plugin-j.1574-6976.2011.00292.x

R E V I E W A R T I C L E

Originsof bacterial diversity through horizontal genetictransferandadaptation tonewecological nichesJane Wiedenbeck & Frederick M. Cohan

Department of Biology, Wesleyan University, Middletown, CT, USA

Correspondence: Frederick M. Cohan,

Department of Biology, Wesleyan University,

Middletown, CT 06459-0170, USA.

Tel.: 11 860 685 3482;

fax: 11 860 685 3279;

e-mail: [email protected]

Received 9 November 2010; accepted 15 June

2011.

Final version published online July 2011.

DOI:10.1111/j.1574-6976.2011.00292.x

Editor: Fernando Baquero

Keywords

species concept; niche-transcending

adaptation; ecotype; evolution; genome

content; amelioration.

Abstract

Horizontal genetic transfer (HGT) has played an important role in bacterial

evolution at least since the origins of the bacterial divisions, and HGT still

facilitates the origins of bacterial diversity, including diversity based on antibiotic

resistance. Adaptive HGT is aided by unique features of genetic exchange in

bacteria such as the promiscuity of genetic exchange and the shortness of segments

transferred. Genetic exchange rates are limited by the genetic and ecological

similarity of organisms. Adaptive transfer of genes is limited to those that can be

transferred as a functional unit, provide a niche-transcending adaptation, and are

compatible with the architecture and physiology of other organisms. Horizontally

transferred adaptations may bring about fitness costs, and natural selection may

ameliorate these costs. The origins of ecological diversity can be analyzed by

comparing the genomes of recently divergent, ecologically distinct populations,

which can be discovered as sequence clusters. Such genome comparisons demon-

strate the importance of HGT in ecological diversification. Newly divergent

populations cannot be discovered as sequence clusters when their ecological

differences are coded by plasmids, as is often the case for antibiotic resistance; the

discovery of such populations requires a screen for plasmid-coded functions.

Introduction

The evolution of bacteria is not just the evolution of animals

and plants writ small. The origin of bacterial species is

accelerated by unique features of bacterial genetics, perhaps

the most important being the ability of bacteria to readily

acquire genes from other organisms (Ochman & Davalos,

2006; Cohan & Koeppel, 2008). Fully sequenced genomes

reveal that a substantial fraction of ORFs have been horizon-

tally transferred (Nakamura et al., 2004; McDaniel et al., 2010),

and many of these acquisitions are thought to have driven the

origins of new bacterial species (Gogarten et al., 2002).

Early evidence of the importance of horizontal genetic

transfer (HGT) in bacterial evolution was seen in the spread

of penicillin resistance through plasmid transfer across the

Enterobacteriaceae (Datta & Kontomichalou, 1965). Bacterio-

logists might have interpreted this rapid spread of resistance

as evidence for the general importance of HGT in bacterial

evolution, but the extent and impact of HGT were not fully

appreciated until much later. With eventual access to

genome sequences, it became clear that HGT occurs

throughout the genome, and has been responsible for the

origins of extremely diverse adaptations (Ochman et al.,

2000). Moreover, HGT has played a role in bacterial evolu-

tion at least since the origins of the bacterial divisions

(Gogarten et al., 2002). For example, methanotrophs

acquired the ability to synthesize some of the cofactors

for methane utilization from methanogenic Archaea

(Chistoserdova et al., 1998; Gogarten et al., 2002).

More recent HGT events have resulted in important

ecological differences between closely related species and

between populations within a single recognized species

taxon (Welch et al., 2002). For example, the virulence factors

that distinguish Salmonella from Escherichia coli were largely

acquired by HGT (Groisman & Ochman, 1996; Gogarten

et al., 2002). Also, antibiotic resistance factors coded on

plasmids distinguish extremely close relatives within recog-

nized species taxa. For many bacterial pathogens, the

acquisition of antibiotic resistance is likely necessary to

survive in the ecological niche of agricultural animals and

humans frequently treated by antibiotics (O’Brien, 2002).

FEMS Microbiol Rev 35 (2011) 957–976 c� 2011 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

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mailto:[email protected]

Through HGT, divergent populations can share an adap-

tation whose value transcends their differences in physio-

logical capabilities, cellular structures, and ecological niches.

This sharing of niche-transcending genes does not generally

make the two populations more similar ecologically. Instead,

HGT allows the recipient to build on its unique, pre-existing

adaptations to either invade a new niche or to improve its

performance in its current niche (Cohan & Koeppel, 2008).

For example, enterotoxigenic E. coli, which attacks the

epithelial cells of the small intestine, has shared the Class 5

fimbriae by HGT with Burkholderia cepacia (Anantha et al.,

2004), which can reside in human lungs of cystic fibrosis

patients, attacking the respiratory epithelium. Acquiring

these niche-transcending genes thus allows each lineage to

better attack cells of its respective niche, but does not cause

the donor and the recipient to converge ecologically (Cohan

& Koeppel, 2008; Cohan, 2011). Likewise, when ecologically

disparate human pathogens acquire the same antibiotic

resistance factors by HGT (Fondi & Fani, 2010), their

ecological niches are not converging beyond their response

to natural selection by antibiotics.

Here, we will review the properties of bacteria that make

HGT so effective in fostering adaptations and the origins of

new ecological populations. We will review the classes of

adaptations most and least likely to be delivered by HGT,

and the ecological and phylogenetic limits on genetic

transfer. Also, we will review the evolutionary challenges on

the recipient to accommodate horizontally acquired adapta-

tions. Finally, we will review evidence from genome content

comparisons that demonstrate a prominent role of HGT in

the origins of bacterial diversity, and how genome compar-

isons have led to the discovery of the ecological dimensions

by which bacterial divergence occurs.

The qualities of bacterial geneticexchange that foster adaptive HGT

Exchange of genes in bacteria is both rare and promiscuous.

A broad survey of recombination rates has shown that

recombination, even among close relatives, occurs at a per

capita per gene rate that is generally close to the rate of

mutation, and rarely more than about 10 times the rate of

mutation (Vos & Didelot, 2009). The rarity of recombina-

tion on a per capita basis does not prevent acquisition of

adaptations from other organisms, as the enormous popula-

tion sizes of many bacteria can bring unlikely recombination

events within reach (Levin & Bergstrom, 2000). Moreover,

recombination in bacteria is too rare to hinder the ecological

diversification of closely related populations. This is because

natural selection against maladaptive, niche-specifying

genes (genes that are adaptive only in the context of their

home population) from other populations easily keeps these

foreign genes at negligible frequencies (Cohan, 1994; Cohan

& Koeppel, 2008) (Box 1).

Bacterial recombination is much more promiscuous than

recombination in higher organisms, where it is always

limited to very close relatives (usually members of the same

species or very closely related species) (Mallet et al., 2007;

Mallet, 2008). Bacterial recombination can extend across the

bacterial divisions and even across the three domains of life

(Garcia-Vallve et al., 2000; Rest & Mindell, 2003). Thus,

bacterial recombination can foster the acquisition of adap-

tations from both close and distant relatives.

Transferred DNA is generally short, often the length of

one to several genes. A short length helps to enable the

success of adaptive HGT between deeply divergent bacteria,

as it allows a recipient to pick up a niche-transcending gene

(or set of genes) without also acquiring the niche-specifying

Box 1. Why recombination does not hinder the adaptive divergence

of bacterial populations

Haldane long ago showed why the rare introduction of niche-

specifying alleles from one population to another cannot reverse the

adaptive divergence between populations: the equilibrium frequency

(p�) of a maladaptive, niche-specifying allele from another population is

equal to its rate of entry into the population (m) divided by the selection

intensity disfavoring the foreign allele (s), i.e. p�= m/s (Haldane, 1932).

In the case of bacteria, the rate of entry of another population’s allele is

equal to the rate of recombination between populations (cb) (Cohan,

1994). A broad survey of recombination rates in the Bacteria and

Archaea showed that recombination generally occurs at about the

same rate as mutation and never greater than about 10 times the rate

of mutation (per gene per generation) (Vos & Didelot, 2009), which is

about 10�6 per gene per generation (Drake, 2009). This analysis of

recombination rates took special care to estimate only recombination

rates among closest relatives, and so the recombination rates may be

taken as estimates of the rate of recombination within populations (cw).

Thus, the estimate of recombination rate at 10�6 per gene per

generation may be taken as an upper limit for the rate of recombination

between populations (cb � cw). If foreign alleles are disfavored even by

weak selection, for example around 10�2, the equilibrium frequency of

foreign alleles would be negligible, in this case around p�= cb/s � cw/s,

or 10�6/10�2 = 10�4.

Thus, even if recombination between populations is occurring at as

high a rate as recombination within populations, each population will

be able to hold onto its respective combinations of genes, which adapt

it to its respective way of making a living. Thus, adaptive divergence in

bacteria does not require sexual isolation (Cohan, 1994; Cohan &

Koeppel, 2008). This argument has been ignored by workers claiming a

central role for recombination and sexual isolation in bacterial

divergence (Fraser et al., 2007; Sheppard et al., 2008), but the

argument has never been refuted.

Nevertheless, recombination is an important force fostering adaptive

evolution in bacteria. Recombination of niche-transcending genes has

been shown to be an important means of introduction of adaptations

into bacterial populations. The difference is that maladaptive

recombination of niche-specifying genes can be rejected by natural

selection, but rare, adaptive introductions of niche-transcending genes

are amplified by natural selection.

FEMS Microbiol Rev 35 (2011) 957–976c� 2011 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

958 J. Wiedenbeck & F.M. Cohan

genes of the donor, which would be maladaptive in the

recipient (Zawadzki & Cohan, 1995; Cohan & Koeppel,

2008). This is in contrast to adaptive introgression in

animals and plants. Because eukaryotic meiosis involves a

50 : 50 mix of the two parents’ genomes, several generations

of backcrossing are required to yield a pure transfer of niche-

transcending genes across species (Rieseberg et al., 2003); in

bacteria, a set of niche-transcending genes can be trans-

ferred, without other genes, in a single transfer event

(Cohan, 2001).

Donor--recipient similarities required forgenetic transfer

The promiscuity of bacteria and the opportunity to transfer

adaptations across taxa are not absolute. Certain shared

characteristics between donor and recipient are required for

transfer to take place (Cohan, 2002). In some cases, genetic

exchange takes place through a plasmid or a phage vector,

whereby the vector incorporates genetic material from one

host, the vector then infects another host, and the genetic

material becomes recombined into the new host (Majewski,

2001). Clearly, vector-based genetic exchange between bac-

teria can take place only if the bacteria share their vectors

(Cohan, 2002). The host ranges of phage and plasmids are

generally narrow, but the host ranges of certain phage

extend across bacterial genera (Jensen et al., 1998), and

some plasmids have extremely broad host ranges that extend

across bacterial divisions (Norman et al., 2009).

Sharing of vectors between hosts is challenged by bacterial

restriction–modification systems. Many bacteria are

equipped with a restriction endonuclease that recognizes

one specific DNA sequence, usually a palindromic tetramer

(e.g. GGCC), hexamer, or octamer; these bacteria are also

equipped with a modification enzyme that methylates the

recognition sequence and thereby protects the cell’s DNA

from its own restriction enzyme (Weiserova & Ryu, 2008).

Thus, bacteria that share the same restriction–modification

system can more easily share a phage or a plasmid, whereas

bacteria with different restriction–modification systems will

effectively digest the DNA of one another’s vectors (Jeltsch,

2003; Budroni et al., 2011). Nevertheless, short plasmid and

phage genomes may be shared across bacteria differing in

their restriction–modification systems, as short vectors are

less likely to contain a given recognition sequence and are

thereby more protected from cleavage (Lacks & Springhorn,

1984). Even some larger plasmids, including the broad-host-

range Inc-P1 plasmids, are able to defend against restriction

endonucleases by removing restriction target sites from their

sequences (Wilkins et al., 1996), whereas others simulate the

host modification system, protecting their sequences from

cleavage (Kruger & Bickle, 1983), thus contributing to their

promiscuity.

In some taxa (e.g. Bacillus and Neisseria), genetic

exchange can occur by transformation, through uptake of

naked DNA from the environment. Most transforming

bacteria are able to take up DNA indiscriminately (Lorenz

& Wackernagel, 1994), but some species are selective about

the DNA sequences that are allowed to cross the membrane.

For example, in Neisseria gonorrhoeae, successful transfor-

mation requires a 10-bp uptake sequence that occurs

abundantly in the genome of this species taxon. The

requirement for this uptake sequence effectively prevents

genetic transfer from divergent organisms (Hamilton &

Dillard, 2006).

Once present in the cytoplasm, transferred DNA must

ensure its replication so that it is not lost. For DNA that

enters a recipient cell on a plasmid, replication may occur by

simply remaining on the plasmid, as seen in many plasmids

with antibiotic resistance factors (Fondi & Fani, 2010).

Alternatively, if transferred DNA is contained within a pair

of insertion sequences on a plasmid or a phage, the DNA

flanked by the insertion sequences may replicate and then

transfer to the recipient chromosome, without a need for

homology between donor and recipient DNA (Vo et al.,

2010).

Otherwise, entering DNA may integrate into the recipient

chromosome through homologous recombination. This

requires a ‘minimum efficient processing segment’ (MEPS)

consisting of near-identical sequences of at least 25 bp at one

or both ends of a donor segment (Shen & Huang, 1986;

Majewski & Cohan, 1999). The requirement of near identity

limits the divergence of genetic material transferred through

homologous recombination, effectively allowing only close

relatives (with o 20% nucleotide divergence) to exchange

genes in this manner (Majewski & Cohan, 1999). The MEPS

requirement has resulted in an exponential decay in the

recombination rate as sequence divergence increases, as seen

in Bacillus (Roberts & Cohan, 1993; Zawadzki et al., 1995;

Majewski & Cohan, 1999), Escherichia (Vulic et al., 1997,

1999), and Streptococcus (Majewski et al., 2000a), although

requirements of near identity may be relaxed in hypermu-

tator strains lacking mismatch repair (Matic et al., 2000).

The requirement for near identity in homologous recom-

bination is expected to reduce the transfer of homologous

adaptations, such as penicillin resistance in Streptococcus,

which occurs through a mutational change in the target

protein (the so-called ‘penicillin-binding protein’) (May-

nard Smith et al., 1991). Resistance alleles have been

transferred within the genus through homologous recombi-

nation (Maynard Smith et al., 1991; Hoffman-Roberts

et al., 2005; Barlow, 2009), and presumably, the rate of

transfer across species taxa was reduced by their sequence

divergence.

Sequence divergence of shared genes may also limit the

transfer of heterologous genes. This is because homologous


959Origins of diversity through horizontal transfer

recombination can facilitate the transfer of heterologous

genes, particularly when a heterologous gene is sandwiched

between homologous genes shared by the donor and the

recipient [i.e. homology-facilitated illegitimate recombina-

tion (HFIR)] (de Vries & Wackernagel, 2002). Provided that

a donor segment’s end sequences are homologous with

recipient sequences and they pass the MEPS criterion, any

genetic material sandwiched between the end segments may

be cotransferred (Majewski & Cohan, 1999).

While acquisition of novel genes by HGT is possible

between extremely divergent organisms, HGT occurs much

more frequently between close relatives than between more

distant relatives. For example, in the gammaproteobacter-

ium Legionella pneumophila, the number of genes acquired

by HGT events from a donor taxon is correlated with the

phylogenetic distance (based on 16S rRNA gene divergence)

between L. pneumophila and the donor (Coscolla et al.,

2011). More broadly, a recent analysis of 657 sequenced

prokaryotic genomes clearly showed that HGT events are

much more common among close relatives than distant

relatives (Popa et al., 2011) (Fig. 1).

A close phylogenetic relationship fosters HGT in at least

two ways. First, close relatives will have greater sequence

identity in their shared genes, thus increasing their rate of

homologous recombination (Zawadzki et al., 1995; Vulic

et al., 1997; Majewski & Cohan, 1998, 1999; Majewski et al.,

2000b), as well as HFIR (de Vries & Wackernagel, 2002).

Second, close relatives are more likely to share the same

habitat than distant relatives; even members of different

families, but the same order are more likely to share habitats

than more distant relatives (Philippot et al., 2010). Because

organisms can exchange genes only when they are in the

same habitat (Matte-Tailliez et al., 2002), phylogenetic

closeness tends to promote genetic exchange through a

sharing of environments.

What adaptations can and cannot bewidely transferred?

Beyond the requirement of similarity between donor and

recipient, there are limitations on the kinds of adaptations

that can be transferred. The various genes must fit on a

chromosomal segment short enough to be transferred or

they must fit on a mobile extrachromosomal element; also,

the set of genes must be functional when it arrives in a new

organism (Lawrence, 1999). The selfish operon model pre-

dicts that natural selection will favor the contiguous ar-

rangement of a functionally related set of genes as an

operon, enabling the gene set to be successfully transferred

as a single, functional element across taxa (Lawrence &

Roth, 1996; Lawrence, 1997, 1999). In this model, natural

selection acts at the level of the operon, preserving the

transferability of the unit, such that an operon can spread

widely across the bacteria. Indeed, certain operons have

passed between organisms at a high rate (Homma et al.,

2007).

An operon may expand to include more functions

through HGT acquisition of additional genes (Homma

et al., 2007); when this occurs, it is generally the first step in

the pathway that is gained, such as the gaining of the fucP

gene in the fucPIKUR operon in E. coli (for the digestion of

fucose) (Pal et al., 2005). In addition, regulators of newly

added genes may be gained along with the genes they control

(Price et al., 2008). The co-incorporation of newly acquired

protein-coding genes along with their regulators may in-

crease the successful transferability of an enlarged operon

(Price et al., 2008).

Transferable gene clusters often contain a set of genes that

are all involved in processing a single resource molecule.

This is exemplified by the lac operon, providing inducible

metabolism of environmental lactose (Jacob & Monod,

1961). The lac operon includes protein-coding genes for a

lactose-binding repressor, which shuts down the operon in

the absence of lactose; a lactose permease, allowing for the

uptake of lactose; and two proteins for catabolizing lactose

in different directions: one that cleaves lactose to its con-

stituent monosaccharides and another that transacetylates

lactose. Another motif for a transferable cluster is an entire

biochemical pathway, for example the full synthetic pathway

for cytochrome c biogenesis (Goldman & Kranz, 1998).

0.5

0.6

0.7

Donor-recipient pairs

Disconnected pairs

0.0

0.1

0.2

0.3

0.4

Pro

port

ion

of s

peci

es p

airs

0.1 1 10 100Genome sequence similarity (%)

Fig. 1. The relationship between genome sequence similarity and the

proportion of species pairs that engaged in HGT. The ‘donor–recipient’

pairs represent the pairs of species that were connected by at least one

HGT event, based on 32 028 HGT events that could be attributed to a

recipient and a donor organism, among 657 sequenced prokaryotic

genomes; ‘disconnected pairs’ represent those species pairs that did not

show evidence of any HGTevents. The decrease in the number of species

pairs apparently engaging in HGT among closest relatives (from 50% to

100% genome sequence similarity) was attributed to the difficulty in

identifying HGT events among close relatives (Popa et al., 2011) (used

with permission from Cold Spring Harbor Laboratory Press).



In both of these cases, a contiguous set of genes constitutes a

transferable adaptation.

Multiple antibiotic resistance genes are frequently ar-

ranged in clusters on plasmids (Gomez-Lus, 1998; Barlow,

2009). While not strictly operons in the sense that the

various contiguously arranged antimicrobial-resistance

genes on a plasmid may be regulated independently

(Gomez-Lus, 1998), the contiguous organization of such

gene sets can be explained through an extension of the

selfish operon theory. The association of different antimi-

crobial resistance genes may contribute to a single, selectable

function. Carrying resistance for multiple antibiotics is a

highly successful strategy for human pathogens likely to be

targeted by multiantibiotic therapy, either simultaneously or

in a cycling regimen (Lawrence, 2000; Summers, 2006).

Surveys of the genes and adaptations that have been most

and least frequently transferred demonstrate some of these

principles of transferability. In a survey of completely

sequenced genomes, the majority of genes identified as

recently transferred were classified into four functional

categories: plasmid, phage, and transposon functions; cell

envelope functions; regulatory functions; and cellular pro-

cesses (Nakamura et al., 2004). Within the cell envelope and

regulatory function categories, commonly transferred genes

include those encoding cell surface structures, biosynthesis

and degradation of surface polysaccharides, and DNA inter-

actions. Frequently transferred cell surface proteins may be

understood as niche-transcending adaptations, as they are

often the targets of immune systems, and a newly acquired

surface protein may allow immune escape (Stein et al.,

2010). The most commonly transferred cellular process

genes were those involved in DNA transformation, patho-

genesis, toxin production, and resistance (Nakamura et al.,

2004). In the case of genes coding for pathogenesis, toxin

production, and resistance, we may interpret their transfer-

ability in terms of providing niche-transcending adaptations

for invading new environments or for resisting new chal-

lenges appearing in an organism’s present environment,

such as antibiotics.

The survey by Nakamura et al. (2004) also showed that

plasmid, phage, and transposon functions comprised nearly

a third of the transferred genes. Many of these transferred

genes are caused by infection by these subcellular particles

and provide no adaptation for bacteria. In one special case,

bacteria can acquire defense against phage through HGT of

gene segments from phage that have infected them; this is

the modus operandi of the clustered regularly interspaced

short repeats (CRISPR) system (Marraffini & Sontheimer,

2008) (Box 2). CRISPR units are comprised of a series of

short repeats separated by spacer sequences that are derived

most often from bacteriophage or plasmids (Bolotin et al.,

2005; Mojica et al., 2005; Pourcel et al., 2005), and provide

defense against the phages or plasmids from which they are

derived (Barrangou et al., 2007). The CRISPR loci are

transcribed into RNA segments, which are cleaved into

smaller units that each target the complementary sequence

on the phage (Brouns et al., 2008) by base-pairing with

either the phage’s DNA (Marraffini & Sontheimer, 2008) or

RNA (Hale et al., 2009). Thus, the CRISPR system repre-

sents a form of acquired defense, where infection of a

bacterium by a phage may be followed by the incorporation

of phage DNA into new spacer regions of the CRISPR

system, thereby providing defense against related phage in

the future (Sorek et al., 2008; van der Oost et al., 2009;

Horvath & Barrangou, 2010).

Genes that have been acquired by a particular lineage

through HGT cannot be assumed to be adaptations, and the

adaptive value of an acquired gene should be confirmed, for

example by showing that it is expressed under natural

conditions in a way that is consistent with its likely adaptive

value (Steunou et al., 2008). Acquired genes are most likely

to be nonadaptive ‘craters’ that have fallen onto the genome

under a variety of circumstances, particularly if the genes

were newly acquired. One possibility, which genome ana-

lyses abundantly support, is that many transferred genes

have entered the genome nonadaptively with invading

phage or transposons (Nakamura et al., 2004). Another

Box 2. Evolution of the CRISPR system through HGT

CRISPR modules are an acquired immunity defense of prokaryotes

against phage and plasmids, and are possessed by most Archaea and

many Bacteria (Makarova et al., 2011). Each CRISPR module within a

genome contains a number of spacer regions, each derived from the

sequence of a previously infecting phage or plasmid through HGT,

and the spacer regions are separated by repeat regions. Resistance to

phages through the CRISPR system is very precise. If the spacer

sequences are not a 100% match to the phage, the phage may

retain the ability to infect the bacterium (Barrangou et al., 2007).

Thus, the incorporation of spacer sequences into CRISPR loci

contributes to the evolutionary arms race between the bacterium

and the phage, where selective pressure imposed on the phage

through the CRISPR system may drive high evolutionary rates in the

phage (Sorek et al., 2008). Spacer regions in CRISPR loci are also

rapidly gained and lost – the most newly acquired spacer regions are

often unique to individual bacterial isolates (Pourcel et al., 2005).

Large-scale evolutionary changes have also been noted in CRISPR

systems. Although sequences of repeats in CRISPR loci vary among

different bacterial species, there are occurrences of unexpectedly

similar repeat sequences between diverse bacteria (Makarova et al.,

2002; Haft et al., 2005; Godde & Bickerton, 2006). These similarities

point to the propagation of CRISPR systems by HGT. The transfer of

CRISPR systems is hypothesized to be mediated by megaplasmids

(plasmids 4 40 kb in size), as CRISPR loci in some genomes are

located on megaplasmids rather than on the bacterial chromosome

(Godde & Bickerton, 2006). The HGTof CRISPR loci may have helped

to establish the widespread presence of CRISPR systems in bacteria

[�40% of bacterial genomes contain CRISPR loci (Godde &

Bickerton, 2006)], and indicates the importance of the CRISPR

system for phage defense.



possibility is that a nonadaptive, transferred gene has

randomly reached a high frequency within a population or

a taxon by genetic drift or by periodic selection. Finally, a

gene may have been adaptive in the past, but is no longer

adaptive. This could be the case for an antibiotic resistance

gene that was adaptive when a particular antibiotic was

applied, but in the absence of the antibiotic, the gene is no

longer useful.

Among the cellular processes that are transferred the least

are informational genes, involved in translation and tran-

scription (Jain et al., 1999; Nakamura et al., 2004). Within E.

coli in particular, Davids & Zhang (2008) found genes

encoding for informational processes to be dominated by

core genes shared by all E. coli strains, with no significant

contribution from HGT. The low rates of transfer of

informational genes [as well as photosynthetic genes (Aris-

Brosou, 2005)] can be understood most generally by Riedl’s

‘burden’ hypothesis (Riedl, 1978). The burden hypothesis

predicts (for animal evolution) that organs burdened by

connections to many other organs would be slow to evolve;

their burden of complex interactions with other functional

units would make their evolution difficult. Aris-Brosou

(2005) has argued that the property of burden (or connec-

tivity) that makes adaptive evolution difficult for complex

structures also renders their HGT unlikely to succeed. More

specifically, the burden hypothesis predicts a low rate of

transfer of genes coding for parts of the transcription and

translation machinery, as these functions are burdened with

many complex interactions; the transfer of only one part of a

complex set of co-adapted structures would likely bring

about an incompatibility and loss of function (Jain et al.,

1999). The degree of burden, and thus the predicted

resistance to the transfer of a gene, may be approximated

by the number of protein–protein interactions of the gene’s

product (Wellner et al., 2007; Price et al., 2008). This

measure of a protein’s connectivity has been shown to be

an important factor for the transferability of genes across all

functional categories, in both ancient and recent transfers

(Cohen et al., 2011) (Fig. 2).

Although uncommon, the transfer of highly connected

informational genes can occur (e.g. the transfer of an rRNA

operon or a gene encoding a ribosomal protein) (Gogarten

et al., 2002; Lind et al., 2010). The burden hypothesis (or the

‘complexity’ hypothesis) predicts that for informational

genes to be successfully transferred, the recipient and the

donor should be closely related. As predicted, the fitness

costs of transferred ribosomal genes in Salmonella in experi-

mental manipulations were generally much higher from

distant relatives than from close relatives (Lind et al., 2010).

In contrast, the most phylogenetically distant transfers, such

as those between the Bacteria and the Archaea domains,

almost always involve metabolic genes (Kanhere & Vingron,

2009). This pattern is consistent with informational genes

being incompatible with the physiology and structures of

any but the most closely related organisms.

Among the most complex structures that show no

evidence of HGT are features of cell architecture, which are

profoundly different across the most anciently divergent

bacterial taxa (Cohan & Koeppel, 2008; Cohan, 2010). The

prokaryotic domains of Bacteria and Archaea differ funda-

mentally in the structure of their cell membranes, owing to

their use of ester vs. ether linkages in their membranes’ fatty

acids. Some Archaean lineages build upon the ether linkage

to produce a tetraether diglycerol structure yielding a lipid

monolayer. This monolayer structure is of great ecological

importance to hyperthermophiles, as the monolayer does

not peel apart at high temperatures, in contrast to the lipid

bilayer of Bacteria (Madigan et al., 2009). Thus, this

profound structural difference between these domains is at

least partly responsible for the ecological success of Archaea

at extremely high temperatures. While this architecture-

based ability to resist high temperature might be of value

for some Bacterial lineages, such differences in the ‘bau-

plans’ of cells, however adaptive, are not likely to be

packaged and transferred with success (Cohan & Koeppel,

2008). Because the lipid monolayer membrane interacts

with many aspects of cell physiology, including the function

of many trans-membrane proteins, transfer of such an

adaptation is unlikely, even if it were succinctly packaged as

a string of genes on a plasmid.

Fig. 2. The relationship between the number of HGT events and the

number of protein–protein interactions. Each dot represents a gene

family, and the number of protein–protein interactions for a given family

is quantified as the number of other gene families with which the given

family interacts. The negative relationship between the number of HGT

events and the protein–protein interactions was quantified with a Spear-

man correlation coefficient of �0.422, with Po 8.18�10�105 (Cohen

et al., 2011) (used with permission from Oxford University Press).



Other nontransferable adaptations include architectural

structures of the Firmicutes and the Planctomyces. The

Firmicutes are built with an extremely strong cell wall,

containing multiple layers of cross-linked peptidoglycan

(Madigan et al., 2009), conferring effective and constitutive

resistance to osmotic stress (Schimel et al., 2007). The gram-

positive cell wall thus confers to the Firmicutes the ability to

thrive in drought-prone environments, which can deliver an

osmotic shock by rapidly re-wetting the cell. The Plancto-

myces build a stalked morphology, unique in the bacterial

world, which lifts the cell above the surface of sediment and

brings the cell within reach of much higher levels of

nutrients (Lawler & Brun, 2007). In all these cases, bau-

plan-level adaptations would in principle be gratefully

received by other lineages for their ecological advantages,

but they have not been transferred, presumably because such

adaptations would integrate into the existing physiology and

development of a recipient cell only with extreme difficulty,

if at all (Cohan & Koeppel, 2008; Cohan, 2010; Philippot

et al., 2010).

Accommodation of transferredadaptations

While a set of genes acquired through HGT may provide an

important adaptation to a recipient, the transfer may also

incur harmful pleiotropic effects (Cohan et al., 1994; No-

gueira et al., 2009). In the case of animals and plants, large

genetic changes often disrupt the organism’s physiology and

development (Fisher, 1958), and we should likewise expect

HGT events to be disruptive in bacteria. It is easy to imagine

that the expression of novel genes would disrupt existing

physiology, but even acquiring an adaptive allele by homo-

logous recombination could disrupt the smooth functioning

of a cell, especially in the case of antibiotic resistance alleles.

This is because many antibiotics target informational pro-

teins, which are difficult to alter, as we have discussed. Thus,

many resistance-conferring mutations in targeted informa-

tion molecules bear a severe cost to the growth rate and

competitiveness of a cell (Cohan et al., 1994; Andersson &

Levin, 1999; Levin et al., 2000).

The deleterious side effects of a new adaptation can drive

natural selection toward ‘domesticating’ the adaptation, that

is, toward ameliorating its negative fitness effects. One mode

of ameliorating the cost of a new adaptation is through

compensatory evolution, whereby natural selection favors

modifiers at other loci that compensate for the deleterious

effects of the adaptation (Cohan et al., 1994; Andersson &

Levin, 1999). These other loci are expected to code for

functions that interact with the new adaptation. For exam-

ple, a costly streptomycin-resistance allele of the S12 riboso-

mal protein locus can be ameliorated by compensatory

mutations in the L19 locus, coding for another ribosomal

protein (Maisnier-Patin et al., 2007).

The potential for compensatory evolution may be eval-

uated by observing spontaneous evolution among the

descendants of a cell that has integrated an adaptation

through HGT. This reveals the potential of new mutations

in the genome’s already-existing genes to ameliorate an

adaptation’s harmful effects (Andersson & Levin, 1999;

Levin et al., 2000). Alternatively, one may place an adaptive

gene into different genetic backgrounds within a species

taxon and observe the extent to which the fitness effect of the

adaptive gene varies between genetic backgrounds (Cohan

et al., 1994). This provides an estimate of the potential for a

species’ standing genetic variation to ameliorate the adapta-

tion. For example, placing rifampicin-resistance alleles of

the rpoB locus into different strains of Bacillus subtilis

yielded a large range of fitness costs across different genetic

backgrounds (Cohan et al., 1994).

Amelioration can also occur through change in the

adaptation itself. Newly acquired genes have higher rates of

evolution than other genes in the genome (Daubin & Och-

man, 2004; Hao & Golding, 2006; Marri et al., 2007; Davids

& Zhang, 2008). For example, Hao & Golding (2006) found

that within the Bacillaceae, recently transferred genes had

longer tree lengths and higher Ka/Ks ratios than native genes,

illustrating rapid evolution. As transferred genes persist in

the genome, the Ka/Ks ratio decreases, suggesting adaptation

of the new genes to the new environment of the host (Hao &

Golding, 2006). Additionally, horizontally transferred genes

are more likely to be regulated by multiple factors; this

complex regulation is believed to evolve after the genes are

transferred (Price et al., 2008).

Another mechanism for domesticating a horizontally

acquired adaptation involves altering the amount of protein

product of the transferred gene, by either gene amplification

or gene repression. In full-genome studies of paralogous

genes, it has been shown that genes newly acquired by

the genome are more likely to undergo gene duplication

(Hooper & Berg, 2003a). Although the reason for the

duplication of horizontally transferred genes is not well

established, it is thought that gene duplication functions to

amplify the amount of gene product for genes with subopti-

mal levels of protein product, as genes with large amounts of

protein product are rarely duplicated (Hooper & Berg, 2003b;

Lind et al., 2010). Gene amplification may be especially

important when HGT results in the homologous replacement

of essential genes. For example, Lind et al. (2010) found

that in Salmonella typhimurium, gene amplification was

common following the homologous replacement of riboso-

mal genes from a variety of sources. The growth rates of

these mutants with gene amplifications were significantly

higher than those without, indicating that the gene duplica-

tion, in this case, served as amelioration following HGT.



Amelioration can also occur by way of gene repression.

Horizontally acquired sequences are often initially repressed

in the host genome by histone-like nucleoid-structuring

proteins (H-NS) (Schechter et al., 2003; Dorman, 2004,

2009; Oshima et al., 2006). H-NS appears to have the ability

to repress horizontally transferred DNA by recognizing

sequences with an aberrant GC content (Dorman, 2009). In

some strains, upwards of 40% of horizontally transferred

genes are associated with H-NS proteins (Lucchini et al.,

2006). In the case of Salmonella enterica, an Hns-like gene on

a plasmid expresses H-NS-like proteins that target horizon-

tally acquired genes only, allowing the silencing of horizon-

tally transferred genes upon transfer (Beloin et al., 2003;

Doyle et al., 2007; Banos et al., 2009).

The initial silencing of transferred genes may foster the

incorporation and domestication of transferred DNA into

the genome, as repression serves to limit the fitness costs of

expression of the novel DNA on the host cell (Dorman,

2007; Banos et al., 2009). Dorman has argued that this initial

silencing can be lessened over time as compensatory changes

occur, allowing a potential benefit of a transferred gene

while minimizing its disruptive effects (Dorman, 2009).

Finally, one other form of amelioration involves changes

in an acquired set of genes to fit the nucleotide composition

of the host genome. Organisms in different taxa above the

genus level frequently are different in their frequencies of

single nucleotides, dinucleotides, trinucleotides, and so on.

The compositional differences between a donor segment

and the recipient are diminished over time as incorporated

genes are subjected to the host’s pattern of mutation

(Lawrence & Ochman, 1998), as well as natural selection to

better integrate the acquired genes with host genome

regulation (Lercher & Pal, 2008).

HGTand ecological divergence amongclose relatives

How did the bacterial world diversify from a single common

ancestor to the hundred or more divisions of today, repre-

senting profoundly different cell architectures, metabolisms,

and ecologies? While the full answer is buried in the vast

expanses of time, we can approach a partial answer by

investigating a much more modest process, that of specia-

tion, in which one lineage splits into two irreversibly

separate lineages that are at least subtly different in ecologi-

cal requirements and capabilities. One can approach the

origins of new diversity through a genetic analysis of newly

divergent bacterial populations. The availability of full

genome sequences from closely related bacteria has enabled

microbiologists to discover the ecological differences among

closely related groups as well as the genetic differences

underlying the ecological divergence.

In cases where habitat differences suggest ecological

differentiation between close relatives, a genome-based

analysis can reveal the physiological basis for the ecological

divergence and the role of HGT. For example, a recent study

by Luo et al. (2011) aimed to find the physiological basis of

differences between E. coli clades associated with external

environments (e.g. freshwater beaches and sediments) vs.

clades associated with mammals and birds, either as com-

mensals or as pathogens. Comparisons of genome content

confirmed that clades associated with external environments

were indeed ecologically distinct from clades associated with

endosymbiont lifestyles within mammals and birds. In

contrast to commensal and pathogenic clades, the environ-

mental clades were found consistently to contain lysozyme,

for breaking down the cell walls of other bacteria in the

external environment, as well as the biochemical pathway

for diol utilization, apparently for the use of diol as an

energy substrate. The gut-associated clades were found to

have transport and metabolic capability related to various

substrates that are known to be common in the gut,

including N-acetylglucosamine, gluconate, and five- and

six-carbon sugars. Most importantly, by virtue of genome

content differences, the environmental clades are not likely

to compete well in the gut and are not expected to be of

concern for human health (Luo et al., 2011).

Likewise, closely related gut and dairy taxa within Lacto-

bacillus have acquired genes that foster adaptation to their

respective environments (O’Sullivan et al., 2009). The gut-

adapted taxon Lactobacillus acidophilus bears the gene for

maltose-6-phosphate glucosidase, for degradation of the

abundant maltose in the gut; also, the gut-adapted taxon

has a gene for bile salt hydrolase, which contributes to

resistance to bile in the gut. Both these functions are absent

in the dairy-adapted taxon Lactobacillus helveticus. The

dairy-adapted taxon has a carboxypeptidase gene not found

in the gut-adapted taxon, which contributes to survival in

environments, such as milk, where amino acid levels are low.

Genome comparisons among other extremely closely

related populations in other taxa also identify the physiolo-

gical basis of ecological divergence and point to a role for

HGT in the origins of ecological diversification. Genome

comparisons among soil populations of Pseudomonas putida

associated with polluted and unpolluted environments show

the polluted populations to hold metal resistance genes not

found in unpolluted sites (Wu et al., 2010). Also, closely

related populations within Prochlorococcus marinus have

diverged in their phosphate-acquiring adaptations: those

populations adapted to marine environments with very low

levels of phosphate have acquired a set of genes involved in

the uptake, regulation, and utilization of organic phosphates

(Martiny et al., 2009a). In studies like these, comparisons of

genome contents among close relatives point to the physio-

logical basis by which populations diverge ecologically and



implicate a role for HGT as the motive force by which this

diversification has proceeded.

Genome content comparisons can also add to our knowl-

edge about the ecological dimensions by which populations

have diverged. This is seen in the case of two clades of

Synechococcus in a Yellowstone hot spring: one clade asso-

ciated with hotter, upstream environments closest to the

source of the spring (the A clade, 60–65 1C) and the other

associated with cooler, downstream environments (B0 clade,

55–61 1C). Comparison of the genome content of these

groups showed that beyond differences in their temperature

optima (Allewalt et al., 2006), the groups were also different

in their abilities to take up and store mineral nutrients

(Bhaya et al., 2007). The genomes suggest that the cooler,

downstream clade can accommodate lower concentrations

of mineral nutrients (owing to consumption by upstream

populations) by being able to take up phosphonate and store

nitrogen, adaptations acquired by HGT.

Metagenomic approaches have also yielded evidence for

HGT in the origin of ecological diversity. A shotgun

sequencing of random clones from the microbial mat of a

Yellowstone hot spring indicated an adaptive divergence

among extremely closely related populations of Synechococcus

(Bhaya et al., 2007). Whereas the complete genome of one

isolate showed no genome content dedicated to transport of

the reduced ferrous ion, several closely related organisms

identified from the metagenome contained the ferrous trans-

port genes feoA and feoB. This suggested that some popula-

tions, but not others are able to scavenge reduced iron during

the nighttime when the mat becomes anoxic (Bhaya et al.,

2007). Thus, niche adaptation strategies can be inferred by

comparing metagenome sequences with an ‘anchor’ isolate

whose genome has been fully sequenced.

Genome-based analyses have also shown how ecological

diversity can emerge from changes other than HGT, through

changes in existing genes. Petersen et al. (2007) searched the

set of genes shared by six E. coli strains for genes whose

amino acid divergence was accelerated by natural selection.

This search identified several genes coding for cell surface

proteins, and the amino acids under selection were consis-

tently found to be in the extracellular region, indicating that

diversification has resulted from the arms race between the

bacteria and their enemies, such as phage and the host

immune system.

Analyses of genome-wide gene expression have also

helped to characterize how changes in existing genes have

led to niche partitioning among close relatives. Denef et al.

(2010) found that one clade of the Archaean Leptospirillum,

which is typically a late colonizer of acid-mine pools, differs

from a closely related, early-colonizing clade in the expres-

sion of several shared proteins that may be important in

growth at low nutrient concentrations, including proteins

for cobalamin synthesis and glycine cleavage. It will be

fascinating to find the extent to which ecological differences

among close relatives are caused by the horizontal acquisi-

tion of novel genes vs. changes in the sequences and

expression patterns of shared genes.

In summary, genome-based comparisons have confirmed

that closely related populations are ecologically distinct; they

have identified the ecological dimensions by which the

populations have diverged; and they have determined the

genetic and physiological bases of ecological divergence,

including the role of HGT. Approaches adopted up to now,

however, have not directly addressed the origins of species,

by which one lineage irreversibly splits into two lineages, as

we next discuss.

HGTand the origins of bacterial species

To discover the ecological and genetic changes that occur

during speciation, it is not enough to compare closely

related species taxa or even close relatives within a species

taxon. To investigate the dynamics of lineage splitting and

the origin of irreversible separateness, we need to identify

the most newly divergent, ecologically distinct populations.

Focusing on these most newly divergent populations will

allow us to identify the features responsible for the earliest

splitting of lineages. If we instead compare more divergent

lineages, we cannot distinguish the features responsible for

lineage splitting from those features added on after the

lineages have become established. Studies of the speciation

process in bacteria have not yet attempted to identify and

compare the genomes of the most recent products of

speciation.

The established systematics of bacteria does not help

identify the most newly divergent, irreversibly separate,

ecologically distinct bacterial populations. The problem is

that the taxa recognized as species by bacterial systematics

are extremely broadly defined, with enormous levels of

divergence in physiology, genome content, and most im-

portantly, ecology (Welch et al., 2002; Whittam & Bum-

baugh, 2002; Tettelin et al., 2005; Lefebure & Stanhope,

2007; Rasko et al., 2008; Touchon et al., 2009; Paul et al.,

2010). For example, isolates within the species taxon E. coli

show huge differences in habitat (Walk et al., 2007, 2009)

and genome content (Welch et al., 2002; Touchon et al.,

2009; Luo et al., 2011). While there are increasing numbers

of studies comparing the genomes of close relatives within

the recognized species taxa, little attention has been paid as

to whether the populations being compared represent the

most newly divergent, irreversibly separate, ecologically

distinct populations.

One approach to identifying such populations takes into

account the dynamic properties that are widely understood

to characterize species, at least outside of the systematics of

bacteria (de Queiroz, 2005). Foremost among these dynamic



properties is that species are cohesive, in the sense that

genetic diversity within a species is limited by a force of

evolution (Templeton, 1989; Cohan & Perry, 2007). In

contrast, different species are viewed to be irreversibly

separate, with no cohesion holding them together. Also,

species are expected to be ecologically distinct, which allows

the species to coexist into the indefinite future. Finally, each

species is expected to be invented only once (de Queiroz,

2005).

The forces of species cohesion in bacteria potentially

include genetic exchange between populations (Papke et al.,

2007; Sheppard et al., 2008; Fraser et al., 2009; Retchless &

Lawrence, 2010), genetic drift (Wernegreen & Moran, 1999;

Kuo et al., 2009), and periodic selection, a diversity-purging

process occurring in populations with low recombination

rates (Koch, 1974; Levin, 1981; Cohan, 1994). Genetic

exchange has been widely considered to be a cohesive force

preventing ecological divergence in obligately sexual animals

and plants; in some models, permanent, adaptive divergence

between animal and plant populations is impossible unless

barriers to genetic exchange have evolved (Mayr, 1963;

Coyne & Orr, 2004), although the necessity of barriers to

genetic exchange in animal and plant speciation has been

recently challenged (Dieckmann et al., 2004; Mallet, 2008).

In the case of bacteria, the rate of exchange of genes

between populations, even between differently adapted

populations of the same microhabitat, is extremely low. This

is because the flow of genes between populations is limited

by the bacterial rate of recombination, which is consistently

near the mutation rate (Vos & Didelot, 2009). At such low

rates of gene flow between populations, genetic exchange is

not a significant force preventing ecological divergence

between bacterial populations (Cohan, 1994; Cohan &

Koeppel, 2008) (Box 1).

Genetic drift and periodic selection are the most likely

forces of cohesion within bacterial species. Cohesion by

these forces is limited to the set of ecologically similar

organisms within an ‘ecotype’ (Kopac & Cohan, 2011).

Genetic drift is most likely to be an important force of

cohesion for ecotypes with low effective population sizes, for

example, obligate endosymbionts that are transmitted be-

tween hosts in extremely small numbers (Wernegreen &

Moran, 1999). Periodic selection can act to purge the

diversity within any ecotype, regardless of its population

size (Levin & Bergstrom, 2000). In a periodic selection

event, an adaptive mutation or a gene acquisition by HGT

within an ecotype outcompetes to extinction all other

members of the ecotype; owing to the low recombination

rates in bacteria, selection favoring the adaptive mutation or

recombinant can bring nearly the entire genome sequence of

the mutant cell to 100% frequency within the ecotype

(Cohan, 2005). Thus, the diversity within an ecotype is

ephemeral, lasting only until the next periodic selection

event (Fig. 3).

The origin of permanent diversity requires the origin of a

new, ecologically distinct population, that is, the origin of an

Fig. 3. The dynamics of ecotype formation and periodic selection within an ecotype. Circles represent different genotypes, and asterisks represent

adaptive mutations. (a) Ecotype-formation event. A mutation or a recombination event allows the cell to occupy a new ecological niche, founding a new

ecotype. A new ecotype can be formed only if the founding organism has undergone a fitness trade-off, whereby it cannot compete successfully with

the parental ecotype in the old niche. (b) Periodic selection event. A periodic selection mutation improves the fitness of an individual such that the

mutant and its descendants outcompete all other cells within the ecotype; these mutations do not affect the diversity within other ecotypes because

ecological differences between ecotypes prevent direct competition. Periodic selection leads to the distinctness of ecotypes by purging the divergence

within, but not between ecotypes. (Used with permission from Landes Publishers.)



ecotype (Cohan & Perry, 2007). A new ecotype is formed

when an organism invades a new ecological niche, by virtue

of a mutation or an HGT event. The diversity-purging

power of an ecotype’s periodic selection events cannot reach

beyond the ecological similarity of ecotype members; the

ecological distinctness across ecotypes will generally protect

an ecotype from periodic selection stemming from another

ecotype (Fig. 3). An ecotype may be most precisely under-

stood as the domain of competitive superiority of an

adaptive mutant (Cohan, 1994).

The formation of a new ecotype requires that the new

niche-invading organism must be able to coexist with the

parental ecotype. This requires a fitness trade-off, where the

success of the niche-invading organism in its new niche

comes at a cost, usually in its ability to compete with the

parental ecotype in the old niche. The fitness trade-off leads

to two alternatively specialized ecotypes that can coexist

(Fig. 4). A different dynamic ensues when a mutant or a

recombinant organism acquires the ability to use new

resources at no cost in the old niche. In this case, the new

organism becomes more of an ecological generalist, and will

simply outcompete the members of its ecotype to extinction,

and no new ecotype is created (Fig. 4).

Thus, the origin of bacterial ecotypes that can coexist can

be viewed as the origin of species: a nascent ecotype is

irreversibly separate from the parental ecotype from which it

is derived, because the divergence of different ecotypes is not

limited either by periodic selection (Fig. 3) or by recombi-

nation (Box 1); also, each ecotype is genetically cohesive,

owing to the effect of genetic drift and/or periodic selection

in purging the diversity among the ecologically interchange-

able membership. Ecotypes thus hold the dynamic attributes

of species, and the origin of species can be understood by

exploring the origins of ecological divergence among newly

formed ecotypes.

Fortunately, the most newly divergent ecotypes can

potentially be identified even when we do not know the

ecological dimensions by which they have diverged,

Event Consequence

(a)

(b)

A

B

Ecotype 1 Ecotype 1

Ecotype 1 Ecotype 1 Ecotype 2

Loss

Ecotypeformation

Periodicselection

Fig. 4. The consequences of a change in ecological niche following an HGT event. Acquisition of a new ecological function by HGT (indicated by the

green triangle) can lead to either a periodic selection event (a) or an ecotype formation event (b). (a) The new ecological function is added to the

ecological repertoire of the recipient strain, and the resultant strain is now able to outcompete the membership of its ecotype by virtue of its greater

repertoire. An example of such a niche-expanding HGT event would be the acquisition of one more antibiotic resistance factor in a human pathogen

that is already resistant to a number of antibiotics; this would expand the set of clinical conditions under which the strain could succeed. (b) Gain of one

function is incompatible with an existing function or reduces the performance of an existing function. Thus, acquisition of the new ecological capability

comes at the expense of some pre-existing function. In this case, acquisition of the new function leads to a new ecotype, which can coexist with the pre-

existing ecotype. An example of this would be the acquisition of a new symbiosis plasmid in Rhizobium, conferring the recipient with the ability to infect

a new plant species; however, two symbiosis plasmids within a cell are incompatible. Thus, the plasmid transfer event would create a splitting of the

ecotype into two ecotypes. Alternatively, a mutation to better utilize a new carbon source could lead to the invention of a new ecotype, provided that

the mutation causes lower performance in utilizing the old carbon source. This has been seen repeatedly in experimental populations of Escherichia coli

that primarily used glucose for carbon; a mutation to utilize secreted acetate created a new ecotype because the acetate-utilizing bacteria were less able

to utilize glucose (Treves et al., 1998).



provided that a particular model of bacterial speciation is

assumed (Cohan & Perry, 2007). Under the Stable Ecotype

model (Fig. 5a), ecotypes are formed rarely and each ecotype

endures many periodic selection events within its lifetime.

This model therefore allows the most newly divergent

ecotypes to accumulate a unique set of neutral sequence

mutations since diverging from their most recent common

ancestor; moreover, the recurrent periodic selection events

ensure that each ecotype appears as a sequence cluster of

very close relatives, based on a phylogeny of any gene in the

genome. Different ecotypes will therefore appear to be much

more distantly related than members of the same ecotype

and will correspond to sequence clusters (Fig. 5a).

For decades, sequence clustering has formed the basis for

discovering ecotypes. For example, a diversity of ecotypes

was hypothesized within the species taxon Mycobacterium

bovis based on the clustering of rapidly evolving sequences,

and these clusters were confirmed to be ecologically distinct

on the basis of differences in host range (Smith et al., 2006).

Most closely related ecotypes have similarly been hypothe-

sized and independently confirmed to be ecologically dis-

tinct in many other taxa, including the photosynthetic

marine cyanobacteria of Prochlorococcus (Martiny et al.,

2009b), on the basis of temperature and nitrogen require-

ments.

The demarcation of ecotypes can be aided by theory-

based algorithms, which do not rely on an investigator’s

intuition about the phylogenetic size of a sequence cluster

most likely to correspond to an ecotype. The algorithms

ECOTYPE SIMULATION (Koeppel et al., 2008) and ADAPTML (Hunt

et al., 2008) infer ecotype demarcations using universal

molecular methods, generally interpreting ecotypes as se-

quence clusters for any gene shared among organisms. These

algorithms have inferred ecotypes from sequence data in

various bacterial systems, and the ecotypes have consistently

been confirmed to be ecologically distinct. For example,

ECOTYPE SIMULATION has identified extremely closely related

ecotypes in soil Bacillus that are ecologically distinct in their

ABDABC ABD Abc aBc abC

Stable ecotype

(a) (b) (c)

Nano-niche Recurrentniche invasion

ABC ABC

Fig. 5. Models of bacterial speciation. Ecotypes are represented by different colors; periodic selection events are indicated by asterisks and extinct

lineages are represented by dashed lines. The letters represent the resources that each group of organisms can utilize. In cases where ecotypes utilize the

same set of resources, but in different proportions, the predominant resource of each ecotype is denoted by a capital letter. (a) The Stable Ecotype

model. The Stable Ecotype model is marked by a much higher rate of periodic selection than ecotype formation, such that each ecotype endures many

periodic selection events during its lifetime. The Stable Ecotype model generally yields a one-to-one correspondence between ecotypes and sequence

clusters. The ecotypes are able to coexist indefinitely because each has a resource not shared with the other. (b) The Nano-Niche model of bacterial

speciation. In the figure, there are three Nano-Niche ecotypes (denoted by Abc, aBc, and abC) that use the same set of resources, but in different

proportions. Each Nano-Niche ecotype can coexist with the other two because they have partitioned their resources, at least quantitatively. However,

because the ecotypes share all their resources, each is vulnerable to a possible speciation-quashing mutation that may occur in the other ecotypes. This

could be a mutation that increases efficiency in the utilization of all resources. These speciation-quashing mutations are indicated by a large asterisk;

each of these extinguishes the other Nano-Niche ecotypes. Thus, in the Nano-Niche model, cohesion can cut across ecologically distinct populations,

provided that they are only quantitatively different in their resource utilization. (c) The Recurrent Niche Invasion model. Here, a lineage may move,

frequently and recurrently, from one ecotype to another, usually by acquisition and loss of niche-determining plasmids. In the figure, the red lines

indicate the times in which a lineage is in the plasmid-containing ecotype; the blue lines indicate the times when the lineage is in the plasmid-absent

ecotype. Periodic selection events within one ecotype extinguish only the lineages of the same ecotype. For example, in the most ancient periodic

selection event shown, which is in the plasmid-absent (blue) ecotype, only the lineages missing the plasmid at the time of periodic selection are

extinguished, while the plasmid-containing lineages (red) persist. Ecotypes determined by a plasmid are not likely to be discoverable as sequence clusters

(Cohan, 2011).



associations with solar exposure (Koeppel et al., 2008), soil

texture (Connor et al., 2010), rhizospheres, elevation, and

salinity (S. Kopac, unpublished data), as well as in hot spring

Synechococcus that are ecologically distinct in their associa-

tions with horizontal (temperature and nutrient availability)

and vertical (light level and quality) dimensions of the mat

(Ward et al., 2006; Melendrez et al., 2011), and in the

pathogen L. pneumophila, where ecotypes differ in their

amoebic host ranges and in their gene expression patterns

(Cohan et al., 2006); ADAPTML has identified ecotypes of

marine Vibrio that are associated with different particle sizes

and with seasons (Hunt et al., 2008).

These most closely related ecotypes, whether discovered

by an intuitive or an algorithmic demarcation, are the key to

discovering the ecological dimensions by which lineages

split and the genetic basis of ecotype formation, as well as

the role of HGT in the origins of ecological diversity.

Genome-based comparisons of the most closely related

ecotypes could confirm that the ecotypes are ecologically

different, and could reveal the ecological dimensions of

ecotype formation; moreover, they could identify the role

of HGT in the origins of species. If genome investigations

could be directed in the future toward comparing popula-

tions identified by algorithms such as ECOTYPE SIMULATION and

ADAPTML to be the most closely related, ecologically distinct

populations, we would make better progress toward study-

ing the dynamics of bacterial speciation and identifying all

the ecological diversity within bacterial taxa of interest

(Cohan & Perry, 2007). To our knowledge, clades that have

been identified as most closely related ecotypes have yet to

be compared by genome-based analyses.

Genome comparisons among close relatives can also test

the validity of alternative models of speciation. Of foremost

importance is the need to test whether bacterial ecotypes are

cohesive, and genome comparisons provide an opportunity

to do this. Doolittle & Zhaxybayeva (2009) have argued that

there is no fundamental reason why bacterial species should

be cohesive, and they suggest that frequent HGT events may

continuously cause a lineage to switch from one ecological

niche to another. In the most extreme form of this argu-

ment, ecological interchangeability may extend only as far as

a bacterium and its immediate offspring.

Doolittle and Zhaxybayeva (2009) have likely exaggerated

the importance of HGT in fostering ecological diversity

among closest relatives (Kopac & Cohan, 2011). While

closest relatives are inevitably different in their rosters of

genes, careful analyses of genome content differences have

shown that nearly all HGT events among closest relatives

involve transfers of genes not known to be involved in the

ecological divergence of bacteria, such as phage-related

genes, genes for transposition activity, and genes whose

function is not known (the genes of so-called ‘hypothetical’

function) (Touchon et al., 2009; Wiedenbeck, 2011).

Nevertheless, the phylogenetic groups identified as eco-

types can be cohesive only if their members are ecologically

interchangeable, as genetic drift and periodic selection can

purge diversity only within populations of the same ecolo-

gical niche; it is therefore essential to test whether the

members of an identified ecotype are ecologically inter-

changeable (Kopac & Cohan, 2011). Genome-based com-

parisons, whether focusing on genome content differences,

positive selection on shared genes, or changes in gene

expression, can test whether the members of a putative

ecotype (identified by sequence-based algorithms) are eco-

logically interchangeable and can identify the phylogenetic

breadth of ecological interchangeability.

We have recently tested for ecological interchangeability

within one putative ecotype hypothesized by ECOTYPE SIMULA-

TION and ADAPTML, to our knowledge the only test of

ecological interchangeability at this small phylogenetic scale

(Wiedenbeck, 2011). This was an analysis of five strains

from a putative ecotype (PE15) within B. subtilis ssp.

spizizenii, including four strains isolated from a Death Valley

canyon (Connor et al., 2010) and one well-characterized

reference strain (Zeigler et al., 2008). The five strains were

shown to be heterogeneous in their genome content, largely

in genes related to phage and transposition as well as genes

classified as hypothetical (Wiedenbeck, 2011). Remarkably,

none of the remaining unshared genes provided any novel

ecological functions; all these genes were either duplicates of

genes already shared by every strain or they were novel genes

contributing to functions already possessed by all strains.

For example, all five strains had multiple genes involved in

the transport and utilization of maltose, and additional

genes unique to some strains were either copies of existing

maltose genes or they provided additional capability for

metabolizing maltose. Every nonphage, nontransposing,

nonhypothetical gene that was unique to a subclade of

strains within our sample shared this pattern seen for

maltose.

Thus, while the putative ecotype’s members appear not to

be ecologically interchangeable, the heterogeneity appears to

be limited. We hypothesized that the divergence among

genome types represents a kind of quantitative divergence,

where each strain sampled has its own ecological niche, but

no strain has a unique resource that protects it completely

from competition (Wiedenbeck, 2011). In this case, the

various members of PE15 may utilize maltose under differ-

ent conditions, and each strain’s unique set of maltose genes

may allow that strain to utilize maltose best under its own

optimal conditions.

This kind of quantitative divergence among ecotypes is

different from the Stable Ecotype model, where each ecotype

is more completely protected from competition from other

ecotypes because each ecotype has some unique resources

(Fig. 5a). In the case of the variants within PE15, each



quantitatively different ecotype may have its own periodic

selection events, but it is still possible that a periodic

selection event in one ecotype could drive the other ecotypes

to extinction, as in the Nano-Niche model of bacterial

speciation (Cohan & Perry, 2007) (Fig. 5b). Thus, while the

putative ecotype analyzed here turned out to be ecologically

heterogeneous, it still may be cohesive owing to the incom-

plete and quantitative nature of the ecological divergence

among populations. This study shows the potential for gene

content comparisons to study both the phylogenetic breadth

of ecological interchangeability and the extent to which a

demarcated, putative ecotype forms a cohesive unit.

An alternative model of bacterial speciation, the Recur-

rent Niche Invasion model, takes into account the role of

mobile genetic elements, such as plasmids or phage, in

determining bacterial niches (Cohan & Perry, 2007)

(Fig. 5c). For example, in the case of Rhizobium, a bacterial

lineage may acquire a symbiosis plasmid that adapts it as an

endosymbiotic mutualist to one set of legume hosts; then

the lineage may lose that plasmid and acquire another

symbiosis plasmid, thereby adapting it to another set of

legumes (Segovia et al., 1991). Also, a pathogenic lineage

may acquire a multiple-resistance plasmid, which will adapt

it to habitats where antibiotics are free flowing, and then lose

it when living in habitats where antibiotics are rarely used.

In either case, a cell can be converted from one ecotype to

another by acquiring and/or losing a niche-specifying plas-

mid or phage, and so a lineage moves back and forth

between memberships in different, previously existing eco-

types. In contrast to chromosomally based ecotypes, eco-

types that are distinguished only by the presence of different

plasmids cannot be discovered as sequence clusters in genes

unrelated to ecological divergence (Cohan, 2011) (Fig. 5c).

We therefore cannot predict and demarcate plasmid-based

ecotypes on the basis of sequence divergence in randomly

chosen chromosomal genes. The ecological diversity created

by plasmids can be discovered only by screening for the

functions known or expected to be coded by the plasmids

ahead of time. As we next discuss, such a functional

approach has been successful in identifying ecological diver-

sity in antibiotic resistance factors, which are commonly

carried and transferred on plasmids (Fondi & Fani, 2010).

The antibiotic resistance of our future

What are the resistance genes of our future? To find the

resistance factors most likely to enter human pathogens

through HGT in the near future, we should take into

account that environmental and phylogenetic proximity of

two organisms contribute to their sharing of genes, as well as

the complexity and compatibility of resistance factors.

One approach screens for resistance factors with environ-

mental proximity to potential human pathogens; here,

bacteria associated with humans have been surveyed for

their antibiotic resistance genes. Sommer et al. (2009)

recently expressed DNA cloned from the human micro-

biome and selected clones for resistance to various antibio-

tics. This approach does not require homology or sequence

similarity of resistance genes to any known resistance factor.

Indeed, Sommer and colleagues found a broad diversity of

resistance factors that were extremely divergent from, and

some not even homologous with, previously known factors

(Box 3). It is not understood why these abundant resistance

factors have not yet made their way into E. coli and other

familiar bacteria in our guts. While this approach identifies

possible resistance factors in our future, it is important to

note that the proximity of these factors to possible human

pathogens might not be enough to foster their transfer.

Beyond searching for antibiotic resistance genes in the

human microbiome, it will be informative to screen the

metagenomes of other organisms from which we frequently

acquire pathogens, including agricultural animals, mice,

ticks, and mosquitoes. We will increasingly be able to

identify the organism sources of resistance genes discovered

in this way as more bacterial genome sequences become

Box 3. Identification of antibiotic resistance factors in human gut

bacteria

The widespread occurrence of antibiotic resistance factors among

bacteria, as well as their propagation by HGT, has led to an increased

interest in characterizing the possible reservoirs for resistance factors

(Riesenfeld et al., 2004; D’Costa et al., 2006). As the majority of

microorganisms are not presently cultivable, metagenomic analyses

of these possible reservoirs may supplement previous analyses of

resistance factors present in bacterial isolates. Sommer et al. (2009)

analyzed a possible resistance reservoir using a metagenomic analysis

of human microbial communities. The authors isolated DNA from

human saliva and fecal samples, and created clone libraries from

these DNA isolates. The clones were then screened for resistance to a

number of antibiotics, and the metagenomic inserts conferring

antibiotic resistance were sequenced and annotated. Interestingly,

many of the resistance inserts identified in this way did not match

previously characterized antibiotic resistance genes (Sommer et al.,

2009). Some of these genes encoded proteins that were identical to

proteins annotated as hypothetical. This finding is especially

intriguing, as it indicates that many hypothetical or poorly

characterized proteins from sequenced bacterial genomes may

indeed be resistance factors. Identifying these proteins will not only

help to further characterize modes of antibiotic resistance in bacteria,

but will also help to improve sequence annotation by identifying the

functions of hypothetical proteins. The findings of the Sommer and

colleagues study indicated that metagenomic analyses are a

successful way to identify new resistance factors. The human

microbiome, however, is only one of many possible reservoirs for

antibiotic resistance. Other important sources for resistance factors

include agricultural sources, water environments (Baquero et al.,

2008), and soil (Riesenfeld et al., 2004). Further investigations into

these reservoirs, including metagenomic analyses, will help to better

characterize resistance factors likely to be medically significant.



available. Those resistance factors that are in phylogenetic

and environmental proximity to human pathogens will be of

greatest concern (Matte-Tailliez et al., 2002).

As we discover increasing numbers of resistance factors,

we may test them for their efficacy in human pathogens. (Or

for safety purposes, we may test them in closely related,

nonpathogenic model systems.) This would include quanti-

tative measures of the resistance benefit as well as the fitness

cost of resistance factors, perhaps by following the high-

throughput approach of Sorek et al. (2007) for introducing

thousands of genes into a strain and measuring the fitness

effects of gene acquisition. It will be interesting to observe

the extent to which antibiotic resistance genes tend to be

specialized to different phylogenetic groups of pathogens

and how easily a resistance factor can co-evolve with a new

pathogen to overcome the initial incompatibility between

resistance and organism.

In summary, predicting our future of antibiotic resistance

will involve identifying the resistance factors that are within

reach by HGT of our species’ pathogens. It will also be

important to use the approaches outlined here to determine

which of these resistance factors are compatible with our

pathogens and which can become compatible through

ameliorative evolution.

The population biology approaches we have outlined may

also help to identify all the bacteria whose acquisition of

antibiotic resistance may harm us and our agricultural

dependents. We have illustrated how the broadly defined

species taxa of bacterial systematics can obscure within them

multiple clades differing in their pathogenic properties;

predicting the next epidemic may require going beyond the

existing taxonomy, through discovering new pathogenic

ecotypes within the established taxa (Cohan & Perry, 2007)

and by not being distracted by ecotypes that are not

pathogenic (Cohan et al., 2006; Smith et al., 2006; Luo

et al., 2011). We suggest using the universal approaches

described here to discover all the ecotypes within known

pathogenic taxa and to characterize their ecological capabil-

ities through genome comparisons. The discovery of poten-

tial pathogens by these universal approaches, followed by

surveillance of their acquisitions of antibiotic resistance, will

be an important preemptive public health strategy.

Acknowledgements

This work was funded by a NASA-funded Connecticut

Space Grant to J.W. and by Wesleyan University research

funds to F.M.C. We thank Jessica Sherry for alerting us to

several examples of niche-specifying HGT events. We thank

Fernando Baquero and Dieter Haas, as well as two anon-

ymous reviewers, for their valuable suggestions for improv-

ing the manuscript.

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