Patterns of Gene Flow Define Species of Thermophilic Archaea

Abstract

Despite a growing appreciation of their vast diversity in nature, mechanisms of speciation are poorly understood in Bacteria and Archaea. Here we use high-throughput genome sequencing to identify ongoing speciation in the thermoacidophilic Archaeon Sulfolobus islandicus. Patterns of homologous gene flow among genomes of 12 strains from a single hot spring in Kamchatka, Russia, demonstrate higher levels of gene flow within than between two persistent, coexisting groups, demonstrating that these microorganisms fit the biological species concept. Furthermore, rates of gene flow between two species are decreasing over time in a manner consistent with incipient speciation. Unlike other microorganisms investigated, we do not observe a relationship between genetic divergence and frequency of recombination along a chromosome, or other physical mechanisms that would reduce gene flow between lineages. Each species has its own genetic island encoding unique physiological functions and a unique growth phenotype that may be indicative of ecological specialization. Genetic differentiation between these coexisting groups occurs in large genomic "continents," indicating the topology of genomic divergence during speciation is not uniform and is not associated with a single locus under strong diversifying selection. These data support a model where species do not require physical barriers to gene flow but are maintained by ecological differentiation.

Full text

Patterns of Gene Flow Define Species of Thermophilic
Archaea
Hinsby Cadillo-Quiroz
1¤
, Xavier Didelot
2
, Nicole L. Held
1
, Alfa Herrera
1
, Aaron Darling
3
,
Michael L. Reno
1
, David J. Krause
1
, Rachel J. Whitaker
1
*
1Department of Microbiology and Institute for Genomic Biology, University of Illinois, Urbana-Champaign, Urbana, Illinois, United States of America, 2Department of
Statistics, University of Oxford, Oxford, United Kingdom, 3Genome Center, University of California, Davis, Davis, California, United States of America
Abstract
Despite a growing appreciation of their vast diversity in nature, mechanisms of speciation are poorly understood in Bacteria
and Archaea. Here we use high-throughput genome sequencing to identify ongoing speciation in the thermoacidophilic
Archaeon Sulfolobus islandicus. Patterns of homologous gene flow among genomes of 12 strains from a single hot spring in
Kamchatka, Russia, demonstrate higher levels of gene flow within than between two persistent, coexisting groups,
demonstrating that these microorganisms fit the biological species concept. Furthermore, rates of gene flow between two
species are decreasing over time in a manner consistent with incipient speciation. Unlike other microorganisms
investigated, we do not observe a relationship between genetic divergence and frequency of recombination along a
chromosome, or other physical mechanisms that would reduce gene flow between lineages. Each species has its own
genetic island encoding unique physiological functions and a unique growth phenotype that may be indicative of
ecological specialization. Genetic differentiation between these coexisting groups occurs in large genomic ‘‘continents,’’
indicating the topology of genomic divergence during speciation is not uniform and is not associated with a single locus
under strong diversifying selection. These data support a model where species do not require physical barriers to gene flow
but are maintained by ecological differentiation.
Citation: Cadillo-Quiroz H, Didelot X, Held NL, Herrera A, Darling A, et al. (2012) Patterns of Gene Flow Define Species of Thermophilic Archaea. PLoS Biol 10(2):
e1001265. doi:10.1371/journal.pbio.1001265
Academic Editor: Nick H. Barton, University of Edinburgh, United Kingdom
Received September 11, 2011; Accepted January 6, 2012; Published February 21, 2012
Copyright: ß2012 Cadillo-Quiroz et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Funding support to R.J.W. was provided by NSF DEB-0816885 and NASA NNX09AM92G. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Abbreviations: HR, homologous recombination; MLSA, multilocus sequence analysis; RAST, Rapid Annotation with Subsystems Technology; SNPs, single
nucleotide polymorphisms.
* E-mail: rwhitaker@life.illinois.edu
¤ Current address: School of Life Sciences, Arizona State University, Tempe, Arizona
Introduction
Molecular sequence analyses of microbial populations commonly
reveal discrete clusters of sequence diversity indicative of closely
related, but distinct, coexisting, lineages [1–9]. Such clusters are
sometimes given the status of species, especially when they are
shown to be ecologically distinct [6]. Resolving the evolutionary
mechanisms that cause the formation and maintenance of these
clusters holds the key to understanding the process of speciation in
clonally reproducing, asexual microorganisms [10].
In the absence of geographic barriers, speciation depends upon
the balance between gene flow holding lineages together and
selection pulling them apart [11,12]. For asexual microorganisms,
two primary theoretical models explain the formation of sequence
clusters, each tipping the scale in the opposite direction of the
recombination-selection balance. The first emphasizes the impor-
tance of selection driving ecological specialization. This model
predicts that the persistent coexistence of sequence clusters can
result from sequential selective sweeps of adaptive mutations to
different niches [13]. Persistent ‘‘ecotypes’’ are held together by the
cohesive force of genetic drift or periodic selection and kept apart by
selection for niche-specific adaptations or against niche-specific
maladaptations [13,14]. This model can incorporate low levels of
gene flow of universally adaptive genetic material without limiting
the effects of periodic selection differentially occurring in the
ecotypes [15]. Ecological differentiation, correlated with decreased
recombination, has been observed using multilocus sequence
markers [6] and recently through whole genome analysis in
Escherichia coli strains that have evolved to inhabit different
environments [16].
A second model relies on barriers to recombination in the
absence of selection to explain persistent sequence clusters [17]. It
demonstrates that clusters will form if the effects of recombination
are lower than mutation. In this neutral model, when recombi-
nation is greater than mutation, recombination plays a cohesive
role that is strong enough to prevent the formation of persistent
independent clusters of sequences, unless there is a significant
physical barrier to gene flow between them [17–19]. For
microorganisms, many such barriers can be hypothesized [20].
The most often mentioned and broadly distributed among
microbial taxa is caused by mismatch repair recognition [21],
which reduces the frequency of homologous recombination (HR)
between divergent sequences. This type of barrier has been shown
to lead to the formation of persistent diverging and independent
PLoS Biology | www.plosbiology.org 1 February 2012 | Volume 10 | Issue 2 | e1001265

clusters of sequences by allowing recombination within but not
between groups that diverge through genetic drift [17,22].
Whether recombination barriers or selection play the primary
role in driving divergence of sequence clusters and whether the
balance between these processes results in the maintenance of
independent species in microorganisms is a controversial topic
[16,23,24]. Many Bacteria and Archaea exhibit significant rates of
HR and other forms of horizontal gene flow [25–28]. However,
for each of the known mechanisms of horizontal transfer
(transduction, transformation, and conjugation) only a small
region of the chromosome may be transferred with each event.
Whether this level of recombination among coexisting strains is
strong enough to overcome ecological specialization and periodic
selection and how the balance between recombination and
selection will affect the topology of speciation across the
chromosome in microorganisms is only beginning to emerge with
the advent of whole genome sequencing [16,29].
To examine the mechanisms of divergence and maintenance of
independent species in Archaea, we sequenced the complete
genomes of 12 strains of the thermoacidophilic Archaeon Sulfolobus
islandicus from a single hot spring from the Mutnovsky Volcano
region in Kamchatka, Russia. This location was selected because
the S. islandicus population from the Mutnovsky volcano has been
shown to be geographically isolated [30,31], thus allowing us to
investigate evolutionary processes occurring within well-defined
geographic boundaries.
Results and Discussion
Multilocus sequence analysis (MLSA) using a set of seven loci [32]
from 97 S. islandicus strains from nine hot springs sampled from the
Mutnovsky Volcano region in the years 2000 and 2010 (listed in
Table S1 and Table S2) showed significant differentiation by F
ST
[33,34] among springs in only a few cases (Table S3) [25]. No
differentiation was observed when strains were pooled by year. The
M.16 spring was chosen for further analysis because the diversity
within this spring represents the diversity of the Mutnovsky
population as a whole (Figure 1A). To further investigate patterns
of gene flow within this population, the genome sequences of 10 new
S. islandicus M.16 strains all from a single sample collected in 2000
were compared to two strains that have been previously sequenced
from the same hot spring sample [30].
Frequency of Homologous Recombination
The 12 S. islandicus genomes had an average size of 2.64 Mb
(Table 1), of which approximately 86% was shared by all strains.
The genomic sequences of the strains were very similar, with
pairwise genetic distances ranging from 0.01% to 0.35% (Table 1).
Shared genomic regions were used to infer the clonal genealogy
using ClonalFrame, which accounts for the possibility of HR
disrupting the signal of vertical genetic inheritance [35]. The
relationships reconstructed using the full genomes (Figure 1B)
confirmed those resolved by MLSA (Figure 1A), and further
resolved bifurcations among strains that had unclear relationships
using only seven marker loci. Based on this clonal genealogy, HR
events were reconstructed using ClonalOrigin [36]. Overall,
ClonalOrigin estimated that each nucleotide was substituted by
recombination with a higher probability than mutation with a
ratio estimated between 1.8 and 13 [37]. This value is above the
threshold predicted to prevent divergence among sequence
clusters in simulations of neutral populations with a small effective
population size of 10
5
[17].
Using ClonalOrigin we were able to map recombinant
fragments between donor and recipient genomes. As shown in
Figure 2, the pattern of recombination between branches of the
tree differed from the values expected under the coalescent
model with constant recombination. The observed number of
HR events was higher than expected within two groups, but
lower than expected between them. The first group contains
seven strains (M.16.27, M.16.46, M.16.13, M.16.23, M.16.43,
M.16.47, and M.16.30), hereafter called the Blue group, and the
second contains three strains (M.16.4, M.16.40, and M.16.02),
hereafter called the Red group. This pattern of higher gene flow
within than between two groups (Red and Blue) coexisting in the
same hot spring fits the biological species concept [10,38,39].
The absolute numbers of events (Figure S1) show that there are
rare transfers between divergent sets of strains in the Red and
Blue groups, indicating that there is not a complete barrier to
gene flow, but rather a relative decrease in the number of
recombination events between groups.
The two intermediate and nearly identical strains (M.16.12 and
M.16.22) receive recombinant fragments at a higher relative
frequency from the Blue group than from the Red group (Figure 2)
but serve as a donor at a lower frequency than expected to both
the Blue and Red groups. We excluded them from further analysis
because it was unclear whether they should be included in the Blue
group or kept separate.
Non-Homologous Gene Flow
Microorganisms such as S. islandicus engage in promiscuous,
non-homologous gene flow through horizontal gene transfer,
which is often mediated by integration of a diversity of mobile
elements such as viruses and plasmids [30,40]. To test whether
there is evidence for non-homologous gene flow among popula-
tions, we mapped variation in genome content onto the core gene
phylogeny. In total, we identified 48 non-core segments longer
than 5 kb that are found in only some of the 12 strains (Table S4).
Thirty-four of these can be explained by a single gain or loss event
on a single branch along the phylogenetic tree (Figure 1B),
including 12 gains by a single genome (Table S4). The remaining
Author Summary
Microorganisms from the bacterial and archaeal domains
of the tree of life comprise the greatest breadth of
biodiversity on earth. Yet the essential evolutionary
process of speciation (through which biodiversity is
generated) is poorly understood in microbes. At issue is
the fundamental question of whether gene flow among
individuals of clonally reproducing microorganisms is rapid
enough to provide coherence within—and prevent
speciation between—coexisting lineages. We use com-
plete sequencing of microbial genomes to observe
speciation in action. We focus on Archaea called Sulfolobus
islandicus gathered from a geothermal hot spring from the
Mutnovsky Volcano in Kamchatka, Russia, whose physical
isolation allows us to pinpoint evolutionary processes to
one location. Contrary to the theoretical predictions for
microbes, we provide evidence that two novel lineages are
in the process of becoming ecologically distinct and
evolutionarily independent despite the fact that they
recombine. The divergence we observe is not happening
uniformly across the genome because certain genomic
regions are more prone to become differentiated between
species than others. This genomic view of the process of
speciation occurring within a single natural microbial
population contributes to our understanding of the
generation of biodiversity in Archaea and furthers our
understanding of speciation across the tree of life.
Speciation in Archaea
PLoS Biology | www.plosbiology.org 2 February 2012 | Volume 10 | Issue 2 | e1001265

14 non-core segments have distributions that require multiple
events to be explained (e.g., gene flow between strains or gene gain
followed by differential gene loss). Only four of these (36, 38–40 in
Table S4) could result from exchange between strains in the Blue
and Red groups. The distribution of non-core regions of the 12 S.
islandicus genomes is therefore consistent with a low level of non-
homologous gene flow between the two groups, analogous to the
pattern of homologous gene flow observed above.
Table 1. Characteristics of 12 strains of S. islandicus from spring M.16.
Group Name Size (Mb) Pairwise Genetic Distance (%) Growth Rate Final OD
M.16.27 M.16.4 M.16.12
Blue M.16.27
a
2.69 — 0.32 0.19 0.005 0.25
M.16.46 2.69
b
0.00 0.32 0.19 0.003 0.13
M.16.13 2.63
b
0.01 0.32 0.19 ND ND
M.16.47 2.64 0.06 0.32 0.18 0.002 0.09
M.16.23 2.6 0.05 0.31 0.18 0.003 0.17
M.16.30 2.61
b
0.06 0.31 0.17 0.003 0.14
M.16.43 2.59 0.08 0.32 0.17 0.001 0.07
M.16.12 2.69
b
0.19 0.31 — 0.009 0.57
M.16.22 2.68
b
0.19 0.31 0.00 0.009 0.63
Red M.16.02 2.65 0.35 0.18 0.35 0.007 0.45
M.16.40 2.67 0.33 0.06 0.33 0.011 0.60
M.16.4
a
2.59 0.32 — 0.31 0.006 0.41
a
Previously published in [30].
b
Genome not closed.
ND, not done.
doi:10.1371/journal.pbio.1001265.t001
M.16.27
M.16.46
M.16.13
M.16.23
M.16.47
M.16.43
M.16.30
M.16.12
M.16.22
20.6
.1
M
M.16.04
M.16.40
B
A
0
0
02
.
7
0
.2
1
.
M
M.12.13.2000
M.14.38.2000
M.03.0.02.2010
M.03.0.27.2010
M.01.3.02.2010
M.02.2.19.2010
M.12.35.2000
M.12.37.2000
M.16.05.2000
M.16.10.2000
M.16.13.2000
M.16.15.2000
M.16.17.2000
M.16.18.2000
M.16.19.2000
M.16.21.2000
M.16.25.2000
M.16.26.2000
M.16.27.2000
M.16.33.2000
M.16.36.2000
M.16.37.2000
M.16.41.2000
M.16.42.2000
M.16
.44.20
00
M.16.46.2000
M.01.1.02.2010
M.02.0.37.2010
M.03.0.16.2010
M.05.2.03.2010
M.03.2.05.2010
M.12.04.2000
M.12.08.2000
M.12.26.2000
M.03.0.50.2010
M.14.17.2000
M.14.25.2000
M.14.46.2000
M.14.39.2000
M.14.34.2000
M.14.p01.2000
M.03.2.01.2010
M.03.0.42.2010
M.03.1.05.2010
M.06.0.08.2010
M.14.16.2000
M.04.1.01.2010
M.04.0.37.2010
M.16.23.2000
M.16.28.2000
M.12.27.2000
M.12.46.2000
M.16.01.2000
M.16.09.2000
M.16.14.2000
M.16.30.2000
M.16.32.2000
M.16.47.2000
M.01.0.02.2010
M.02.0.30.2010
M.02.0.33.2010
M.02.1.13.2010
M.05.0.25.2010
M.06.0.06.2010
M.06.1.01.2010
M.06.1.04.2010
M.06.2.04.2010
M.01.2.01.2010
M.16.43.2000
M.16.45.2000
M.02.0.16.2010
M.02.0.20.2010
M.04.0.29.2010
M.05.0.01.2010
M.16.38.2000
M02.2.07.2010
M.02.1.06.2010
M.01.1.03.2010
M.01.2.04.2010
M.04.0.13.2010
M.04.0.10.2010
M.05.1.05.2010
M.14.06.2000
M.04.1.04.2010
M.05.0.43.2010
M.16.03.2000
M.16.04.2000
M.02.3.11.2010
M.16.02.2000
M.16.06.2000
M.16.40.2000
M.05.0.30.2010
M.16.12.2000
M.16.22.2000
M.16.34.2000
M.05.3.04.2010
*
*
*
*
*
*
*
*
*
*
*
*
Figure 1. Phylogenetic relationships among
S. islandicus
from the Mutnovsky Volcano. (A) ClonalFrame [35] reconstruction based on
seven loci from 97 S. islandicus strains from the Mutnovsky Volcano region of Kamchatka, Russia (details in Table S1). Strains in purple were isolated
from spring M.16. The first number in each name indicates the spring from which strains were isolated, the second indicates the isolate number from
that spring, and the third indicates year of isolation. * designates strains selected for genome sequencing and comparison. (B) ClonalFrame
phylogeny based on the core genome alignment of 12 S. islandicus strains from hot spring M.16.
doi:10.1371/journal.pbio.1001265.g001
Speciation in Archaea
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Decreasing Gene Flow Between Groups
The Red and Blue groups identified in Figure 2 could be either
diverging over time or could have evolved independently and
started to converge through a process of multiple migration with
introgression [41,42]. To differentiate these two hypotheses, we
studied the distribution of recombination events between the two
species through coalescent time as inferred by ClonalOrigin.
Figure 3 shows that the percentage of the recombinant events from
the Blue to the Red groups is decreasing over coalescent time
(Figure 3A). This indicates that the groups are progressively
diverging in a manner that is consistent with ongoing speciation.
As shown by MLSA in Figure 1A and Table S3, high levels of
differentiation do not occur between hot springs. This indicates
that the speciation we have observed in one spring has in fact
occurred at a larger scale within the Mutnovsky population, which
has been shown to be geographically isolated when compared to
similar populations from North America [30,31]. We cannot
exclude the possibility that one of these two groups initially
diverged elsewhere and migrated to the Mutnovsky Volcano. The
decrease in gene flow between groups suggests that if this were the
case, ongoing migration is decreasing over time. Our observation
of recent, low levels of gene flow between groups demonstrates the
coexistence and interaction of these groups of strains. Therefore,
we investigated mechanisms that either drive or maintain the
independence of these groups as they coexist.
Absence of Physical Barriers to Gene Flow
Although recombination rates in this Archaeon, as in other
microorganisms, are low relative to sexual eukaryotes, the two
primary models for speciation in microorganisms predict that
either barriers to recombination, or diversifying selection, between
the two types is necessary to explain their maintenance and
ongoing divergence. We first investigated possible physical barriers
to recombination between the two species. Neutral divergence of
lineages can occur when there is a decrease in recombination with
genetic divergence resulting from mismatch recognition [21]. This
relationship has been identified in many bacterial and eukaryotic
species [43,44]. We tested this hypothesis by examining whether
regions of the chromosome with higher divergence exhibited lower
frequencies of recombination than those with lower divergence.
This analysis therefore looks at the variation in rates of
recombination along the chromosome rather than between
particular partners as above. We found no correlation between
genetic divergence and recombination frequency when all
Figure 2. Heat map representation of homologous recombination frequency for every donor/recipient pair of branches of the core
genome phylogeny of 12
S. islandicus
strains. Recombination frequency is measured relative to its expectation under the prior of the
ClonalOrigin model and color coded according to the upper left color/magnitude legend (light blue and blue for the frequency of recombination
events below a 1:1 ratio and yellow to red for the frequency of recombination events above 1:1). Light gray cells represent non-significant ratios with
less than four observed and expected events. White shows number of events that match the prior expectations. Names of strains are color coded as
Blue and Red groups.
doi:10.1371/journal.pbio.1001265.g002
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recombination events from the populations were pooled (slope
0.016, R
2
= 6.3e-07, Figure S2A). This contrasts with (i) the same
analysis in the Bacillus cereus group [36], where a negative
correlation was found (slope 23.28, R
2
= 0.29, Figure S2B), (ii)
experimental data for Bacteria tested over the same range of genetic
distances (Figure S2C) [17,43–46], and (iii) metagenomic analysis
of the Archaeon Ferroplasma [47]. This lack of correlation between
sequence divergence and rate of recombination is, however,
consistent with the mechanisms of recombination reported for
another Sulfolobus species, S. acidocaldarius, in which short tracts of
20–22 nt are incorporated during transformation with sequence
identity of only 2–3 nt required at either end of the import [48]. In
addition, the lack of an identified mutSL system in this species may
result in the failure to prevent recombination between divergent
sequences [49].
Homologous recombination and non-homologous gene flow
through mobile elements has been observed in laboratory cultures
between strains of S. acidocaldarius [50,51]. In this system, DNA
transfer between cells is thought to occur through pilin-mediated
aggregation and conjugation [51,52]. We considered physical
barriers that could prevent aggregation and conjugation among
sympatric species of S. islandicus. The most highly variable
sequence in the genome is that of the large subunit of the S-
layer protein that covers the cell surface of S. islandicus and other
microorganisms [53]. With one exception, alleles of the S-layer
cluster into three distinct groups: Blue, Red, and the strains
M.16.12 and M.16.22. The notable exception, M.16.30, is the
most divergent Blue strain, which possesses an allele most similar
to the Red group (Figure S3). The incongruence of the M.16.30
strain possessing the Red allele but showing a history of
recombination with the Blue strains suggests that S-layer
divergence did not pose the barrier to recombination that resulted
in the divergence of the Red and Blue groups.
The Red and Blue S-layer alleles are highly divergent (with 12%
nucleotide substitutions) compared to the rest of the genome,
especially in the surface regions of the protein that are shown to be
highly glycosylated in other species [54]. The allele in the Blue
group appears to have been acquired by horizontal gene transfer,
as it does not fit the core gene phylogeny of other sequenced
Sulfolobus strains (Figure S3) [30]. Two amino acid changes are
identified between the Red and Blue alleles of the ups pili believed
to be responsible for aggregation and possible DNA exchange in
contact with the S-layer [52,55]. We cannot exclude the possibility
that the history of recombination shown in M.16.30 is not
consistent with its current genotype because of the recent
acquisition of the novel allele by the Blue group or the recent
transfer of this allele between a strain belonging to the Red group
and M.16.30. In this case, the S-layer may serve as a barrier to
current gene transfer, analogous to the prezygotic barrier in
macroorganisms, possibly reinforcing the isolation between
lineages following their initial differentiation.
The specificity of restriction enzymes could serve as barrier to
recombination between species [20]. Because restriction enzymes
are difficult to recognize bioinformatically, we investigated the
distribution of methyltransferases that can confer specific protec-
tion to their linked restriction systems among the genomes of our
12 S. islandicus strains. While variation exists between strains, none
of it was consistent with the distinction between the Blue and Red
groups of S. islandicus strains, indicating that these systems are not
providing a barrier to genetic exchange between the two groups.
Although there may be physical barriers to the transfer and
integration of DNA between the Red and Blue groups of strains
that we have not yet identified, the set of possible mechanisms we
have tested thus far do not appear to contribute to the decrease in
gene flow between the Red and Blue groups that we observe.
Genomic Signatures of Selection and Ecological
Differentiation
In the absence of physical barriers to gene flow, diversifying
selection may be driving these two incipient species apart or
maintaining their differentiation. We note that the relatively low
frequency of recombination observed in microorganisms, as
compared with sexual eukaryotes, facilitates selection-driven
divergence. We examined the differences in genotypes and
phenotypes between the two groups to identify possible loci under
differential selection that could cause ecological differentiation.
First, we estimated the ratio of non-synonymous to synonymous
rates of substitutions (d
N
/d
S
) between the Blue and Red groups
and performed the MacDonald-Kreitman test [56] for all core
genes. No indication of diversifying selection (values significantly
greater than 1.0) was found. Overall, our estimate of the average
d
N
/d
S
was 0.42, which is consistent with recent divergence of these
two groups in which purifying selection has not yet cleared mildly
deleterious non-synonymous substitutions [57,58].
Figure 3. Variation in recombination events between the Red
and Blue groups through time. For the Red (A) and Blue (B)
recipient strains, the total proportion of events that could be assigned
as originating from either donor Red (colored red) and Blue (colored
blue) strains is shown as a function of coalescent time with 10 being the
most recent divergence and 1 being the common ancestor of this set of
strains. A coalescent unit of time is equal to the average length of a
generation multiplied by the effective population size.
doi:10.1371/journal.pbio.1001265.g003
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Genomic loci that are under differential selection between two
diverging species have been identified as outlier loci in genomic
islands associated with divergent alleles differentially fixed between
populations [59,60]. In total, we identified 8,185 informative single
nucleotide polymorphisms (SNPs) (excluding indels) within the
Blue and Red groups of strains. Of these, 4,232 (52%) were fixed
differences between the two groups. This high number of fixed
differences between the two groups of strains relative to recently
diverged sexual species [61,62] may result from the relatively low
rates of non-reciprocal gene flow occurring in Sulfolobus.We
calculated F
ST
values based on individual non-indel SNPs for each
gene and in sliding windows of 10 kb across the genome (Figure 4).
F
ST
measures the level of differentiation between two groups
[63,64]. Low values indicate more diversity within than between
groups that can result from either constrained divergence between
groups by purifying selection or the exchange of alleles through
recombination. High values indicate differentiation with more
variation between groups than within. This occurs in regions that
are under diversifying selection or where there are low levels of
recombination [63]. This analysis revealed that the majority of the
core chromosome exhibits high F
ST
values and appears to be
differentiated. Of the 1,883 genes shared between the Blue and
Red groups in which variation was detected, only 466 genes (25%)
exhibit F
ST
values lower than 0.5. As shown in Figure 4, although
the majority of the genome is highly differentiated (F
ST
.0.5) lower
levels of differentiation (F
ST
,0.5) between the two species occur
exclusively in three large regions of the genome (ranging in size
from approximately 265 Kb to 770 Kb), separated by differenti-
ated genomic ‘‘continents’’ (ranging in size from 290 Kb to
370 Kb) [65] where no lower values are observed. Within these
larger regions, smaller differentiated islands, between 5 Kb and
230 Kb, in length exist separated by contiguous regions of low
differentiation between 10 Kb and 30 Kb long.
The broad topology of differentiation across the chromosome is
consistent with either differential selection among many loci within
the chromosome (located within the three regions of high
differentiation), different levels of gene flow in different regions
of the chromosome (higher gene flow in regions of lower
differentiation), or both [64,65]. Many loci under weak selection
would explain our failure to identify loci under diversifying
selection using the patterns of non-synonymous to synonymous
substitutions or the MacDonald-Kreitman test. This pattern is also
Position on the chromosome of M.16.27
Fst
0.2
0.4
0.6
0.8
1.0
500000 1000000 1500000 2000000 2500000
A
B
*
Figure 4. F
ST
values between the Red and Blue groups along the chromosome of strain M.16.27. (A) 10,000 bp windows on the M.16.27
genome where genome sequence is not present in all 10 strains from the Red and Blue groups. These positions highlight variable portions of the
M.16.27 genome. *Indicates a recently integrated plasmid. (B) F
ST
values were calculated for sliding windows of 10 kb moving in 5 kb steps. Empty
windows where sequence from M.16.27 is not shared by all strains are not plotted. Shading highlights regions of the chromosome that are less
differentiated beginning and ending with the first window with F
ST
values lower than 0.5.
doi:10.1371/journal.pbio.1001265.g004
Speciation in Archaea
PLoS Biology | www.plosbiology.org 6 February 2012 | Volume 10 | Issue 2 | e1001265

consistent with the prediction that, due to the mechanisms of gene
flow in microorganisms, different regions of the chromosome will
exhibit different patterns of speciation [29]. Chromosomal regions
that are less susceptible to recombination between species will
differentiate first even if they are not associated with ecological
differentiation. Interestingly, most non-core genes occur in regions
of low differentiation between the two groups (Figure 4). This is
inconsistent with novel genes driving differentiation between
species or with the import of novel gene islands decreasing
recombination and promoting speciation [16,66]. This lack of
differentiation in regions of the chromosome with variable gene
content supports the earlier observation and experimental data
that suggest long regions of high sequence homology is not
required for recombination. While the basis for the existence of
these genomic ‘‘continents’’ of differentiation is unknown, they
highlight the importance of using whole genomes when investi-
gating speciation.
To determine whether differences in gene content between the
Red and Blue groups could be responsible for their ecological
differentiation, we identified four contiguous regions of more than
5 Kb in which more than half of the genes are shared exclusively
by all members of a group (Table S4). Two of these contain genes
of unknown function (1 and 3, Table S4), with one having
signatures of being an integrated plasmid fragment (1 on Table
S4). Two additional islands, one in each species, with functional
annotations, were identified. The first island (2 in Table S4),
present only in the Red strains, contains six subunits (TmoAab,B,
C, D, and E) and three accessory proteins of a putative Toluene-4-
monoxygenase system. This region is present in many previously
sequenced strains of S. islandicus [30] and was probably lost by the
Blue group after its divergence from the Red group. The Blue
strains share an island of four subunits (a,b,d, and c) of a putative
respiratory nitrate reductase system (4 in Table S4). Active nitrate
reductases have been observed in other Archaea [67] but not in
Sulfolobales, while a putative monooxygenase operon, similar to that
in the Red group, was reported to be active in S. solfataricus [68]. In
both of these regions, d
N
/d
S
values and divergence were similar to
the estimates for the rest of the genomes. Both of these islands
occur in the large, second region of lower differentiation and high
variation in gene content identified in Figure 4.
Finally, we observed a difference in growth characteristics that
might result from ecological differentiation. Triplicate cultures
were grown that showed that the two groups differ in
heterotrophic growth characteristics in rich media developed for
Sulfolobus in the laboratory [31]. The Red strains have a shorter
lagging time, higher growth rate, and higher culture density than
the Blue strains (Figure 5). The growth difference between the Red
and Blue groups was statistically significant (ttest, p,0.001).
Although growth differences could result from many aspects of
Sulfolobus physiology, these data provide a phenotypic basis for the
definition of two species that is commonly suggested as necessary
in microbial taxonomy [69,70].
Conclusions
The sequencing of multiple genomes from closely related strains
of S. islandicus coexisting in the same hot spring has allowed us to
identify evidence for sympatric speciation in this natural archaeal
population. Without typical barriers to recombination associated
with genetic distance, the mechanism of speciation is likely to be
ecological differentiation among many loci throughout highly
differentiated regions of the chromosome or by differential islands
of gene content. Two incipient species are persistent in the
Mutnovsky Volcano region, as MLSA of 42 strains collected from
six springs in 2010 shows a very similar structure to that observed in
2000 (Figure 1A). This supports ecological differentiation that
prevents competitive exclusion resulting in extinction of one type on
this time scale [71,72]. Speciation driven by ecological divergence
rather than physical barriers to gene flow has been increasingly
observed in sexual eukaryotes [11,73–76]. Furthermore, the
genomic pattern of differentiation under these circumstances has
been theoretically predicted [77] and empirically demonstrated to
exist in ‘‘continents’’ of differentiation using genomic analyses [65].
These initial reports identifying the genomic pattern of differenti-
ation among species in model organisms of the domains Archaea and
Eukarya point towards a possible unified genomic process of
speciation across these two domains.
Material and Methods
Strain Isolation and DNA Extraction
Ninety-seven S. islandicus strains were isolated from eight hot
springs located in the Mutnovsky Volcano region in the
Kamchatka Peninsula (Russia), with specifications listed in Table
S1. Cultures were colony isolated on four different media: DT
(dextrin and tryptone) spread plate as described in [31,78], DTO
plate containing DT plate plus an overlay of 0.002% Gelrite
(Sigma), DTS spread plate containing DT plate plus an overlay of
0.002% colloidal sulfur, and a DTSO plate containing a DTS
plate as described above plus an overlay of 0.002% Gelrite (Sigma)
as shown in Table S1. Each isolate was subjected to three
additional rounds of colony purification, and then grown in liquid
phase followed by DNA extractions as detailed elsewhere [30,31].
Multilocus Sequence Analysis (MLSA)
The set of seven MLSA loci including the primer sequence, PCR
conditions, reactant concentrations, and sequencing conditions
were selected as a subset of the 12 loci described in detail elsewhere
[32]. Sequences of unique alleles are deposited in GenBank under
the accession numbers HQ123504-HQ123512, HQ123518-
HQ123527, HQ123532-HQ123534, and HQ123541-HQ123543,
and JQ339286–JQ339304. MLSA data were evaluated for all seven
loci using the Clonal Frame V1.2 software [35]. Runs of 250,000
iterations, after 100,000 burn-in iterations, were found to have
reached convergence based on between run comparisons. Arlequin
0
0.2
0.4
0.6
0 20 40 60 80 100 120 140
O.D. 600
Hours
Figure 5. Growth of M.16 strains under standard heterotrophic
conditions. Lines are color coded for strains assigned to the Red and
Blue groups. Negative control with no inoculum added is shown in
grey. Error bars show the variation in growth among three independent
replicate cultures.
doi:10.1371/journal.pbio.1001265.g005
Speciation in Archaea
PLoS Biology | www.plosbiology.org 7 February 2012 | Volume 10 | Issue 2 | e1001265

3.5 [34] was used for performing F
ST
calculations to test for
differentiation using MLSA data between springs or between two
years of sampling. Significance of F
ST
values was determined
through comparison to a permutation test, with p,0.05 considered
significant.
Genome Sequencing and Assembly
Ten strains from a single hot spring designated M.16, which is
approximately 25 cm in diameter, were selected for de novo
sequencing. Genomic preparations were done using a scaled up
version of the protocol used for previous DNA extractions [30].
Briefly, 1,000 ml of S. islandicus cultures were concentrated and the
pellet treated with 10 ml of 16GES and 7.5 ml of 7.5 M
ammonium acetate. After complete cell lysis, 15 ml of phenol:
chlorophorm:iso-amyl alcohol (25:24:1) was added to the mix,
homogenized, and subsequently centrifuged. Aqueous layer con-
taining DNA and RNA mix was recovered for subsequent isopropyl
alcohol and ethyl alcohol DNA purification. Extracted DNA was
treated with 1 unit of RNAse I (New England Laboratories) for
30 min at 37uC. DNA quality and concentration was evaluated by
gel electrophoresis and spectrophotometry. Genome sequencing
was done using a 454 Life Sciences GS-FLX sequencer (Roche) at
the University of Illinois Core Sequencing Facility (www.biotec.
illinois.edu). All 10 strains were initially ‘‘shotgun sequenced’’
reaching 8–206coverage. Six strains (M.16.02, M.16.22, M.16.23,
M.16.40, M.16.43, and M.16.47) were further selected for a second
round using ‘‘454 paired-end sequencing,’’ increasing the genome
coverage to 28–516(Table S5).
Genome assembly was done in three steps. First, reads were
assembled using the GS De Novo Assembler V.2.0.00 (Roche).
Second, the GS assemblies were evaluated using the Consed V.19
software [79], where contigs exceeding more than twice the
expected average coverage were reassembled using the ‘‘minia-
ssembly’’ tool with default settings. For the remaining contigs, a
fragment of 700 bp was removed from both ends and the removed
fragments reassembled individually with the miniassembly tool; if
the miniassembly tool produced a single contig, then the fragment
was joined back into the contig, but if more than one contig was
formed from reassembly, then the fragments were left as new
contigs to be resolved in the next assembly step. This second
assembly step addresses two possible artifacts: (a) separating reads
with sections of similar sequence, but belonging to different copies
of repetitive or duplicated elements, forming short contigs with
abnormally high coverage, and (b) resolving partially assembled
reads with masked regions (not contributing to contig consensus) at
both ends of a contig. The third assembly step used comparative
genomics to organize and scaffold contigs of the draft genomes
using the two completed M.16 genomes [30] followed by PCR
sequencing of small gaps. MUMmer 3.0 [80], the ‘‘move contigs’’
tool from the Mauve V.2.3.1 [81] and ABACAS [82] software,
was used to generate the draft genomes of the 10 newly sequenced
strains described in Table 1. Comparative genomics and paired-
end data predicted very few gaps in the draft genomes, none of
which is likely longer than 4 or 7 Kb. These gaps were joined by
short strings of ‘‘N’’ to artificially close draft genomes to facilitate
gene prediction and genome analysis. Four of seven genomes from
the Blue group and all three genomes from the Red group were
closed to exclude possibility of missing genes in gaps of draft
sequences that could contribute to ecological differentiation
between them. The draft versions of all genomes were deposited
as a Whole Genome Shotgun project at DDBJ/EMBL/GenBank
under the accession AHJK00000000, AHJL00000000, AHJM
00000000, AHJN00000000, AHJO00000000, AHJP00000000,
AHJQ00000000, AHJR00000000, AHJS00000000, AHJT
00000000. The version described in this paper is the first ver-
sion, AHJK01000000, AHJL01000000, AHJM01000000, AHJN
01000000, AHJO01000000, AHJP01000000, AHJQ01000000,
AHJR01000000, AHJS01000000, AHJT01000000 and are also
available at http://www.life.illinois.edu/Sulfolobus_islandicus.
ORFs in the newly sequenced genomes were predicted and
automatically annotated using the Rapid Annotation with Subsys-
tems Technology (RAST) V2.0 software and FIGfams set of protein
families [83].
Identification of the core genomic regions was done using the
ProgressiveMauve algorithm [81] on the set of 12 M.16 genomes.
The alignment contains regions shared among all genomes, shared
by a subset, or unique to one genome. The blocks shared by all
genomes were split where gaps of more than 20 alignment
positions were found, resulting in 155 core region fragments. The
cumulative length of the core region for each genome was
recorded and the fraction that they represent of each strain
genome is detailed in Table 1.
The core region was then analyzed using ClonalFrame with
default parameters [35]. Runs of 10,000 iterations were found to
have reached convergence based on between-runs comparisons.
The clonal genealogy reconstructed by ClonalFrame is shown in
Figure 1B.
Homologous Recombination Analysis with ClonalOrigin
ClonalOrigin is a Bayesian method to perform approximate
inference under the coalescent model with gene conversion [84]
using whole microbial genomes [36]. Assuming the correctness of
the clonal genealogy reconstructed by ClonalFrame (Figure 1B),
ClonalOrigin reconstructs recombination events that represent a
deviation from such vertical inheritance. Each recombination event
is characterized by the genomic segment it affects, as well as an
origin and destination on the clonal genealogy. By summarizing
these last two properties across all events, we reconstructed the
overall pattern of genetic flux between branches of the tree (Figures 2
and S1). We also studied the distribution of recombination segments
along the genome and found no correlation with the level of
polymorphism in 1, 000 bp windows of the alignment (Figure S2).
The absolute recombination rate was estimated by ClonalOrigin to
be 2.41610
24
per site per coalescent unit of time. A coalescent unit
of time is equal to the average length of a generation multiplied by
the effective population size. ClonalOrigin also estimated the ratio
of frequencies of recombination and mutation (r/h) and the average
tract length of recombination events (d). The relative effect of
recombination and mutation (r/m) was estimated using the formula
of Jolley et al. 2005 [37]: r/m= (r/h)*d*p, where pis the average
pairwise distance between two genomes. This formula is correct
assuming that any two genomes are equally likely to recombine, but
here we found (Figure 2) that recombination happens more often
between genomes from the same lineage so that r/m might be
overestimated by this formula. We also calculated r/m taking the
formula above with pequal to the average pairwise distance of two
genomes in the same lineage. This would be correct if recombina-
tion happened only between members of the same lineage, but here
we know that recombination also happens across lineage boundaries
(Figure 2). Thus, this second calculation is likely to underestimate r/
m, and taken together, the two calculations above provide us with a
lower and upper bound on r/m.
Recombination events were also evaluated by manual pairwise
scanning methods using the Recombination Detection Program 3
software [85] for all core genomic regions. A genomic section was
identified as evidence of a HR event after finding significant results
(p,0.01) with at least three of the following four tests as
implemented in the Recombination Detection Program 3
Speciation in Archaea
PLoS Biology | www.plosbiology.org 8 February 2012 | Volume 10 | Issue 2 | e1001265

software: RDP, GENECONV, MaxChi, and Bootscan. Overall,
these results show similar patterns to the ClonalOrigin analysis.
Evaluation of Integrated Elements and Distribution of
Variable Genome Components
Boundaries of variable segments from the Mauve alignment are
defined where there are core regions greater than 5 kb. Variable
gene segments that are smaller than 5 kb in length or contain less
than half variable gene segments were excluded from analysis
because the majority result from insertion elements.
Each variable segment was investigated for its distribution
among the 12 genomes. This allowed us to identify composite
segments with different distributions among strains. There was one
segment per genome that was excluded from analysis due to a
complicated pattern of shared and unique content that made
segment boundaries very difficult to assign. In M.16.27, this
segment is 74 kb long and located at 997,394–1,071,175 in the
genome. To identify integrated mobile elements, variable gene
segments were compared to a database of Sulfolobus mobile
elements [40] including elements that are integrated into S.
islandicus genomes [30,86] using BLASTN (e,0.001, 2F f) and to
the NCBI nr database using BLASTX (e,1E-5, 2F f).
BLASTN was used to compare variable segments to the rest of
the S. islandicus genomes from the M.16 hot spring in order to
assess Mauve’s assignment of variation.
The genome in which each variable segment was longest was
compared to each other genome in which the segment is present,
and the level of nucleotide identity was calculated between each of
them as the number of matching nucleotides divided by the length
of the match as reported by BLASTN and averaged over all of the
pairs. The coverage of the longest segment was also calculated
pairwise as with percent identity, with the total length of matching
nucleotides divided by the length of the longest segment and
averaged over all pairs. Genes present in the variable segments
that separate Blue from Red were compared to NCBI’s nr
database with BLASTP (e,0.001, 2F f) if they were complete in
every genome from either Blue or Red and if they were not core
genes.
Genomic Patterns of Differentiation
A table containing the position and nucleotide of every SNP in
the core genome alignment was exported from the genome
alignment using the ‘‘Export SNPs’’ tool from the Mauve software.
SNPs found within and between the Blue and Red species were
used for sliding window F
ST
evaluations. F
ST
values were
calculated for sliding windows of 10,000 bps moving in 5,000 bp
steps across the genome of M.16.27. Arlequin 3.5 [34] was used
for calculating F
ST
to test for differentiation in the M.16
populations. Low regions were defined as beginning and ending
where windows of F
ST
values were less than 0.5. F
ST
values were
not calculated for empty windows or where sequence found in
M.16.17 is not present in all strains. F
ST
values for genes were
calculated using the same methods but applying the coordinates of
each gene to SNPs exported from the core alignment from Mauve
rather than sliding windows.
Pairwise d
N
/d
S
ratios were calculated using the ORF clusters
identified by MCL analysis [87]. All clusters containing a single
copy of an ORF per genome (2,187) were evaluated for all
pairwise d
N
,d
S
, and d
N
/d
S
ratios using the SNAP program
(http://hiv-web.lanl.gov/) [88] with the Nei and Gojobori method
as described elsewhere [89]. Sequence alignments for clusters that
resulted in d
N
/d
S
values greater than 1.0 were manually checked
to resolve homopolymer indels from the 454 sequence data.
Growth under Heterotrophic Conditions
The 12 M.16 strains were evaluated for their growth charac-
teristics in rich liquid media. As initially described by Whitaker et
al. (2003) [31], culturing conditions were at pH 3.5, 75–78uC and
with media containing a basal mineral solution, 0.1% Dextrin
(Fluka) and 0.1% Tryptone (Difco). All cultures were initiated
using exponentially growing cultures to inoculate 50 ml liquid
cultures in 250 ml flasks at an estimated starting concentration of
2610
3
cells/ml. Cultures were incubated under static conditions
and growth was followed by OD600 reads up to the sixth day of
incubation.
Supporting Information
Figure S1 Heatmap showing absolute number of recombinant
events between donor/recipient pair of branches of the core
genome phylogeny of 12 S. islandicus strains. For each pair
observed, number of events (top) is compared to the number of
expected events under the prior used in the ClonalOrigin model
(bottom). As in Figure 2, recombination frequency is measured
relative to its expectation under the prior used in the ClonalOrigin
model and color coded according to the upper left color/
magnitude legend (light blue and blue for rates below a 1:1 ratio
and yellow to red for rates above 1:1). Light gray cells represent
non-significant ratios with less than four observed and expected
events. White shows number of events that match the prior
expectations. Names of strains are color coded as Blue and Red
groups.
(EPS)
Figure S2 Relationship between genetic distances and recom-
bination frequency as measured using ClonalOrigin (A and B) or
experimental data (C) over the range of genetic distances observed
in S. islandicus. Recombination frequency is normalized to 1 being
the maximum number of events observed. (A) Sulfolobus islandicus
and (B) Bacillus cereus [1]; (C) blue circles, Saccharomyces cerevisiae [2];
red diamonds, Streptococcus pneumoniae [3]; green triangles, Bacillus
subtilis [4]; purple squares, Escherichia coli [5].
(EPS)
Figure S3 Maximum likelihood phylogeny using the nucleotide
sequences of S-layer protein from published S. islandicus strains [6–
9]. Phylogeny was produced in MEGA 5 [10] using the GTR+G
model determined to best fit the data using Modeltest [11]. Each
node on this phylogeny has greater than 80% support out of 1,000
bootstrap replicates. Strains from this study are highlighted
according to their species affiliation (Red or Blue) as in Figure 2.
(EPS)
Table S1 List of S. islandicus strains.
(XLSX)
Table S2 MLSA allele assignment for S. islandicus strains.
(XLSX)
Table S3 Pairwise F
ST
values for each hot spring sample
calculated from seven concatenated MLSA loci.
(XLSX)
Table S4 Variable genome segments among 12 S. islandicus
genomes.
(XLSX)
Table S5 Sequencing statics for 10 new S. islandicus genomes.
(XLSX)
Text S1 Supporting references.
(DOCX)
Speciation in Archaea
PLoS Biology | www.plosbiology.org 9 February 2012 | Volume 10 | Issue 2 | e1001265

Acknowledgments
We would like to thank K. Wright and A. Hernandez at the W. M. Keck
Center for Comparative and Functional Genomics for their assistance on
genome sequencing and D. Grogan for initial isolation of S. islandicus strains
from Kamchatka samples collected in 2000. In addition we thank F.
Cohan, N. Barton, and four anonymous reviewers for helpful comments on
the manuscript.
Author Contributions
The author(s) have made the following declarations about their
contributions: Conceived and designed the experiments: RJW HCQ.
Performed the experiments: RJW HCQ AH. Analyzed the data: RJW XD
HCQ NLH AH AD MLR DK . Contributed reagents/materials/analysis
tools: XD AD. Wrote the paper: RJW XD.
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