Reznikoff, 2005; Moran et al., 2008).
GECs can link different species in a common host
or environment
Antibiotic-resistance plasmids can variously be transferred
among strains of Escherichia, Salmonella, Klebsiella and
Pseudomonas when these bacteria share a common environment (e.g. Gebreyes & Altier, 2002; Schjørring et al., 2008;
Kelly et al., 2009a; Shakibaie et al., 2009). Mathew et al.
(2009) report identical class 1 integron variable regions in
identically sized plasmids of E. coli and Salmonella spp. from
a single swine farm, consistent with recent lateral transfer;
one plasmid confers resistance to streptomycin and spectinomycin, and the other to trimethoprim. Hinnebusch et al.
(2002) describe the transfer of a resistance plasmid from
E. coli to Yersinia pestis in an insect-gut model.
GECs can be a single species or strain living in
diverse hosts and/or environments
In principle, a distinction can be made between an antibiotic-resistant strain that, on the one hand, infects a new
kind of host or colonizes a new environment or, on the
other, transfers a resistance plasmid to a related strain
already established in a different host or environment. As
an example of the former, methicillin-resistant S. aureus
(MRSA) strains can cross-infect humans, domestic animals
and cattle (Juhász-Kaszanyitzky et al., 2007; Monecke
et al., 2007; Brody et al., 2008). Alternatively, virulence
factors can be transferred by a plasmid between hospitalacquired human MRSA and strains of bovine S. aureus
(Brody et al., 2008), and genes are exchanged among
strains of E. coli commensal in humans, animals and birds
(Grasselli et al., 2008; Mellata et al., 2009). Resistance
to vancomycin and to other antibiotics is transferred
readily, in the absence of selective pressure, from porcine to
human strains of Enterococcus faecium during experimental
infection of the mouse intestinal tract (Moubareck et al.,
2003).
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Lateral genetic transfer and genetic exchange communities
GECs can link different genera across diverse hosts
and/or environments
Strains of the Gram-negative bacteria Streptococcus, Enterococcus (formerly a section of Streptococcus), Staphylococcus
and Listeria form exchange communities in various environments. Conjugative plasmids encoding antibiotic resistance can be transferred between Streptococcus and
Enterococcus (Sedgley et al., 2008), from Enterococcus to
Staphylococcus (Noble et al., 1992), from Enterococcus and
Streptococcus to Listeria (Charpentier & Courvalin, 1999;
Zhang et al., 2007), among diverse Listeria (Charpentier &
Courvalin, 1999), and from Listeria to Enterococcus (Bertrand et al., 2005). A tetracycline-resistance plasmid has
moved from a piscine Lactococcus (formerly Streptococcus)
to human Listeria (Guglielmetti et al., 2009). More generally,
genetic material can flow from Enterococcus, Streptococcus
and Staphylococcus into Gram-negative bacteria including
Campylobacter, Escherichia, Haemophilus and Klebsiella
(Courvalin, 1994; Wagner & de la Chaux, 2008).
Conjugatively self-transferrable IncA/C family plasmids
transfer antibiotic resistance efficiently between unrelated
bacteria from different environments. Structurally similar
plasmids have been characterized in the fish pathogens
Aeromonas hydrophila and Photobacterium damselae, agricultural S. enterica and multidrug-resistant V. cholerae and
Yersinia species. In the laboratory, IncA/C plasmids transfer
conjugatively from Pseudomonas putida to diverse marine
bacteria including the phylogenetically distant Planctomyces
maris (Dahlberg et al., 1998), and from E. coli to diverse
Gram-negative (Guiney, 1993) and Gram-positive (TrieuCuot et al., 1987) bacteria and to Saccharomyces cerevisiae
(Heinemann & Sprague, 1989). TraBDF transfer proteins
from IncA/C plasmids have homologues on certain ICEs
from Photobacterium, Proteus, Providencia, Shewanella and
Vibrio species (Fricke et al., 2009). The transfer efficiency of
different IncA/C plasmids can vary over four orders of
magnitude; instances of nontransferability may be due to
chromosomal features or the absence of helper plasmids
(Fricke et al., 2009).
Genetic exchange may moreover allow bacteria to extend
their range of hosts and environments. The plant pathogen
Erwinia carotovora ssp. atroseptica, for example, shares the
common enterobacterial genomic backbone, but has acquired from other plant-associated bacteria numerous genes
that support its plant-pathogenic lifestyle (Toth et al., 2006).
Environments can delimit GECs
Phylogenetic analysis reveals a nonrandom association of
integron integrase gene lineage with the type of environment. All intI genes from soil and freshwater bacteria,
together with those from the IntI1 and IntI3 families of
FEMS Microbiol Rev 35 (2011) 707–735
mobile integrons, form a monophyletic group, whereas all
intI genes from marine environments constitute an older,
paraphyletic assemblage (interestingly, a different outgroup
rooting could render the marine integrases monophyletic as
well). By contrast, taxa map much less cohesively onto the
intI tree, with beta-, gamma- and deltaproteobacterial
sequences admixed (Mazel, 2006).
GECs sometimes cross boundaries of biological
domains
Agrobacterium tumefaciens, commonly considered a plant
pathogen, has a surprisingly broad host range extending well
beyond plants; under laboratory conditions, it can form a
conjugative structure (type IV secretion system), transfer
T-DNA (derived from its Ti plasmid) and plasmid-encoded
virulence proteins and genetically transform the nuclear
genomes of a wide range of eukaryotes including green
plants, S. cerevisiae, filamentous fungi, mushrooms and
cultured human cells (Lacroix et al., 2006). Transformation
of eukaryotes by T-DNA is arguably the clearest example of a
unidirectional transfer mechanism, as eukaryotes are not
known to initiate conjugation with prokaryotes. Likewise,
E. coli can conjugatively transform yeast (Heinemann &
Sprague, 1989; Nishikawa et al., 1992). The intracellular
parasite Wolbachia has transferred much of its genome to
arthropod and nematode nuclei (Dunning Hotopp et al.,
2007), and nuclear genomes of bacteriophagic protozoa
show evidence of recent LGT from bacteria (Gomez-Valero
et al., 2009). Other putative examples of prokaryoteto-eukaryote transfer are summarized by Ragan & Beiko
(2009). Transfer in the other direction, from eukaryote to
bacterium, seems less frequent (Ragan & Beiko, 2009).
Particularly interesting is the abundance of eukaryotic-like
proteins encoded by Legionella genomes; several lines of
circumstantial evidence point to multiple origins by LGT,
presumably via transformation (Gomez-Valero et al., 2009).
GECs: data-driven approach
Since early in the multigenome age, computational analyses
have been applied to multigenome datasets with the aim of
identifying minimal (Mushegian & Koonin, 1996) or universal (Gaasterland & Ragan, 1998b) ORF sets, predicting
protein function (Koonin et al., 1997; Tatusov et al., 1997;
Pellegrini et al., 1999), delineating sets of orthologous genes
(Bansal et al., 1998; Koonin, 2005) or gene regions (Wong &
Ragan, 2008), mapping patterns of gene conservation and
innovation (Gaasterland & Ragan, 1998a; Huynen & Bork,
1998), exploring the origins of eukaryotes (Ragan & Gaasterland, 1998), inferring genome phylogenies (Snel et al.,
1999; Clarke et al., 2002) or dynamics (Dagan & Martin,
2007) and examining the distribution of genomes in the
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biosphere (Chaffron et al., 2010). Typically in studies of this
nature, each genome is treated as a set of gene (or protein)
sequences and these are compared pairwise in all combinations, for example using BLAST (Altschul et al., 1990) or
SSEARCH (Pearson, 1991), to yield a matrix of match scores.
This matrix can be filtered, clustered and/or otherwise
analysed to identify profiles or groups. Groups of sequences
delineated in this way can then be aligned, or the underlying
pairwise match scores used directly, for phylogenomic
inference of gene or protein trees or networks (Beiko et al.,
2005; Ge et al., 2005; Kunin et al., 2005; Dagan et al., 2008).
With certain caveats, conflicting (topologically incongruent)
phylogenetic signals can be interpreted as prima facie
evidence of LGT. The breadth and detail of GECs identified
by these multigenome approaches seem to be limited only
by the input data, and by computability.
The first such multigenome analyses focused only on sets
of chromosomal genes. Kunin et al. (2005) found most gene
flow to follow a tree of vertical inheritance, but with
numerous tiny ‘vines’ of LGT entangling its branches; they
identified species of Bradyrhizobium, Erwinia and Pirellula
as most involved in lateral gene exchange with other
genomes, and the reduced genomes of Chlamydia, Rickettsia
and Treponema as among the least connected. Closely
related organisms were more connected laterally than distant ones, probably because incoming DNA could be
integrated via homologous gene replacement. Beiko et al.
(2005) found that the extent of LGT was usually much
greater within than between high-level taxa; that the Alpha-,
Beta- and Gammaproteobacteria (perhaps not coincidentally
the best-represented genomes in their analysis) were particularly active in gene exchange; and that ecologically
versatile groups such as cyanobacteria, and species such as
P. aeruginosa and Ralstonia solanacearum, were the most
involved in lateral transfer. These investigators identified
substantial exchange between an ancestor of Y. pestis and the
common ancestor of E. coli and Salmonella.
With increasing numbers of genomes available for analysis, data management, computation and visualization have
become more challenging, necessitating tradeoffs between
coverage and resolution. All large-scale analyses so far, for
example, have used genes (or proteins) as the unit of
analysis, although many genes are mosaics of regions with
conflicting evolutionary histories (Chan et al., 2009). In
visualizing their results, Beiko et al. (2005) and Kunin et al.
(2005) aggregate by taxon, thereby losing the details of the
actual GECs. One way forward is to focus on a specific type
of gene, for example transposases that transfer via IS
elements (Hooper et al., 2009). Working with annotations
of bacterial species as generalists (found in multiple habitats) or specialists (aquatic, marine, soil or living in a host),
Hooper and colleagues found that most (but not all) lateral
movement of transposases has taken place within a habitat
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E. Skippington & M.A. Ragan
type, and that generalists often had a narrow range of
exchange partners. Streptococcus pneumoniae and two Bacillus species were identified as bridges between groups of taxa
that do not exchange directly with each other.
The same computational approach has been used to map
exchange networks among vectors. Comparing 47 completely sequenced plasmids from strains of Escherichia, Shigella
and Salmonella, Brilli et al. (2008) found that plasmids
rarely form tight clusters by host species, but instead show
complex evolutionary histories that reflect ‘massive’ LGT
and gene rearrangement. A single GEC may extend across
these three genera, while E. coli and Shigella form a subset
within which certain plasmids and genes are shared uniquely. Transposases are among the most highly connected
proteins. Focusing on antibiotic resistance, Fondi & Fani
(2010) analysed 5030 resistance-associated sequences encoded in 956 plasmids representing 364 organisms and 134
distinct bacterial genera, using a normalized similarity ratio
approach (cf. Clarke et al., 2002) to identify prima facie
instances of LGT. As intrageneric LGT could not confidently
be distinguished from vertical transmission, their unit of
analysis became the bacterial genus. In this analysis, most
plasmid proteins are seen to be connected to the rest of the
network via only a few links; relatively few are highly
connected, and these represent most main functional
classes. More than half of these bridging proteins are from
bacteria (especially Staphylococcus) normally associated with
a eukaryotic host; fewer come from bacteria living in multiple habitats (Corynebacterium, Enterococcus) and the fewest
from soil or water bacteria.
Lima-Mendez et al. (2008) used a similar computational
approach to reconstruct a phage network. In this analysis,
phages were also revealed as extensively mosaic due to
successive LGT. These investigators grouped phage proteins
with similar phylogenetic profiles into evolutionarily cohesive modules; among temperate phages, the proteins in each
of these modules typically contribute to a common function
(e.g. replication), whereas among virulent phages, each
evolutionary module tends to be functionally heterogeneous.
It remained for Halary et al. (2010) to move beyond the
chromosome/vector dichotomy, using pairwise BLAST to
compare 119 381 families representing 98 bacterial and nine
archaeal chromosomes, four protistan nuclear genomes and
165 529 phage, plasmid and environmental virome sequences – 578 527 sequences in all. The resulting similarity
network is partly disconnected: at this level of resolution, it
is not the case that ‘everything talks to everything’. The
network is highly structured by vehicle type: over some
98.5% of the network chromosome is connected to chromosome, plasmid to plasmid or phage to phage: thus, ‘when a
DNA family enters a type of DNA vehicle . . . it mainly
evolves in it’. In general, plasmids, not viruses, mediate LGT
among bacterial chromosomes.
FEMS Microbiol Rev 35 (2011) 707–735
725
Lateral genetic transfer and genetic exchange communities
As the sequence similarity threshold is decreased, networks of increasingly ancient transfers come into view. At
100% identity – the most recent transfers – strains of
Legionella, Xanthomonas and Yersinia share DNA with
plasmids, whereas Streptococcus preferentially exchanges
with phage. At 85% identity, a cluster of plasmids unites
Xanthomonas, Prochlorococcus, Synechococcus, Streptococcus,
Rhodopseudomonas, Burkholderia and Yersinia, while other
plasmids mediate transfer between Burkholderia/Xanthomonas and Streptococcus. In general, DNA tends to reside longterm in plasmids, but short-term in phages.
Halary and colleagues identify 106 central nodes that bridge
regions of the similarity graph that are locally well connected,
but relatively isolated from each other. Most of these central
nodes represent plasmids, i.e. plasmids are key in redistributing genetic material between GECs. These plasmids tend to be
phylogenetic mosaics; many carry drug- and/or metal-resistance determinants that likely contribute to their ability to be
successful in different taxa and/or habitats. What taxa and
habitats do these nodes link? Through the generosity of
Halary and colleagues, we were able to examine the underlying data (E. Skippington & M.A. Ragan, unpublished data).
Among the 60 most central plasmids, 48% are annotated as
resident in Gammaproteobacteria, 32% in Firmicutes, 7% in
other Proteobacteria and 7% in Actinobacteria. Although these
proportions may, to some extent, simply reflect taxon coverage, they also suggest key roles for Proteobacteria and
Firmicutes in acquiring and redistributing genes between
otherwise genetically separate communities.
While most of these highly central nodes are plasmids,
phages and chromosomes are also represented, particularly
at lower sequence-identity thresholds. We introduce the
term central-node neighbourhood to describe the set of nodes
directly connected to a central node (excluding the central
node itself). In this dataset, central nodes that are plasmids
most frequently connect to other plasmids. Of 84 instances
(60 unique plasmids, some neighbourhoods that vary by
identity threshold), more than half have neighbourhoods
composed exclusively of other plasmids. In contrast, phage
and chromosomal central nodes more frequently connect to
a different type of vehicle, although the numbers are small.
Regardless, the Halary and colleagues data contain examples
of plasmid, phage and chromosomal central nodes connected to all other vehicle types: no vehicle type invariably
presents an impermeable barrier to dissemination.
The central-node neighbourhoods in the Halary and
colleagues dataset are taxonomically diverse. Indeed, we find
neighbourhoods that differ at every level of taxonomic rank.
One that differs at the rank of phylum, for example, contains
nodes that all fall within the same domain, but represent
more than one phylum. Of 106 neighbourhoods, only 10
contain nodes that all belong to the same order; of these 10,
only seven contain nodes that all belong to the same family.
FEMS Microbiol Rev 35 (2011) 707–735
Only three neighbourhoods contain nodes that all belong to
the same genus and in only one do all nodes represent the
same species. Thus, even among central nodes that connect
the least diverse neighbourhoods, transfer is almost always
intergeneric or greater. If we accord viruses a separate
domain, 35% of central-node neighbourhoods have members from two or more biological domains. These central
nodes construct exchange communities that are usually
orthogonal to, and inter-relate, accepted taxa.
Summary and prospectus
Given the ubiquity of DNA and phages in natural environments, the diversity of transfer mechanisms and the extent
of the variable gene set in many bacterial genomes, the
microbial biosphere may at some minimal level constitute a
single exchange community. At a finer scale, of course, the
microbial world is highly heterogeneous both with regard to
the groups of bacteria and vectors that exchange at appreciable frequency and the specific genetic material that circulates therein. We began this review by setting out a
framework, based on abstraction of genetic exchange as a
graph, within which criteria for delineating GECs arise
naturally (GECs: conceptual framework and parameters).
We defined a GEC as a set of entities, each of which has over
time both donated genetic material to, and received genetic
material from, every other entity in that GEC, via LGT. This
definition avoids biome-scale GECs while not setting impossibly high evidentiary standards.
At each successive step, LGT offers opportunities and
barriers that can be differentially exploited: DNA uptake,
phage and plasmid host range, plasmid exclusion, restriction–modification and CRISPR/Cas systems, combinatorial
association with systems for transposition and recombination, homologous and illegitimate recombination, gene
silencing, integration into genetic regulatory and biomolecular interaction networks, the cost–benefit equation on
host fitness, genetic linkage with selectable traits and host
population structure. GECs are constructed through the
contingencies and stochasticities of their interplay in dynamic environments.
Much remains to be understood about the number,
structure, dynamics and inter-relationships of GECs as they
exist in natural habitats and in clinical, agricultural and
other settings. To the extent known, GECs can vary widely in
spatial extent, taxonomic diversity, density of internal connectivity and involvement of vector types. Determinants
that may be benign in one part of the GEC may be
pathogenic in another. Large-scale computational analyses
confirm that LGT can be successful at different levels of
granularity, from physically proximate exchange among
closely related strains to long-distance transfer crossing
biological domains. Plasmids are key agents of transfer
2011 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
c
726
within and across many GECs. New DNA sequencing
technologies are poised to increase the available data on real
bacterial communities – both natural and in clinical settings
– by orders of magnitude. From this will arise more
complete maps of the highways, roads, streets and footpaths
of DNA transfer within and among GECs. These maps,
converted to computational models, will guide us toward
being able to reduce or block the spread of antibiotic
resistance and other unwelcome traits.
Acknowledgements
We acknowledge the support of the Australian Research
Council grant CE0348221. E.S. is supported by an Australian Postgraduate Award and a Queensland Government
Smart State PhD Scholarship.
Statement
Re-use of this article is permitted in accordance with the
Term and Conditions set out at http://wileyonlinelibrary.
com/onlineopen#OnlineOpen-Terms
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