Novel Phylogenetic Approaches to Problems in
Microbial Genomics
by
Lawrence A. David
Submitted to the Computational & Systems Biology Initiative
in partial fulfillment of the requirements for the degree of
PhD
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
September 2010
c Massachusetts Institute of Technology 2010. All rights reserved.
�
Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Computational & Systems Biology Initiative
August 26th, 2010
Certified by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Eric J. Alm
Assistant Professor
Thesis Supervisor
Accepted by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Chris Burge
Chair, Ph.D. Graduate Committee
Novel Phylogenetic Approaches to Problems in Microbial
Genomics
by
Lawrence A. David
Submitted to the Computational & Systems Biology Initiative
on August 26th, 2010, in partial fulfillment of the
requirements for the degree of
PhD
Abstract
Present day microbial genomes are the handiwork of over 3 billion years of evolution.
Comparisons between these genomes enable stepping backwards through past evolutionary events, and can be formalized using binary tree models known as phylogenies.
In this thesis, I present three new phylogenetic methods for gaining insight into how
microbes evolve. In Chapter 1, I introduce the algorithm AdaptML, which uses strain
ecology information to identify genetically- and ecologically-distinct bacterial populations. Analysis of 1000 marine Vibrionaceae strains by AdaptML finds evidence
that niche adaptation may influence patterns of genetic differentiation in bacteria.
In Chapter 2, I introduce the algorithm AnGST, which can infer the evolutionary
history of a gene family in a chronological context. Analysis of 3968 gene families
drawn from 100 modern day organisms with AnGST reveals genomic evidence for
a massive expansion in microbial genetic diversity during the Archean eon and the
gradual oxygenation of the biosphere over the past 3 billion years. Lastly, I introduce in Chapter 3 the algorithm GAnG, which can construct prokaryotic species trees
from thousands of distinct gene trees. GAnG analysis of archaeal gene trees supports
hypotheses that the Nanoarchaeota diverged from the last ancestor of the Archaea
prior to the Crenarchaeota/Euryarchaeota split.
Thesis Supervisor: Eric J. Alm
Title: Assistant Professor
2
Acknowledgments
Funding
I am deeply grateful for the research support provided by:
• National Defense Science & Engineering Graduate Fellowship (DoD)
• Whitaker Health Sciences Fund Fellowship (MIT)
Personal
Many people have deliberately or unwittingly helped me complete my doctoral work.
My words of gratitude below are only a small installment against a lifetime of welcome
debt.
Foremost, I would like to thank my advisor, Eric Alm, who gave me the freedom
to work on the problems I found interesting and the scientific training to solve them.
His unique enthusiasm over these past years has helped fuel my research and provided
me with personal joys that I will always cherish.
I have been also remarkably fortunate to have joined a group of wonderful colleagues here at MIT. Mark Smith, Chris Smillie, Greg Fournier, Sarah Preheim, Ines
Baptista, Matt Blackburn, Yonatan Friedman, Alexandra Konings, Claudia Bauer,
and Albert Wang have been a source of thoughtful discussion and much needed levity. In particular, the old guard of Arne Materna, Sean Clarke, Sonia Timberlake,
and Jesse Shapiro is enshrined in some of my fondest memories of graduate school.
Classmates in my department, especially Robin Friedman, Charles Lin, and Michelle
Chan, have been inspirational young-scientists who I have looked to for advice and
motivation.
A host of caring individuals enabled me to reach the closing stages of my graduate
career. Sheila Frankel, Darlene Strother, Darlene Ray, James Long, and Bonnie Lee
Whang supplied administrative and moral support. My graduate committee, whose
membership has included Ed DeLong, Drew Endy, Manolis Kellis, Dianne Newman,
Howard Ochman, (and unofficially Martin Polz), generously set aside time to provide
me with professional advice and letters of recommendation. [greg] ensured that my
cluster jobs ran smoothly and provided crucial UNIX humor.
I also acknowledge my friends and family, who conspired to make graduate school
the shortest five years of my life. Within the Parsons laboratory, David GonzalezRodriguez, Piyatida Hoisungwan, Marcos, and Crystal Ng, unfailingly made me excited to come to lab each morning. MacGregor’s F-entry has been my adopted undergraduate family, and I hold dear my memories of living among them. My sister
has continuously given me her love, and my father and mother have selflessly worked
to give me the opportunities I presently enjoy. Finally, my wife Christina has simply
been the nicest person I’ve ever met.
3
Contents
I
Introduction
II
5
Contributions
13
1 Resource partitioning and sympatric differentiation among closely
related bacterioplankton
15
2 Rapid evolutionary innovation during an Archean Genetic Expansion
51
3 Building prokaryotic species trees from thousands of gene trees
92
III
Conclusion
113
IV
Bibliography
116
4
Part I
Introduction
5
Overview
Present day microbial genomes are the handiwork of over 3 billion years of evolution.
Comparisons between these genomes enable stepping backwards through evolutionary
history – genetic features present across a wide diversity of genomes likely arose
more anciently than features found in subsets of related genomes. This intuition is
formalized using binary tree models of sequence evolution known as phylogenetic trees.
Phylogenies propose a series of ancestral sequence divergence events that explain the
similarity of extant sequences. These trees can in turn be used to build models for
the evolution of organismal phenotypes, such as preferred environment or lifestyle.
However, prokaryotes’ capacity for horizontal gene transfer (HGT) can require regions
of the same genome to be associated with different phylogenetic trees, and ultimately
obscure which phylogenetic tree best represents overall genome evolution.
In this thesis, I present three novel phylogenetic approaches for inferring microbial evolutionary history through the comparison of gene sequences. The remainder
of Part I briefly describes the research context in which I developed: AdaptML, an
algorithm for detecting signatures of ecological adaptation influencing bacterial genetic differentiation; and AnGST, an algorithm for inferring the series of HGT, gene
duplication, and gene loss events that gave rise to a gene family. I go on in Chapter 1
of Part II to use AdaptML to identify genetically- and ecologically-distinct clusters of
Vibrionaceae coexisting in a marine environment. In Chapter 2, I use AnGST to infer
patterns in microbial genome evolution over the past 3.8 billion years. I use AnGST
again in Chapter 3 in the development of GAnG, a new method for constructing
prokaryotic species trees from thousands of gene trees. I conclude this thesis in Part
III with a summary of the chapters and a brief discussion of ongoing and future work
with AdaptML, AnGST, and GAnG.
6
Detecting relationships between genetic and ecological differentiation in bacteria
Distinct groups of closely-related bacteria, or phylogenetic clusters, are a recurring
pattern of genetic differentiation among bacterial isolate housekeeping genes [1–3].
Ecological adaptation is suspected of playing a role in cluster formation [4]. According to the ecotype model, genetically-distinct bacterial populations form when
bacterial populations adapt to an ecological niche and are repeatedly purged of genetic variation through periodic selection events [5]. However, a theoretical study
has shown that genetically distinct sub-populations can form under a neutral model
that either prohibits recombination, or simulates high within-cluster recombination
[6]. Alternatively, a recent phylogenetic analysis of eight sequenced Vibrio isolates
has found evidence for a combined model featuring both ecological adaptation and
neutral processes contributing to genetic differentiation. Under this model, the introduction of niche-adaptive alleles initially erodes sympatry in a bacterial population.
Reduced gene flow between niche-adapted bacteria and the remaining population
subsequently yields genetically-distinct subgroups [7]. Ultimately, if niche adaptation drives the formation of genetically-distinct bacterial groups, members of each
group should inhabit a common niche. Mathematical models capable of identifying
both genetically- and ecologically-cohesive bacterial groups can thus be used to help
resolve the role of ecological adaptation in the genetic differentiation of bacteria.
Several existing statistical methods, such as the Fst test, the P test, and Unifrac,
can evaluate the null hypothesis that phylogenetic clusters do not exhibit distinct
ecological associations. These tests assume that bacterial sequences are annotated
with ecological metadata describing the environment each sequence was harvested
from. The Fst test compares the genetic diversity among bacteria annotated as
sharing the same environment to the genetic diversity measured across all sampled
sequences. Low genetic diversity within a particular environment, coupled with high
genetic diversity between environments, is evidence for rejection of the null hypothesis
of no association between genetic clustering and bacterial ecology [8]. Alternatively,
7
the P test builds a phylogeny of strain sequences, labels leaves by their environmental
association, and uses a parsimony model to infer the number of times ancestral strains
on the tree changed environmental associations. Low parsimony scores are evidence
for rejecting the null hypothesis [8]. Lastly, the Unifrac model combines elements of
both the Fst and P test, utilizing genetic distances and strain tree topology to test
the relationship between strain genetic clustering and associated environment [9].
One weakness, however, of the Fst, P, and Unifrac statistics is their potential for
erroneously reporting no association between genetic clustering and ecology when the
ecological forces driving cluster formation are unmeasured or improperly annotated.
For example, consider a bacterial sequence cluster caused by adaptation to conditions
between 20◦ -30◦ C. An association between this cluster and ecology would go unrecognized by Fst, P, or Unifrac analyses if temperature data was not collected, or if
temperature data were discretized into only two ranges: < 25◦ C and ≥25◦ C. Thus,
these statistics may not be appropriate for analyzing the evolution of bacteria whose
niche composition is unknown or highly uncertain, as environmental parameters describing these bacteria’s niche may not have been measured.
Another inference algorithm, Ecotype Simulation (ES), can identify geneticallyand ecologically-distinct clusters in a manner insensitive to how ecological parameters
are measured [10]. ES finds ecotypes by fitting a maximum likelihood model onto a
gene phylogeny. This model estimates the rates of ecotype formation, periodic selection, and genetic drift, as well as the total number of ecotypes present. Identified
ecotypes can subsequently be analyzed using ecological measurements and multivariate statistics in order to confirm that niche-adaptation has taken place and identify
environmental parameters that define the niche. Recent application of this approach
discovered ecotypes among Bacillus strains sampled from Death Valley, CA, which
could be distinguished by adaptation to solar exposure and soil texture [11]. One
drawback to the ES algorithm, however, is that it cannot detect nascent ecotype
formation events that have not yet undergone multiple series of periodic selections.
In Chapter 1, I present a new method named AdaptML, which uses a maximum likelihood model to identify genetically- and ecologically-coherent clusters of
8
bacterial strains. This model explicitly combines genetic information embedded in
sequence-based phylogenies with environmental sampling data. Recent niche adaptation events, characterized by ecologically coherent clusters with minimal genetic
distinction from a parent clade, can be captured by the model. Although AdaptML
cannot detect ecological associations with unmeasured environmental parameters, the
algorithm can account for environmental parameter discretization schemes that would
generally confound previous methods for detecting ecological associations. To do this,
I introduce the model concept of a “habitat.” Habitats are characterized by discrete
probability distributions describing the likelihood that a strain adapted to a habitat
will be sampled from a given ecological state (e.g. at a particular location in an estuary). Habitats are not defined a priori but rather learned directly from the sequence
and ecological data using an Expectation Maximization routine. Once habitats are
defined, I learn a maximum likelihood model for the evolution of habitat association
on the tree. Randomization experiments can be used to determine which sequence
clusters show a statistically-significant association with a given habitat.
9
Inferring the evolutionary history of microbial gene
families
Microbial genomes do not evolve solely by point mutation [12, 13]. Comparison of
gamma-proteobacterial genomes suggests gene loss events eliminated thousands of
genes from the ancestor of the Buchnera following its adoption of an endosymbiotic
lifestyle [14]. Genes can be gained, via either the duplication of small regions of the
genome [15], or via the duplication of the entire genome itself, as has been shown for
yeast [16]. Gene gain is also possible via HGT and is a well-known source of genomic
diversity among the prokaryotes [12]. Cases of HGT have also been identified between
eukaryotes [17, 18] and even from bacteria to animals [19, 20]. Models that can infer
when genes have undergone loss, duplication, or HGT, and when genes have been
vertically inherited, are necessary for understanding the relative contribution of these
four mechanisms to genome evolution.
Algorithms for inferring the evolutionary history of gene families vary according to
their reliance on phylogenetic models and how they account for gene gain events (Table 1). Phylogeny-free methods utilize features such as GC-bias to detect xenologous
genes [21, 22], or within-genome BLAST searches to find evidence for past duplication
events [23]. More complex approaches, known as presence-absence models, construct
a phylogeny of sampled species and identify which leaves on the tree are represented
in a gene family of interest. Parsimony algorithms can then be used to identify a set of
ancestral gene duplication, gene loss, or HGT events to explain the observed pattern
of gene presence and absence on the species tree [24–27]. However, presence/absence
algorithms may underestimate the amount of HGT in a gene family history, since
frequent HGT events can produce presence/absence patterns similar to those caused
by gene birth at a deep node, followed by vertical descent. More sensitive models
capable of differentiating between these scenarios utilize gene sequence information,
in addition to a species tree. Quartet methods quantify how strongly quartets of
orthologous genes support each of the three possible 4-taxon trees representing their
evolutionary history [28, 29]. Quartets that strongly support topologies discordant
10
Model
GC-bias
BLAST-hits
Presence/absence
Quartet mapping
Parsimony reconciliation
Probabilistic reconciliation
Species
tree
Gene
tree
No
No
Yes
Partial
Yes
No
No
No
Partial
Yes
Unc.
gene
trees
Yes
No
Yes
Yes
Yes
Finds
HGT
Finds
dup.
Refs.
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
No
Yes
[21, 22]
[23]
[24–27]
[28, 29]
[30, 31]
Yes
No
[33, 34]
Table 1: Selection of existing models used to infer gene family evolutionary
histories: Models are characterized by their explicit usage of species trees and gene trees,
their consideration of gene tree uncertainty, and their ability to detect HGT and duplication events. Note that only References [27, 31] can find HGT and duplication events
simultaneously.
with the expected species tree are evidence for HGT within the gene family. More
elaborate “reconciliation” models compare full gene and species trees in order to infer a precise phylogenetic location for each inferred evolutionary event. Parsimony
reconciliation models [30, 31], however, will infer spurious events if phylogenetic construction errors are present in the gene tree [32]. Newer probabilistic reconciliation
algorithms have been developed to deal with these potential inaccuracies [33, 34].
Gene family evolutionary history models can also be partitioned according to
whether they account for gene gain using duplication or HGT events. With the
exception of Snel and Charleston’s algorithms [27, 31], evolutionary history models
usually account for only one of these two events. The specificity of these models may
be caused by self-reinforcing biases associated with the expected modes of eukaryotic
and prokaryotic genome evolution. The relative rarity of reported HGT events among
eukaryotes, compared to duplication events, likely encourages analyses of eukaryotic
genome evolution using tools specialized only to detect gene duplications. By contrast,
recognition of how HGT can accelerate prokaryotic adaptation and blur species lines
has probably reduced interest in broad surveys of potential prokaryotic duplication.
Exceptions to this proposed bias among prokaryotic studies do exist, however, as
11
Gevers et al. cataloged gene duplications in 106 bacterial genomes [23] and Snel
searched for both HGT and duplication among 17 archaeal and bacterial genomes
[27]. Increasing examples of HGT among eukaryotes [17, 18] are also fueling new
interest in systematically searching for HGT across the eukaryotes [35]. Bias against
the creation of models that account for both HGT and duplication is also likely due
to issues of model complexity. In certain scenarios, gene duplication and gene loss
can produce gene tree topologies similar to those yielded by HGT [36]. A combined
HGT/duplication inference model must be capable of recognizing this scenario and
proposing plausible HGT and duplication scenarios. Moreover, a combined model
requires defining a metric to choose which of these scenarios is preferable.
In Chapter 2, I present a new reconciliation method for inferring a set of gene loss,
gene duplication, and HGT events that explain topological incongruities between a
species tree and a gene tree. I named this algorithm the Analyzer of Gene & Species
Trees, or AnGST. AnGST was inspired by a gene family evolution model originally
designed for problems in biogeography and the inference of gene duplication and gene
loss events [30]. Also referred to as a host-parasite model, this approach seeks to infer
which ancestral genome (the host) on the reference tree possessed each ancestral gene
copy (the parasite). AnGST employs a generalized parsimony framework in order to
choose when duplication events should be inferred instead of HGT scenarios. This
framework assigns scores to each type of evolution event and returns the evolutionary
history with the lowest overall score. AnGST can further minimize reconciliation
scores by reconciling multiple gene tree bootstraps simultaneously and combining
their lowest scoring subtrees into a single chimeric gene tree. This bootstrap amalgamation step reduces the opportunity for poorly resolved gene tree subtrees to cause
the spurious inference of evolutionary events.
12
Part II
Contributions
13
Chapter 1
Resource partitioning and
sympatric differentiation among
closely related bacterioplankton
Dana E. Hunt*, Lawrence A. David*, Dirk Gevers, Sarah P. Preheim,
Eric J. Alm, Martin F. Polz
*These authors contributed equally to this work.
This chapter is presented as it originally appeared in Science 320, 1081 (2008).
Corresponding Supplementary Material is appended.
Chapter 1
Resource partitioning and
sympatric differentiation among
closely related bacterioplankton
Identifying ecologically differentiated populations within complex microbial communities remains challenging, yet is critical for interpreting the
evolution and ecology of microbes in the wild. Here we describe spatial
and temporal resource partitioning among Vibrionaceae strains coexisting in coastal bacterioplankton. A quantitative model (AdaptML) establishes the evolutionary history of ecological differentiation, thus revealing
populations specific for seasons and life-styles (combinations of free-living,
particle, or zooplankton associations). These ecological population boundaries frequently occur at deep phylogenetic levels (consistent with named
species); however, recent and perhaps ongoing adaptive radiation is evident in Vibrio splendidus, which comprises numerous ecologically distinct
populations at different levels of phylogenetic differentiation. Thus, environmental specialization may be an important correlate or even trigger of
speciation among sympatric microbes.
15
Microbes dominate biomass and control biogeochemical cycling in the ocean, but
we know little about the mechanisms and dynamics of their functional differentiation
in the environment. Culture-independent analysis typically reveals vast microbial diversity, and although some taxa and gene families are differentially distributed among
environments [37, 38], it is not clear to what extent coexisting genotypic diversity can
be divided into functionally cohesive populations [37, 39]. First, we lack broad surveys
of nonpathogenic free-living bacteria that establish robust associations of individual
strains with spatiotemporal conditions [40, 41]; second, it remains controversial what
level of genetic diversification reflects ecological differentiation. Phylogenetic clusters have been proposed to correspond to ecological populations that arise by neutral
diversification after niche-specific selective sweeps [5]. Clusters are indeed observed
among closely related isolates (e.g., when examined by multilocus sequence analysis)
[4] and in culture-independent analyses of coastal bacterioplankton [42]. Yet recent
theoretical studies suggest that clusters can result from neutral evolution alone [6],
and evidence for clusters as ecologically distinct populations remains sparse, having
been most conclusively demonstrated for cyanobacteria along ocean-scale gradients
[43] and in a depth profile of a microbial mat [44]). Further, horizontal gene transfer
(HGT) may erode the ecological cohesion of clusters if adaptive genes are transferred
[45], and recombination can homogenize genes between ecologically distinct populations [46]. Thus, exploring the relationship between phylogenetic and ecological
differentiation is a critical step toward understanding the evolutionary mechanisms
of bacterial speciation [6].
In this study, we investigated ecological differentiation by spatial and temporal
resource partitioning in coastal waters among coexisting bacteria of the family Vibrionaceae, which are ubiquitous, metabolically versatile heterotrophs [47]. The coastal
ocean is well suited to test population-level effects of microhabitat preferences, because tidal mixing and oceanic circulation ensure a high probability of migration, reducing biogeographic effects on population structure. In the plankton, heterotrophs
may adopt alternate ecological strategies: exploiting either the generally lower concentration but more evenly distributed dissolved nutrients or attaching to and degrading
16
small suspended organic particles, originating from algal exopolysaccharides and detritus [39]. Bacterial microhabitat preferences may develop because resources are
distributed on the same scale as the dispersal range of individuals, due to turbulent
mixing and active motility [48]. Of potential microhabitats, particles represent abundant but relatively short-lived resources, as labile components are rapidly utilized (on
time scales of hours to days) [49, 50], implying that particle colonization is a dynamic
process. Moreover, particulate matter may change composition with macroecological conditions (such as seasonal algal blooms). Zooplankton provide additional, more
stable microhabitats; vibrios attach to and metabolize chitinous zooplankton exoskeletons [51, 52] but may also live in the gut or occupy niches specific to pathogens. The
extent to which microenvironmental preferences contribute to resource partitioning in
this complex ecological landscape remains an important question in microbial ecology
[53].
We aimed to conservatively identify ecologically coherent groups by examining
distribution patterns of Vibrionaceae genotypes among free- living and associated
(with suspended particles and zooplankton) compartments of the planktonic environment under different macroecological conditions (spring and fall) (Figs. 1.3 & 1.5).
Because the level of genetic differentiation at which ecological preferences develop is
not known, we focused on a range of relationships (0 to 10% small subunit ribosomal RNA (rRNA) divergence) among co-occurring vibrios [54]. Particle-associated
and free-living cells were separated into four consecutive size fractions by sequential
filtration (four replicate water samples, each subsampled with at least four replicate
filters per size fraction); each fraction contained organisms and dead organic material
of different origins (detailed in the supporting online material [SOM]; Section 1.2).
For simplicity, we refer to these fractions as enriched in zooplankton (≥63 mm), in
large (5 to 63 mm) and small (1 to 5 mm) particles, and in free-living cells (0.22 to 1
mm) (Fig. 1.5B). The 1- to 5-mm size fraction was somewhat ambiguous, probably
containing small particles as well as large or dividing cells; however, it provided a firm
buffer between obviously particle-associated (>5 mm) and free-living (<1 mm) cells.
Vibrionaceae strains were isolated by plating filters on selective media, previously
17
shown by quantitative polymerase chain reaction to yield good correspondence between genotypes recovered in culture and those present in environmental samples [54].
Roughly 1000 isolates were characterized by partial sequencing of a protein-coding
gene (hsp60 ). To obtain added resolution, between one and three additional gene
fragments (mdh, adk, and pgi ) were sequenced for over half of the isolates (SOM),
including V. splendidus strains, the most abundant group [54].
Our rationale for testing environmental associations grows out of the following
considerations. First, as in most ecological sampling, the true habitats or niches
are unknown and can only be observed as projections onto the sampling dimensions
(“projected habitats”). Thus, associations can be detected as distinct distributions
of groups of strains if habitats/niches are differentially apportioned among samples.
Second, the lack of an accepted microbial species concept implies that it is imprudent
to use any measure of genetic relationships to define a priori the populations whose
environmental association should be assessed. Therefore, we first tested the null
hypothesis that there is no environmental association across the phylogeny of the
strains. We then refined such estimates by developing a new model to simultaneously
identify populations and their projected habitats. Finally, these model-based results
were tested with nonparametric empirical statistics.
The initial null hypothesis of no association between phylogeny and ecology is
strongly rejected (seasons: p < 10−79 ; size fractions: p < 10−49 ) by comparing the
parsimony score of observed environments on the tree to that expected by chance [55]
(SOM), confirming the visual impression of differential patterns of clustering among
seasons and size fractions (Fig. 1.1A). This result is robust toward uncertainty in the
phylogeny, which should diminish but not strengthen associations, and is confirmed
by introducing additional uncertainty in the phylogeny (Fig. 1.6). The observed
overall association with season and size fraction therefore suggests that water-column
vibrios partition resources, but neither provides insights into the phylogenetic bounds
of populations or the composition of their habitats.
We therefore developed an evolutionary model (AdaptML) to identify populations as groups of related strains sharing a common projected habitat, which reflects
18
their relative abundance in the measured environmental categories (size fractions and
seasons) (SOM). In practice, the model inputs are the phylogeny, season, and size
fraction of the strains. It then maps changes in environmental preference onto the
tree by predicting projected habitats for each extant and ancestral strain in the phylogeny. Although similar in spirit to existing parsimony, likelihood, and Bayesian
methods, which map ancestral states onto trees [56], the model accounts for the complexities and uncertainties of environmental sampling. First, projected habitats can
span multiple sampling dimensions to account for complex life cycles (such as time
spent in multiple true habitats) and problems inherent in environmental sampling:
Discrete samples rarely equate to true habitats, and true habitats are frequently misplaced among their typical sample categories (for example, zooplankton fragments
may also be found in smaller size fractions). Second, projected habitats can span
multiple phylogenetic clusters to allow for the possibility that clusters may arise neutrally or that the relevant parameters differentiating them ecologically have not been
measured.
Briefly, AdaptML builds a hidden Markov model for the evolution of habitat
associations: Adjacent nodes on the phylogeny transition between habitats according
to a probability function that is dependent on branch length and a transition rate,
which is learned from the data (SOM) (Fig. 1.7). Subsequently, we optimize the
model parameters (the transition rate and the composition of each projected habitat)
to maximize the likelihood of the observed data. Finally, we use a simple ad hoc rule
for reducing noninformative parameters: We merge habitats that converge to similar
distributions (simple correlation of distribution vectors >90%) during the modelfitting procedure (SOM). This reproducibly identified six nonredundant habitats for
the observed data set (HA to HF in Figs. 1.1B and 1.9). Moreover, the algorithm acts
conservatively, as suggested by two tests. First, the model did not overfit the data
when there was no ecological signal present: When the environments were shuffled,
only a single generalist habitat (evenly distributed over all size fractions and seasons)
was recovered. Second, when simulated habitats were used to generate environmental
assignments, the model usually identified a number of habitats equal to or less than
19
the true number present (Fig. 1.10).
The analysis suggests that a single bacterial family coexisting in the water column
resolves into a striking number of ecologically distinct populations with clearly identifiable preferences (habitats). The algorithm identified 25 populations, associated
with one of the six habitats defined by distinct distributions of isolates over seasons
and size fractions (Fig. 1.1 and Fig. 1.11). Most clusters have a strong seasonal
signal; interestingly, two pairs of highly similar habitats are observed in both seasons
(Fig. 1.1B). The first of the habitat pairs corresponds to populations occurring both
free-living and on particles but lacking zooplankton-associated isolates (HB and HC );
the second indicates a preference for zooplankton and large particles (HE and HF )
(Fig. 1.1B). The remaining two habitats were season-specific. Habitat HA combines
all primarily free-living populations in the fall, whereas habitat HD identifies a second
particle- and zooplankton-associated group in spring, but unlike HE and HF it has a
higher proportion of large particles and maps onto a single small group (G25) (Fig.
1.1). However, we cannot place high confidence in the absence of the free-living habitat in the spring, because relatively few strains were recovered from that fraction.
Moreover, the distribution of individual populations among seasons and size fractions varies considerably, with remarkably narrow preferences for some populations
whereas others are more broadly distributed. For example, V. ordalii (G3) is almost
exclusively free-living in both seasons, whereas V. alginolyticus (G5) has a significant
representation in both zooplankton and free-living size fractions but occurs exclusively in the fall (Fig. 1.1, A and B). The sequences of three additional genes for V.
alginolyticus isolates were identical, arguing against misidentification due to recombination or additional population substructuring. Similarly, there was good agreement
when two different gene phylogenies (hsp60 and mdh) were used to identify habitats
for V. splendidus (Fig. 1.12), although fewer habitats were identified using the mdh
tree, most likely because it is less well-resolved. Overall, across all vibrios sampled,
association with the zooplankton-enriched and free-living fractions dominated, and
although several populations contain particle-associated isolates, only a few appear
to be specifically particle-adapted. Because vibrios are generally regarded as particle
20
and zooplankton specialists [47], this observed partitioning offers new insight into
their ecology.
Thus, in spite of the highly variable conditions of the water column, populations appear to finely partition resources, especially because our habitat estimates
are conservative, as clusters occupying the same habitat may be differentiated along
additional (unobserved) resource axes. For example, different zooplankton-associated
groups may be host- or body region-specific, and the strong seasonal signal of most
clusters may be due to a variety of factors; however, temperature is a likely candidate because it has so far arisen as the strongest correlate of microbial population
changes both over a seasonal cycle [57] and along ocean-scale gradients [43]. Finally, populations, which appear unassociated in our study, may be true generalists
with respect to the resource space sampled or may be adapted to environments not
sampled in this study, such as animal intestines or sediments [47]. Despite these uncertainties, the observed strong partitioning among associated and free-living clusters
may have important implications for population biology in the bacterioplankton. As
recently suggested [6], for attached bacteria, the effective population size (Ne ) may
be considerably smaller than the census size because colonization serves as a population bottleneck, whereas in free-living clusters, Ne may be closer to the census
size. Although computing the true magnitude of Ne in microbial populations remains
controversial [58], it is an important parameter that determines the relative strength
of selection and drift. Thus, attached and free-living populations may evolve under
different constraints [6].
The phylogenetic structure of populations also provides insights into the history
of habitat switches. Deeply branching populations may have remained associated
with habitats over long evolutionary time, and shallow branches may have diversified
more recently (Fig. 1.1, A and B). These stable habitat-associated clusters roughly
correlate to named species within the Vibrionaceae. For example, V. ordalii (G3) and
Enterovibrio norvegicus (G2) both represent clusters without close relatives containing > 50 isolates, which are overwhelmingly predicted to follow primarily free-living
(HA ) and free-living/particle-associated lifestyles (HC ), respectively (Fig. 1.1A). On
21
the other hand, some very closely related clusters are associated with different habitats; V. splendidus, which is composed of strains that are ∼99% identical in rDNA
gene sequence [54], differentiates into 15 microdiverse habitat-associated clusters, of
which one is distributed roughly evenly among both seasons, and 9 and 5 predominantly occur in spring and fall, respectively. Thus, V. splendidus appears to have
ecologically diversified, possibly by invading new niches or partitioning resources at
increasingly fine scales.
Recent or perhaps ongoing radiation by sympatric resource partitioning is most
strongly suggested for two nested clusters within V. splendidus, where groups of
strains differing by as little as a single nucleotide in hsp60 display distinct ecological
preferences (Fig. 1.1A, insets, and Fig. 1.3). These strains were isolated from multiple
independent samples and thus do not represent clonal expansion, suggesting that this
may reflect a true habitat switch; nonetheless, homologous recombination could also
move alleles between distantly related, ecologically distinct clusters, creating spurious
phylogenetic relationships, which can be detected by comparison with other genes.
Multilocus sequence analysis shows that for nested cluster I, a close relationship
was artificially created because hsp60 gene phylogeny is discordant with three other
genes (Fig. 1.2). However, this still represents a habitat switch, just at a slightly
larger sequence distance, as I.A is nested within the much larger G16 cluster in
both the hsp60 and the mdh-pgi -adk phylogenies. For the second nested cluster,
the three additional genes confirm partial separation of the subclusters II.A and II.B
by a single base pair difference in one of the genes, whereas the other genes consist
of identical alleles. This reinforces the idea that subcluster II.A is not incorrectly
grouped because of recombination, despite its distinct ecological affiliation (Fig. 1.2).
In combination, these data support the idea that there is ecological differentiation
among recently diverged genotypes and show that such changes might be recognized
in protein-coding genes as soon as they accumulate (neutral) sequence changes.
How might adaptation to a new habitat relate to speciation, the generation of distinct clusters of closely related bacteria? Mathematical modeling has recently shown
that the dynamics of speciation depend on the ratio of homologous recombination to
22
mutation rates (r/m) [6]. When this ratio per allele exceeds ∼1, populations transition from essentially clonal to sexual, with the major consequence that selection is
probably required for the formation of clusters [6]. Our preliminary multilocus sequence analysis on a set of strains with similar taxonomic composition suggests that
their r/m is well above that threshold. Thus, our observations of habitat separation
for highly similar but clearly distinct genotypes suggest that ecological selection may
have triggered phylogenetic differentiation. A plausible mechanism is that differential distribution among habitats (possibly caused by few adaptive loci) is sufficient
to depress gene flow between associated genotypes [6, 59]. Consequently, mutations
will no longer be homogenized but instead accumulate within specialized populations,
even for ecologically neutral genes. Over time, genetic isolation may increase because
homologous recombination rates decrease log-linearly with sequence distance [60]. We
detected associations with different habitats among sister clades over a wide range
of phylogenetic distances, possibly representing populations at various stages of differentiation (Fig. 1.1A). Although we cannot determine whether clusters represent
transiently adapted populations or nascent species, our observations of differential
distributions of genotypes suggest that there exists a small-scale adaptive landscape
in the water column allowing the initiation of (sympatric) speciation within this community.
Although it has recently been suggested that microbial lineages remain specific
to macroenvironments over long evolutionary times [61], this study demonstrates
switches in ecological associations within a bacterial family coexisting in the coastal
ocean. In the V. splendidus clade, speciation could be ongoing, but the divergence
between most other ecologically defined groups appears large. This is consistent with
our previous suggestion that rRNA gene clusters, which are roughly congruent with
the deeply divergent protein-coding gene clusters detected here, represent ecological
populations [42]. However, the example of V. splendidus highlights the fact that using
marker genes to assess community-wide diversity may not capture some ecological
specialization. Moreover, different groups of organisms could evolve under different
constraints, and the mechanisms suggested here apply to the invasion of new habitats
23
and are thus different from (but compatible with) the widely discussed niche-specific
selective sweeps [10]. Why V. splendidus appears to have radiated recently into new
habitats whereas other groups appear to be more constant is not known but may
be related to its high heterogeneity in genome architecture [54]. This could indicate
a large (flexible) gene pool that, if shared by horizontal gene transfer, gives rise to
large numbers of ecologically adaptive phenotypes. It will therefore be important
to compare whole genomes within recently ecologically diverged clusters to identify
specific changes leading to adaptive evolution.
24
1
Department of Civil and Environmental Engineering,
Massachusetts Institute of Technology (MIT), Cambridge, MA
02139, USA. 2Computational and Systems Biology Initiative,
MIT, Cambridge, MA 02139, USA. 3Laboratory of Microbiology, Ghent University, Gent 9000, Belgium. 4Bioinformatics
and Evolutionary Genomics Group, Ghent University, Gent
9000, Belgium.5Department of Biological Engineering, MIT,
Cambridge, MA 02139, USA, and Broad Instilate of MIT and
Harvard University, Cambridge, MA 02139, USA.
1.1
Figures
*These authors contributed equally to this work.
†To whom correspondence should be addressed. E-mail:
ejalm@mit.edu (E.J.A.); mpolz@mit.edu (M.F.P.)
population-level effects of microhabitat preferences, because tidal mixing and oceanic circulation ensure a high probability of migration,
reducing biogeographic effects on population
structure. In the plankton, heterotrophs may
adopt alternate ecological strategies: exploiting
either the generally lower concentration but more
evenly distributed dissolved nutrients or attaching to and degrading small suspended organic
particles, originating from algal exopolysaccharides and detritus (3). Bacterial microhabitat
preferences may develop because resources are
distributed on the same scale as the dispersal
provide additional, more stable microhabitats;
vibrios attach to and metabolize chitinous zooplankton exoskeletons (18, 19) but may also live
in the gut or occupy niches specific to pathogens.
The extent to which microenvironmental preferences contribute to resource partitioning in this
complex ecological landscape remains an important question in microbial ecology (20).
We aimed to conservatively identify ecologically coherent groups by examining distribution
patterns of Vibrionaceae genotypes among freeliving and associated (with suspended particles
and zooplankton) compartments of the plankton-
Fig. 1. Season and size fraction distributions and habitat predictions size fractions. The habitat legend matches the colored circles in (A) and
mapped onto Vibrionaceae isolate phylogeny inferred by maximum (B) with the habitat distribution over seasons and size fractions inferred
likelihood analysis of partial hsp60 gene sequences. Projected habitats by the model. Distributions are normalized by the total number of counts
are Figure
identified by1.1:
colored Season
circles at the
parent
nodes.
(A) Phylogenetic
in each environmental
categorypredictions
to reduce the effects
of uneven sampling.
and
size
fraction
distributions
and habitat
mapped
onto
tree of all strains, with outer and inner rings indicating seasons and size The insets at the lower right of (A) show two nested clusters (I.A and I.B
Vibrionaceae
isolate
phylogeny
inferred
by
maximum
likelihood
analysis
of
partial
hsp60
fractions of strain origin, respectively. Ecological populations predicted and II.A and II.B) for which recent ecological differentiation is inferred,
by gene
the model
are indicated by
alternating blue
and gray shading
of includingby
habitat
predictions
at each at
node.the
The closest
named
species to
sequences.
Projected
habitats
are identified
colored
circles
parent
nodes.
clusters if they pass an empirical confidence threshold of 99.99% (see numbered groups are as follows: G1, V. calviensis; G2, Enterovibrio
Phylogenetic
tree levels
of all
strains,
with
andG3,inner
rings
indicating
and
SOM(A)
for details).
Bootstrap confidence
are shown
in fig. S10.
(B) outer
norvegicus;
V. ordalii;
G4, V. rumoiensis;
G5, V.seasons
alginolyticus;
G6, V.
Ultrametric
tree summarizing
habitat-associated
identifiedEcological
aestuarianus; populations
G7, V. fischeri/logei;predicted
G8, V. fischeri;by
G9, the
V. superstes;
G10,
size fractions
of strain
origin, populations
respectively.
model
by the model and the distribution of each population among seasons and V. penaeicida; G11 to G25, V. splendidus.
1082
are indicated by alternating blue and gray shading of clusters if they pass an empirical
confidence threshold of 99.99% (see SOM for details). Bootstrap confidence levels are shown
23 MAY 2008 VOL 320 SCIENCE www.sciencemag.org
in Fig. 1.14. (B) Ultrametric
tree summarizing habitat-associated populations identified
by the model and the distribution of each population among seasons and size fractions. The
habitat legend matches the colored circles in (A) and (B) with the habitat distribution over
seasons and size fractions inferred by the model. Distributions are normalized by the total
number of counts in each environmental category to reduce the effects of uneven sampling.
The insets at the lower right of (A) show two nested clusters (I.A and I.B and II.A and II.B)
for which recent ecological differentiation is inferred, including habitat predictions at each
node. The closest named species to numbered groups are as follows: G1, V. calviensis; G2,
Enterovibrio norvegicus; G3, V. ordalii ; G4, V. rumoiensis; G5, V. alginolyticus; G6, V.
aestuarianus; G7, V. fischeri/logei ; G8, V. fischeri ; G9, V. superstes; G10, V. penaeicida;
G11 to G25, V. splendidus.
25
Downloaded from www.sciencemag.org on May 26, 2008
between ecologically distinct populations (13).
Thus, exploring the relationship between phyloge-
acterized by partial sequencing of a proteincoding gene (hsp60). To obtain added resolution,
between one and three additional gene fragments
differential patterns of cluste
and size fractions (Fig. 1A).
toward uncertainty in the
should diminish but not stre
and is confirmed by introduc
tainty in the phylogeny (fig
overall association with seas
therefore suggests that water
tition resources, but neither p
the phylogenetic bounds of
composition of their habitats
We therefore developed an
(AdaptML) to identify popu
related strains sharing a com
itat, which reflects their relat
measured environmental ca
tions and seasons) (SOM). In
inputs are the phylogeny, se
tion of the strains. It then ma
ronmental preference onto th
projected habitats for each
strain in the phylogeny. Alth
to existing parsimony, likeli
methods, which map ancest
(23), the model accounts for
uncertainties of environmen
projected habitats can span
dimensions to account for
Fig. 2. Multilocus sequence analysis of nested (such as time spent in multip
clusters (IA and IB and IIA and IIB) with differential problems inherent in envir
Figure 1.2: Multilocus sequence analysis of nested clusters (IA and IB and IIA and IIB)
Discrete samples rarely equ
habitatassociation
association
comparison
of partial
with differential habitat
by by
comparison
of partial
hsp60hsp60
(left) and concatenated
andbytrue
habitats are frequent
concatenated
partialHabitat
mdh, predictions
adk, and pgi
partial mdh, adk, (left)
and pgiand
(right)
gene phylogenies.
(indicated
colored
their
typical
boxes) and the numbering
of clusters
correspondHabitat
to Fig. predictions
1.1. Scale bar is
in units
of sample catego
(right) gene
phylogenies.
nucleotide substitutions
per site.
(indicated
by colored boxes) and the numbering zooplankton fragments may
of clusters correspond to Fig. 1. Scale bar is in units smaller size fractions). Secon
of nucleotide substitutions per site.
can span multiple phylogene
www.sciencemag.org
26
SCIENCE
1.2
1.2.1
Supplementary Material
Sampling rationale
To investigate partitioning of Vibrionaceae strains in the water column, we examined their distribution among the free-living and associated (with particles and zooplankton) fractions of the bacterioplankton community at two time points. This was
achieved by sequential filtration with decreasing pore size cutoffs and subsequent cultivation on Vibrio selective media (Fig. 1.5B). Here, we give additional details on
sampling protocols and rationale supplementing the overview given in the main text.
Filtration is commonly used in oceanography to separate particle-associated and
free-living populations by retention of particles on filters, although the filter size cut off
for collecting particle-attached bacteria has varied in past studies between 0.8 and 10
µm [62–65]. To obtain higher ecological resolution, we used sequential filtration since
alternate types of particulate organic matter and organisms (e.g., phytoplankton,
zooplankton) will have distribution maxima in different size fractions thus enabling
differentiation of associated bacterial genotypes.
We collected a total of four size fractions with different expected composition
of particles and organisms (Fig. 1.5B). The largest fraction (≥63 m) was visually
enriched in zooplankton and detrital material (e.g., pieces of macroalgae, terrestrial
plant material); however, large gelatinous material [frequently part of marine snow,
which represents particles >0.5 mm [66]] was likely not collected since it is disrupted
by the pressure on the plankton nets used for collection. All other fractions were
collected by gravity rather than vacuum filtration to minimize disruption of fragile particles. The large particle fraction (63-5 µm) likely contains zooplankton fecal
pellets, dead and living algae, and other detritus. The composition of the 5-1 µm
size fraction is somewhat ambiguous since it may contain both cells attached to very
small particles as well as large or dividing cells; however, it provides a firm buffer
between obviously particle-attached (>5 µm) and free-living (<1µm) cells. Particulate material in this size range may include small algae, bacterial cell walls, as well as
fragments of larger particles, which have broken apart; nonetheless, the small size of
27
such particles in unlikely to sustain a resident bacterial population. Free-living bacteria, observed in the 1-0.22 µm size fraction, likely live on dissolved organic matter
produced by living algae, cell lysis and the dissolution of particles.
1.2.2
Sample collection
Coastal ocean water samples were collected at high tide on the marine end of the
Plum Island Estuary (NE Massachusetts) (Fig. 1.5A) on two days representing spring
(4/28/06) and fall (9/6/06) conditions in the coastal ocean. Nutrient concentrations,
water temperature and chlorophyll levels were measured on both sampling dates (Fig.
1.3).
Two replicate samples of the largest size fraction (enriched in zooplankton) were
collected by filtering ∼100 L each through a 63 µm plankton net, which was subsequently washed with sterile seawater (Fig. 1.5B). Particle-associated and freeliving bacterial populations were collected from quadruplicate water samples, which
were independently 2 pre-filtered through the 63 µm plankton net (to remove the
zooplankton-enriched fraction) into 4 L nalgene bottles (Fig. 1.5B). For each bottle,
water was sequentially filtered through 5, 1 and 0.22 µm pore size filters, collecting
at least four replicate filters per size fraction. To avoid disruption of fragile particles, the 63-5 and 5-1 µm fractions were collected on polycarbonate membrane filters
(Sterlitech) using gravity filtration followed by washing with 5 ml of sterile (0.22
µm-filtered and Tindalized) seawater to remove free-living bacteria that might have
been retained on the filter. The <1 µm fraction containing free-living bacteria was
collected on 0.22 µm Supor-200 filters (Pall) by applying gentle vacuum pressure.
After size fractionation, particles and zooplankton were broken up before plating
(Fig. 1.5B). The zooplankton sample was homogenized using a tissue grinder (VWR
Scientific) and vortexed for 20 minutes at low speed before concentration on 0.22 µm
Supor-200 filters (Pall). These filters were then plated directly on selective media.
Similarly, 5 µm and 1 µm filters were placed in 50 ml conical tubes with 50 ml sterile
seawater and vortexed at low speed for 20 min to break up particles and detach
bacteria from the filters. The supernatant was concentrated on 0.22 µm filters, and
28
both the original and supernatant filters were placed directly on media to collect
isolates.
1.2.3
Strain isolation and identification
Isolates were obtained from TCBS plates (Accumedia or Difo) with 2% NaCl since
this media has been shown to yield good correspondence in phylogenetic groups of
vibrios detected by quantitative PCR and isolation [54]. After 2-3 days of growth,
colonies were counted and re-streaked a total of three times alternately on Tryptic
Soy Broth (TSB) (Difco) with 2% NaCl and media.
For classification of strains by sequencing, purified isolates were grown in marine
TSB broth overnight; DNA was extracted using either a tissue DNA kit (Qiagen) or
Lyse- N-Go (Pierce). Following the rationale of multilocus sequence analysis (MLSA),
housekeeping genes were used for further strain characterization since these are unlikely to be under environmental selection. The partial hsp60 gene sequence was
amplified for all isolates as described previously [67]. For isolates with an hsp60
sequence differing by more than 2% from an already characterized strain, the 16S
rRNA gene was PCR amplified using primers 27F- 1492R and sequenced using the
27F primer [68]. The 16S sequence was used to identify the organism using the
RDP classifier [69] and BLAST [70]. In cases where the hsp60 gene either failed
to amplify or the sequence diverged greatly from other vibrios, 16S rRNA gene sequencing confirmed that these isolates largely belonged to the genera Pseudomonas,
Shewanella, Pseudoalteromonas, and Agaravorans (RDP Classifier) [69]. We excluded
non-Vibrionaceae strains from further analysis.
To confirm relationships for V. splendidus, the most highly represented group
among isolates, an additional gene (mdh) was sequenced. The partial mdh gene was
amplified using primers mdh mod.for (5’- GAY CTD AGY CAY ATC CCW AC -3’)
and mdh mod.rev (5’- GCT TCW ACM ACY TCD GTR CCY G -3’) (S. Preheim,
unpublished data). Two additional housekeeping gene sequences were obtained (pgi,
adk ) for select groups of strains, using pgi.for (5 GAC CTW GGY CCW TAC ATG
GT 3 - 3) and pgi.rev (5-CMG CRC CRT GGA AGT TGT TRT-3) (S. Preheim,
29
unpublished data) and adk.for (5- GTA TTC CAC AAA TYT CTA CTG G-3) and
adk.rev (5- GCT TCT TTA CCG TAG TA- 3) [71]. All of these genes were amplified
using the following PCR conditions: 2 min at 94◦ C followed by 32 cycles of 1 min each
at 94◦ C, 46◦ and 72◦ C with a final step of 6 min at 72◦ C. Most genes were sequenced
at least twice using forward and reverse primers. All sequencing was performed at
the Bay Paul Center at the Marine Biological Laboratory, Woods Hole MA.
1.2.4
Phylogenetic tree construction and representation
The partial hsp60, mdh, adk, and pgi gene sequences yielded unambiguous alignments of 541, 422, 372, 395 nucleotides, respectively. Phylogenetic relationships were
reconstructed using PhyML v.2.4.4 [72] with following parameter settings: DNA substitution was modeled using the HKY parameter [73]; the transition/transversion
ratio was set to 4.0; PhyML estimated the proportion of invariable nucleotide sites;
the gamma distribution parameter was set to 1.0; 4 gamma rate categories were used;
a BIONJ tree was initially used; and, both tree topology and branch lengths were
optimized by PhyML. Circular tree figures were drawn using the online iTOL software
package [74]. To prevent numerical instabilities in AdaptMLs maximum likelihood
computations, branches with zero length were assigned the minimal observed non-zero
branch length: 0.001.
1.2.5
Empirical statistical testing
We employed empirical statistics to quantify evidence for differential environmental
distribution of phylogenetic groups (Fig. 1.6). We first tested the overall association
of phylogeny with our environmental data using a non-parametric parsimony-based
metric. We assigned a different character to each of the environmental categories,
and calculated the minimum number of character transitions needed to explain the
data given the observed hsp60 phylogeny. Although this test is likely to be overly
conservative given the heterogeneous nature of our observed clusters, it nonetheless
supported a highly significant correlation between phylogeny and both size fraction (p
30
< 10−49 ) and season (p < 10−79 ). Exact p-values were computed based on the algorithm of [55]. It was not possible to compute p-values for both season and size fraction
together because the computational complexity of the algorithm grows exponentially
with the number of character states.
We also employed non-parametric empirical statistics to test specific model predictions. We tested the hypothesis that each of the clusters identified by the model
would be likely to arise by chance. To do this, we produced a 2 × 8 contingency table
to test for any associations between cluster membership and distribution across environments. We used the Fisher exact test [75] as implemented in the R programming
language to evaluate the significance of each association. The results are shown in
Figure 1.4.
1.2.6
Overview of AdaptML
We developed a maximum likelihood method to help identify the boundaries of ecologically distinct populations and infer the ancestral habitat association of internal
nodes in the strain phylogeny. The key to our method is a hidden variable mapping a
’projected habitat’ to each node. We mathematically characterize each habitat as a
discrete probability distribution, which describes the likelihood that a strain adapted
to that habitat will be observed in each of our eight environmental categories. These
distributions, which we refer to as emission probabilities in accordance with terminology used in machine learning, are not known a priori and must be learned from the
data. Because of this probabilistic definition of habitats, a phylogenetic group spanning several environmental categories can still be considered an ecologically distinct
population.
Using the habitat variables and isolate sequence-based phylogeny, we built a
hidden-Markov model (HMM) [75] describing the evolution of habitat association
(Figure 1.7). The probability that adjacent nodes in the phylogeny share the same
habitat is a function of both the branch length separating them and a parameter that
represents the rate at which a lineage can transition between habitats (the transition
rate). The observed variables the environmental category from which each strain
31
was sampled occur only at the leaves of the phylogeny. The parameters necessary
for our model can be learned from the data according to the following algorithm:
1. Initialize parameters: We initialize 16 habitats, each with random emission
probability distributions over the 8 environmental categories. The transition
rate parameter is initialized to 10−1 transitions per substitution/site (relative
to the gene phylogeny branch lengths).
2. Infer the observed datas likelihood, given phylogeny and parameter
estimates: We use a dynamic programming algorithm and the model parameters (transition rate and emission probabilities) to compute the likelihood of
the observed data (environmental category for each isolate). Our computation
proceeds in a manner identical to Felsenstein’s “pruning” method of computing
likelihoods on a phylogenetic tree [76].
3. Optimize parameters to maximize likelihood of observed data: We estimate the probability that each internal node is associated with a given habitat
by summing over all possible habitat assignments at other nodes (E-step). These
probabilities are used (M-step) to update the:
(a) Transition rate parameter : We numerically optimize the transition rate to
maximize the likelihood of the observed data.
(b) Emission probability matrix : We update the emission probability matrix
by taking the matrix that maximizes the likelihood of the observed data
given the marginal likelihoods for the habitat assignments at each of the
phylogenys leaves.
We note that separating these two steps represents an approximation, as these
two parameters are not strictly independent. The approximation, however,
speeds up the implementation considerably as only one parameter (instead of 1
+ 16 × 7 = 113) is optimized numerically.
4. Test for convergence: If the model parameters do not change significantly
from the previous iteration, then the emission probabilities and the transition
32
rate are considered to have converged: continue to step (5). (A typical trajectory of emission probability convergence is shown in Figure 1.8. Note: the
approximation identified in step 3 can lead to fluctuation near a likelihood maximum rather than actual convergence). Otherwise, return to step (2).
5. Test for model complexity/redundancy. If a pair of habitats has emission
probability distributions that exhibit correlations greater than 0.90, they are
merged into a single habitat and the algorithm continues from step (2). If
no habitats are merged, the parameter estimation loop terminates. Although
our approach employed manual inspection and empirical testing rather than
a likelihood-based criterion for reducing model complexity [such as the AIC
[77]], our algorithm can be easily extended to include a likelihood criterion.
To test for overfitting, we performed simulations as described in the main text
and Figure 1.10. We found that our scheme acts conservatively since it usually
underestimated the true number of habitats. Figure 1.13 shows how the inferred
habitats identified by the model vary with different cutoffs.
Once a set of model parameters has been learned, we utilize the following protocol
to identify ecologically distinct and statistically significant populations.
1. Infer node habitat assignments that maximize the joint probability
of the observed data: We rely upon a joint likelihood calculation to infer a
single habitat assignment per ancestral node. To compute this likelihood, we
use the parameter estimates inferred by the algorithm described above, which
sums over all habitat assignments. Phylogenetic groups that share a common
habitat association are taken as candidate ecological populations if they pass
an empirical significance test.
2. Empirical testing to identify ecologically distinct populations: The
assignment of nodes to habitats in step (1) identifies the most likely set of
population boundaries, but may include some weakly predicted clusters. To
filter low confidence ecological groupings, we estimate empirical p-values for
33
each clade and only report statistically significant (p < 0.0001) populations
(Figure 1A & 1B). Empirical p-values are computed by comparing the likelihood
of the parent node for a cluster to the likelihood observed at the same node in
randomized trials where environmental assignments are shuffled, but phylogeny
is maintained. For comparison, all possible clusters (with no significance cutoff)
can be inferred from the full model results shown in Figure 1.11.
1.2.7
Detailed description of maximum likelihood model
Conditional likelihoods
The conditional likelihood describes the likelihood L that the leaves of a subtree
exhibit their observed states, conditional on the subtree’s root node k taking state s.
This likelihood can be defined recursively, assuming two child nodes l and m
Lk (s) =
�
�
�
P (x|s, tl )Lkl (x)
x
×
�
�
�
P (y|s, tm )Lkm (y)
y
(1.1)
where the function P (x|s, t) represents the probability of transitioning between
states x and s along some interval t. To reduce the number of fitted parameters
early in our model, we use the simplifying assumption that all state transitions can
be described using the same transition rate parameter µ. Thus, we compute the
probability of transitions between states as
P (x|y, t) =
�
1�
1 − e−hµt
h
(1.2)
and the probability of remaining in the same state
P (x|x, t) =
�
1�
1 + (h − 1) × e−hµt
h
(1.3)
which is analogous to the Jukes-Cantor model for nucleotide sequence evolution
[78]. Note that if k is a leaf node in state s with observed environment o, the likelihood
is drawn from the emission probability matrix Pe :
34
Lk (s) = Pe (o|s)
(1.4)
Marginal and joint likelihoods
To compute the marginal likelihood that a node k has state s, we combine the conditional likelihoods:
�
�
� �
1
M Lk (s) =
×
P (x|s, tkl )Ll (x)
K
t
x
(1.5)
where the l are drawn from the set of nodes adjacent to k, and K is a normalization
factor such that
�
M Lk (s) = 1
(1.6)
s
To compute the likelihood of the observed data for the single best assignment of
habitats to nodes (17), the summation terms in the likelihood formula are replaced
with max operations. This is equivalent to maximizing the “joint” likelihood:
�
� �
Lk (s) = max P (x|s, tkl )Ll (x) × max P (y|s, tkm )Lm (y)
�
x
y
(1.7)
The backtracking process used to keep track of the max arguments is analogous
to the Viterbi algorithm for finding the most probable state path in an HMM (16).
Parameter estimation
As probability distributions, each set of emission probabilities must satisfy
�
Pe (o|s) = 1
(1.8)
o
Because leaf nodes are independent samples of their emission probability distributions (given their state assignment), we use a weighted-average approach to calculating the emission probability matrix Pe
35
Pe (o|s) =
�
1
×
M Lk (s) × ∆(o, ok )
K
k
(1.9)
where M Lk (s) is the marginal likelihood that node k is adapted to habitat s, the
function ∆(x, y) equals 1 if and only if x equals y (and is 0 otherwise), and K is a
normalization factor.
We learn the transition rate parameter µ by numerical optimization to maximize
the likelihood of the observed data (summed over all values for the hidden variables).
1.2.8
Defining ecologically coherent and significant clusters
We use empirical testing to estimate the statistical significance associated with the
likelihood value computed for each node in the maximum (joint) likelihood assignment
of habitats to nodes (given the final parameter estimates). Each trial preserved
the phylogeny, the inferred habitat assignments, and the habitat and transition rate
parameters, but environmental categories at the leaves were shuffled. The maximum
’joint’ likelihoods at each node were compiled over all trials and used as empirical
background probability distributions.
We use empirical testing to estimate the statistical significance associated with
the likelihood value computed for each node in the maximum (joint) likelihood assignment of habitats to nodes (given the final parameter estimates). Each trial preserved
the phylogeny, the inferred habitat assignments, the habitat and transition rate parameters, and the frequency of the various environmental categories; however, each
leaf was randomly assigned an environmental category in each trial. The maximum
joint likelihoods at each node were compiled over all trials and used as empirical
background probability distributions.
Using the habitat associations learned from the original (non-randomized) data
set, we identified internal nodes where habitat transitions were inferred to take place.
These nodes were then iterated through using a post-fix traversal:
36
Nested clusters
At each of these transitional nodes, a second, pre-fix traversal took place. If the subtree rooted by the current node possessed a likelihood greater than that observed in
99.99% of the random trials and 90% of its leaves shared the current nodes habitat
assignment, we recognized the subtree as an ecologically coherent and statistically
significant cluster. This latter cutoff (90% coherence) was necessary to ensure meaningful clusters because the likelihood at a parent node can be unusually high when two
nearly significant (but non-identical) child groups are combined. Requiring a majority of child nodes to have the same predicted model state as the parent has the effect
of identifying clusters that correspond more directly to single putative populations.
Making the threshold too high (e.g., 100%) would eliminate some larger clusters that
have a small, internal nested cluster.
To enable the discovery of nested clusters, identified clusters were pruned from
the phylogeny. Nodes representing clades that contained the cluster had their jointlikelihoods modified so that they no longer incorporated information from the pruned
groups. If the clade rooted by the current node did not satisfy both the p-value and
90% coherence thresholds, the second recursion would descend to the current nodes
children. The second recursion only terminated if the current node was either a leaf,
itself a habitat transition node, or determined to root an ecologically coherent and
statistically significant cluster.
37
IV. SUPPLEMENTAL TABLE AND FIGURES
This section contains Supplementary Table S1 & S2 and Supplementary Figures S1 to
S10. See main text for details.
Supplementary Figures
Table S1. Temperature and nutrient concentrations on sampling dates.
Temperature Chlorophyll a1
Spring
(4/28/06)
Fall
(9/6/06)
DOC2
TDN2
NO3-+NO2-
NH4+
TDP2
PO43-
[˚C]
[µg/L]
[mg C/L]
[mg N/L]
[µg N/L]
[µg N/L]
[µg P/L]
[µg P/L]
11
4.07
2.11
0.17
9
189
18
14
16
6.03
2.28
0.27
5
144
24
25
1
measured using overnight extraction in 90% acetone (19)
DOC = dissolved organic carbon, TDN = Total Dissolved Nitrogen, TDP = total dissolved phosphorous.
All chemical analyses were performed at the University of New Hampshire, Durham, NH.
2
Figure 1.3: Temperature and nutrient concentrations on sampling dates.
11
38
Table S2. Empirical significance testing of ecologically differentiated clusters predicted
by the model. Fisher’s exact test was used to identify significant associations between
clusters and environments as described in the SOM. Group numbers correspond to Figure
1 in the main text.
Group
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
P-value
1.16 E-07
1.04 E-16
5.06 E-15
1.70 E-01
1.11 E-02
1.26 E-04
7.54 E-10
2.46 E-05
2.35 E-07
7.19 E-07
1.26 E-13
2.62 E-03
5.13 E-10
1.16 E-06
1.01 E-02
1.13 E-08
4.03 E-07
2.81 E-03
1.08 E-08
1.80 E-06
2.31 E-13
7.02 E-03
3.32 E-06
6.06 E-14
2.76 E-03
Figure 1.4: Empirical significance testing of ecologically differentiated clusters predicted
by the model. Fishers exact test was used to identify significant associations between
clusters and environments as described in the SOM. Group numbers correspond to Figure
1.1 in the main text.
39
12
A
! B
Map courtesy of the Gulf of Maine Council
on the Marine Environment
2x
63µm plankton net
4x
4x
tissue grinder
[
4x
[
4x
[
vortex
filter plated on
selective media
]
5 µm filter
]
1 µm filter
63 µm prefiltered
seawater
]
0.22 µm filter
Figure 1.5: Sampling site and outline of sampling strategy for determination of bacterial
distribution among seasons and size fractions in coastal water. (A) Sampling location
shown on a map of North America (left) with a white box depicting the bounds of the
picture at right: the Gulf of Maine. The arrow points to the sampling location, Plum Island
Sound, MA. (B) Protocol for obtaining size fractionated of bacterial seawater isolates using
sequential filtration and plating on selective media.
40
0
500
!20
400
!40
log(P!value)
Parsimony Score
600
300
0
0.1
0.2
0.3
0.4
0.5
0.6
Sequence Fraction
0.7
0.8
0.9
1
!60
Fig. S2. Empirical statistical testing for ecological association of phylogenetic clades.
likelihood
that the
observedtesting
parsimony
(or lower)
for size fraction
data might clades.
FigureThe
1.6:
Empirical
statistical
for score
ecological
association
of phylogenetic
have
arisen
by
chance
was
calculated
[using
the
method
of
(15)]
for
a
series
of
trees,might have
The likelihood that the observed parsimony score (or lower) for size fraction data
inferred
using
of strains
from
themethod
hsp60 gene
alignment.
To testofthe
effectinferred
of
arisen by
chance
wassubsets
calculated
[using
the
of [55]]
for a series
trees,
using
statistical
uncertainty
on
the
inferred
association,
1%,
10%,
50%,
90%,
or
100%
of
the
subsets of strains from the hsp60 gene alignment. To test the effect of statistical uncertainty
sequenceassociation,
data was randomly
selected
and90%,
used or
to 100%
construct
the phylogeny
(with was
3 randomly
on the inferred
1%, 10%,
50%,
of the
sequence data
replicates
each).
The
parsimony
score
for
each
such
tree
is
shown
with
green
dots
selected and used to construct the phylogeny (with 3 replicates each). The parsimony score
the left axis; the p-value of obtaining that parsimony score by chance is
for eachcorresponding
such tree istoshown
with green dots corresponding to the left axis; the p-value of
shown with blue dots corresponding to the right axis. These results are based on size
obtaining that parsimony score by chance is shown with blue dots corresponding to the right
fraction data only, but similar results are obtained with season data.
axis. These results are based on size fraction data only, but similar results are obtained
with season data.
41
14
A
B
H1
H1
H2
H3
H2
H3
0.0
1.0
C
Figure 1.7: Overview of hidden Markov model components. (A) Habitats represent hidden
variables
in the model;
do not directly
measure
what hidden
habitat a
Fig.(latent)
S3. Overview
of hidden
Markovexperiments
model components.
(A) Habitats
represent
strain
is
adapted
to.
Two
adjacent
nodes
on
the
phylogeny
may
differ
in
their
habitat
(latent) variables in the model; experiments do not directly measure what habitat a strain
assignment
according
to the nodes
rate ofonhabitat
transition
(arrows
habitats). In our
is adapted to.
Two adjacent
the phylogeny
may
differ between
in their habitat
simple
model,
all
transition
rates
are
equivalent.
(B)
Associated
with
each
habitat
assignment according to the rate of habitat transition (arrows between habitats).
In ouris an
emission probability distribution describing how likely a strain associated with a particular
simple model, all transition rates are equivalent. (B) Associated with each habitat is an
habitat (H1 -H3 ) is sampled from a given environment (blue or black). Bars in this caremission probability distribution describing how likely a strain associated with a
toon depict hypothetical probability distributions; strains adapted to habitat 1 have higher
particular habitat
(Hobserved
given environment
or black).
Bars in this
1-3) is sampled
probability
of being
in thefrom
blacka environment
than in(blue
the blue
environment.
(C)
cartoon
depict
hypothetical
probability
distributions;
strains
adapted
to
Habitat
1 have the
Our model maps a habitat onto each node in the phylogeny such that it maximizes
higher probability
of being observed
in the
black Observations
environment than
in the blue
probability
of the observed
data (at the
leaves).
are limited
to the leaves of
environment.
Ourcannot
model directly
maps a habitat
each node
in the
phylogeny
that the
it
the
phylogeny,(C)
as we
sample onto
ancestral
strains.
In the
examplesuch
shown,
mostly
blue
group
is
mapped
to
habitat
3,
the
mostly
black
group
to
habitat
1,
and
the
maximizes the probability of the observed data (at the leaves). Observations are limited
heterogeneous
is mappedas
towe
habitat
2. directly sample ancestral strains. In the
to the leaves ofgroup
the phylogeny,
cannot
example shown, the mostly blue group is mapped to habitat 3, the mostly black group to
habitat 1, and the heterogeneous group is mapped to habitat 2.
42
1
likelihood
emission prob
transition rate
!2820
!2840
0
5
10
15
20
25
iteration
30
35
40
45
0.5
parameter change
likelihood
!2800
0
50
Fig. S4. Convergence of iterative parameter optimization. During the parameter
Figure 1.8: Convergence of iterative parameter optimization. During the parameter optioptimization,
both thechange
average
in probability
for components
of the emission
mization,
both the average
in change
probability
for components
of the emission
probability
matrix
line)inand
the change
transition
(blackrapidly.
line) decrease
matrix probability
(red line) and
the (red
change
transition
ratein(black
line)rate
decrease
The overall
rapidly.ofThe
log-likelihood
of theinobserved
data converges
in concert
with the
log-likelihood
theoverall
observed
data converges
concert with
the parameter
estimates
(blue
line). parameter estimates (blue line).
43
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
HA
HB
HC
HD
HE
HF
Fig. S5. Reproducibility of inferred habitats. Sixty independent trials of the iterative
habitat learning process were performed. Shown are the frequencies of occurrence of the
Figure 1.9:
Reproducibility of inferred habitats. Sixty independent trials of the iterative
habitats presented in Figure 1. The habitat similarity cut-off for counting a match was an
habitat learning
processprobability
were performed.
the frequencies
of occurrence
of the
average emission
difference < Shown
0.10 overare
all environmental
categories.
As
habitats expected,
presented
in
Figure
1.1.
The
habitat
similarity
cut-off
for
counting
a
match
was
the least abundant habitat in our data, HD (see Fig. S4) is the least reproducible,
an average
emission
probability
difference
< 0.10
over all environmental categories. As
although
even this
habitat is identified
in 83%
of trials.
expected, the least abundant habitat in our data, HD (see Fig. 1.8) is the least reproducible,
although even this habitat is identified in 83% of trials.
17
44
y=x
y = mx
simulation run
6
inferred habitats
5
4
3
2
1
0
0
1
2
3
actual habitats
4
5
6
Figure
1.10: of
Number
of habitats
from simulated
We generated
simFig. S6.
Number
habitats
inferredinferred
from simulated
data.data.
We generated
120120
simulated
ulated datasets and compared the number of inferred habitats to the true number. Each
datasets
andwas
compared
number
of inferred
the habitats
true number.
Each dataset
dataset
randomlythe
assigned
between
2 and 6habitats
habitats;tothese
were distributed
was randomly
assignedcategories
betweenby2 randomly
and 6 habitats;
thesethe
habitats
over environmental
partitioning
intervalwere
(0,1) distributed
according toover
a
uniform
distribution,
but
requiring
that
each
successive
partitioning
must
occur
at
a
larger
environmental categories by randomly partitioning the interval (0,1) according to a
value than the last (random partitioning without this restriction leads to a large number of
uniform
distribution, but requiring that each successive partitioning must occur at a larger
similar generalist habitats). These habitats were mapped onto the set of clusters learned
valuefrom
thanthe
the
last (random
without
restriction
leadsinferred
to a large
number of
Vibrionaceae
datapartitioning
(Figure 1A). Shown
arethis
the number
of habitats
for these
trials
versus the habitats).
number actually
(a small
of Gaussian
added to
similar
‘generalist’
Thesepresent
habitats
wereamount
mapped
onto the noise
set ofwas
clusters
learned
each
data
point
so
that
the
data
points
could
be
discerned).
These
results
suggest
that
the
from the Vibrionaceae data (Fig. 1, main text). Shown are the number of habitats
habitat inference algorithm is generally conservative, inferring fewer rather than more than
inferred
for these
trials
versus the number actually present (a small amount of Gaussian
the true
number
of habitats.
noise was added to each data point so that the data points could be discerned). These
results suggest that the habitat inference algorithm is generally conservative, inferring
fewer rather than more than the true number of habitats.
45
Legend:
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habitat 0
habitat 15
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habitat 13
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5F
FS
213
181
FS
1S
275
193
1F
1S
33
120
1S
5S
89
239
5S
5S
139
149
5S
1S
97
77
ZF
5S
86
41
5S
FS
214
286
5S
5S
28
240
ZS
FS
127
285
ZS
ZF
213
65
5S
ZF
292
170
5S
5F
64
281
5S
ZS
119
63
ZS
5F
262
133
5S
ZF
183
57
ZS
ZF
35
14
ZS
FF
296
75
1S
ZF
5
270
ZS
ZF
153
30
5S
ZF
201
207
ZS
ZF
119
205
1S
5F
279
171
5S
5F
190
301
5S
207
ZF
ZS
264
105
ZF
5F
173
99
ZF
ZF
90
ZS
255
ZF
102
ZS
59
1F
101
5S
28
FF
101
124
5S
1F
67
ZS
289
1F
162
5S
274
1F
136
5S
53
46
ZS
273
1F
150
127
1S
1F
48
160
1S
FF
FF
145
111
FF
1F
24
97
FF
1F
1
1F
175
ZF
102
1F
61
ZF
169
5F
6
1S
261
1S
265
5F
202
1S
269
FF
166
185
ZS
FF
95
103
5S
ZF
51
ZS
113
5S
163
73
ZF
5S
7
ZS
184
5S
156
57
ZS
5F
23
5S
104
ZS
202
ZS
83
1F
121
FS
13
FF
156
ZS
253
5F
317
FF
102
FF
251
200
ZF
1F
2
1F
277
FF
157
FF
465
1F
138
1F
245
1F
183
FF
267
5F
309
FF
263
FF
139
93
FF
ZF
95
FF
61
283
FF
460
ZF
1F
144
1F
43
FF
302
FF
177
FF
341
70
1F
5F
55
FS
189
1F
319
19
FF
5F
6
FF
488
FF
198
FF
310
5F
215
FF
174
5F
207
FF
129
1S
206
FF
180
ZS
209
160
ZF
ZS
79
70
1F
ZS
57
1S
278
ZS
106
92
ZF
ZS
77
ZF
64
5S
175
5F
196
FF
286
151
5F
ZS
18
FF
221
FF
141
466
5S
FF
37
149
5S
FF
55
5F
22
161
47
ZS
FF
25
47
FF
5F
20
36
1S
FF
35
53
ZS
FF
55
5S
141
5F
FF
106
ZS
231
FF
246
5S
148
5F
181
135
1F
5F
38
5S
210
5F
237
ZF
79
5F
149
5F
85
FF
5F 296
31
FF
1F
54
223
5F
FF
69
1F
29
5F
172
264
FF
1F 101
FF
261
FF 140
FF 386
FF 350
1F 183
FF 280
FF 213
FF 442
5F 318
FF 107
1F 110
FF 475
FF 267
1F 155
FF 105
FF 385
5F 20
FF 272
1F 277
ZF 111
FF 299
1F 169
FF 112
1F 148
1F 285
FF 168
1F 293
1F 185
FF 260
5F 126
1F 25
FF 145
FF 377
FF 247
FF 445
5F 311
FF 161
FF 86
1F 297
FF 348
1F 153
5F 60
5F 1
FF 312
1F 300
5F 315
FF 290
1F 181
FF 59
FF 305
FF 484
FF 233
5F 283
FF 481
FF 292
FF 132
5S 7
ZF 41
1F 158
FF 13
5F 302
FF 459
ZS 111
ZF 20
1S 29
500
ZS
FF
97
225
1F 303
FF 375
FF 169
1F 42
FF 315
1F 199
5F 44
1F 60
5F 4
1F 249
1F 63
ZF 203
5S 217
ZF 261
ZS 214
ZS 184
ZS 50
ZS 186
ZS 67
ZS 44
ZS 49
ZS 137
ZS 17
1F 279
FF 237
5F 324
FF 382
FF 268
Size Fraction:
5F 275
1F 308
5F 320
1F 195
ZF 76
FF 496
FF 14
FF 250
FF 376
FF 249
FF 266
1F 299
ZF 193
5F 273
FF 265
1F 187
1F 292
FF 3
1F 269
1F 9
Habitat:
Season
HA
< 1 !m
spring
HB,HC
1-5 !m
fall
HD
HE,HF
> 63 !m
5-63 !m
Fig. S7. Inferred habitat associations for all ancestors of sequenced Vibrio strains. The
rings surrounding the tree represent the season (outer) and size fraction (inner) from
which strains were isolated. The maximum likelihood assignment of nodes to habitats is
shown for all nodes, regardless of the confidence of each prediction (only confident
Figure 1.11: Inferred habitat associations for all ancestors of sequenced Vibrio strains. The
assignments are shown in main text Fig. 1A). Colored circles on each branch indicate the
rings surrounding
the tree represent the season (outer) and size fraction (inner) from which
habitat assignment (HA-F, as in Fig. 1 main text) for the node immediately below that
strains were
isolated.
maximum
likelihood
assignment
of nodes
to habitats is shown
branch (see aboveThe
legend
for color scheme).
Branch
lengths are adjusted
to aid
for all nodes,
regardless
of
the
confidence
of
each
prediction
(only
confident
assignments
visualization and do not represent evolutionary distances.
are shown in Figure 1.1A). Colored circles on each branch indicate the habitat assignment
(HA -HF , as in Figure 1.1A) for the node immediately below that branch (see above legend
for color scheme). Branch lengths are adjusted to aid visualization and do not represent
19
evolutionary distances.
46
A
HB
HC
HD
HE
HF
mdh
hsp60
B
habitat identity
50th percentile of <dist>
95th percentile of <dist>
1
0.9
0.8
0.7
fraction
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.05
0.1
0.15
0.2
genetic distance
0.25
0.3
0.35
Fig. S8. Comparison of habitat inference on different gene phylogenies (hsp60 and mdh)
Comparison
habitat
inference of
onhabitats
differentlearned
gene phylogenies
(hsp60
forFigure
Vibrio 1.12:
splendidus
strains. of(A)
Juxtaposition
from the mdh
andand
mdh)datasets
for Vibrio
strains.
(A)only;
Juxtaposition
from
the mdh and
hsp60
forsplendidus
V. splendidus
strains
habitats of
arehabitats
labeledlearned
to allow
comparison
hsp60
datasets
for
V.
splendidus
strains
only;
habitats
are
labeled
to
allow
comparison
with
with habitats predicted for all Vibrionaceae in Fig. 1, main text. Emission probabilities
habitats predicted for all Vibrionaceae in Fig. 1.1. Emission probabilities are normalized
are normalized by the total number of isolates obtained in each environmental sample to
by the total number of isolates obtained in each environmental sample to reduce the effects
reduce
the effects
expected,
fewer habitats
aremdh
identified
fromwhich
the
of sampling
bias. of
Assampling
expected,bias.
fewerAs
habitats
are identified
from the
phylogeny,
mdh
which
has a lower
rate of nucleotide
and thus(B)
is less
wellhasphylogeny,
a lower rate
of nucleotide
divergence
and thus is divergence
less well-resolved.
Comparison
resolved.
(B)assignments
Comparison
habitat
assignments
to nodes.
Because
it is difficult
to map
of habitat
to of
nodes.
Because
it is difficult
to map
the internal
nodes between
topologically
distinct
trees,
the
habitat
assignment
for
the
last
common
ancestor
of
the internal nodes between topologically distinct trees, the habitat assignment for theeach
last
pair
of
V.
splendidus
strains
was
compared.
If
both
corresponded
to
the
same
habitat
(H
C,
common ancestor of each pair of V. splendidus strains was compared. If both
HE , or HF in both phylogenies), they were considered to be in agreement, otherwise they
corresponded to the “same” habitat (HC, HE, or HF in both phylogenies), they were
were considered to be in disagreement. The fraction of nodes in agreement is shown as a
considered
to be in agreement, otherwise they were considered to be in disagreement.
function of increasing genetic distance between the pairs of strains considered (in the hsp60
The
fraction ofThe
nodes
in and
agreement
shown distances
as a function
increasing
genetic
distance
phylogeny).
black
red linesisindicate
that of
include
50% and
95% of
strains
between
the
pairs
of
strains
considered
(in
the
hsp60
phylogeny).
The
black
and
red
lines
within
same cluster in main text Figure 1.1, respectively.
indicate distances that include 50% and 95% of strains within the same cluster in main
text Figure 1, respectively.
47
20
85%
90%
95%
99%
Fig. S9. Influence of the model complexity/redundancy parameter on inferred habitats.
Clusters
are merged
during
themodel
modelcomplexity/redundancy
fitting procedure when the
vectors describing
Figure
1.13:
Influence
of the
parameter
on inferredtheir
habidistribution
across
environments
are
more
than
90%
correlated.
As
this
cutoff
is
varied,
tats. Clusters are merged during the model fitting procedure when the vectors describing
slightly
differentacross
habitats
are observed.are
Atmore
85%,than
habitat
HCcorrelated.
is not recovered;
higheris vartheir
distribution
environments
90%
As thisatcutoff
values
additional
habitats
become
more redundant
the
90%
cutoff
allows
ied,
slightly
different
habitats
are observed.
At 85%,suggesting
habitat Hthat
is
not
recovered;
at
higher
C
values
additional
habitats
become moreprojected
redundant
suggesting that the 90% cutoff allows
conservative
recovery
of characteristic
habitats.
conservative recovery of characteristic projected habitats.
21
48
F FAL482
1 FAL223
5 FAL172
F FAL461
5 FAL183
F FAL286
5 FAL155
F FAL195
1 FAL193
1 FAL146
F FAL277
Z FAL157
1 FAL39
Z FAL155
5 FAL186
F FAL100
F SPR148
F SPR103
F SPR219
F FAL118
Z SPR109
1 SPR136
1 SPR45
5 SPR187
1 SPR209
1 SPR242
F FAL9
F FAL255
F SPR217
F SPR272
F FAL197
F SPR143
F FAL68
F SPR144
F FAL495
F FAL187
1 FAL128
F FAL93
1 FAL69
F FAL222
1 SPR257
F FAL344
F FAL284
1 FAL236
1 FAL259
F FAL99
1 SPR143
F SPR129
5 FAL177
1 SPR123
F FAL483
F SPR134
F FAL155
F FAL2
F FAL498
F FAL19
F FAL279
F FAL167
F FAL166
F FAL443
F SPR215
F FAL263
F SPR203
F FAL236
F FAL134
F FAL188
F FAL451
F FAL31
F FAL489
F FAL380
F SPR104
5 FAL136
F FAL130
F FAL206
F FAL288
F SPR206
F FAL343
1 FAL247
F SPR238
F FAL159
F FAL317
F SPR214
5 FAL45
F FAL291
F SPR259
F FAL349
1 FAL30
Z FAL141
1 SPR10
F FAL232
F FAL320
F FAL179
5 FAL25
F FAL217
1 FAL160
F FAL173
F FAL252
F FAL472
1 FAL104
F FAL342
F FAL32
F FAL84
1 FAL73
F FAL62
5 FAL132
1 FAL99
1 FAL115
F FAL230
1 FAL221
5 FAL65
5 FAL268
F FAL5
5 FAL31
1 FAL213
F FAL313
5 FAL316
5 FAL222
5 FAL266
1 FAL240
F FAL4
5 FAL256
1 FAL230
F FAL468
F FAL92
F FAL447
F FAL45
5 FAL224
1 FAL20
5 FAL174
F FAL65
F FAL262
F FAL492
5 FAL323
F FAL452
1 FAL261
F FAL85
5 FAL325
F FAL354
1 FAL92
5 FAL309
F FAL51
F FAL345
1 FAL81
5 FAL259
1 FAL125
5 FAL212
F FAL150
1 FAL4
1 FAL36
F FAL285
F FAL72
1 FAL287
F FAL257
1 FAL121
F FAL301
F FAL15
F FAL63
5 FAL148
F FAL226
5 FAL245
Z FAL26
F FAL480
1 FAL205
F FAL384
F FAL8
1 FAL180
F FAL464
F FAL351
F FAL109
5 FAL49
1 FAL151
5 FAL304
F FAL497
1 FAL174
F FAL209
1 FAL238
Z FAL53
F FAL295
F FAL181
1 FAL280
Z FAL105
Z FAL23
5 FAL247
F FAL337
5 FAL112
1 FAL66
5 FAL80
Z FAL45
5 FAL143
1 FAL79
1 FAL220
5 FAL73
Z FAL61
F FAL79
1 FAL218
1 FAL90
F FAL273
1 FAL201
Z FAL71
5 FAL119
F FAL296
1 FAL94
F
FAL82
FAL316
5 FAL90
F
1
FAL178
5 FAL269
5 FAL299
1 FAL211
1 FAL257
F
FAL77
1
FAL310
F
FAL164
FAL18
F
FAL24
5
5
FAL225
Z
FAL279
1
FAL75
Z
FAL249
1
FAL22
Z
FAL17
5
FAL35
Z
FAL260
F
FAL154
Z
FAL98
F
FAL491
F
FAL302
1
FAL457
F
FAL7
1
FAL474
F
FAL108
F
FAL295
F
FAL71
F
FAL34
1
FAL182
1
FAL165
Z
FAL96
F
FAL13
Z
FAL2
FAL76
Z
FAL458
5
F
FAL322
5
FAL169
1
SPR257
5
FAL212
F
SPR230
5
FAL234
5
FAL104
F
FAL293
5
FAL161
SPR9
1
F
FAL19
5
FAL203
Z
SPR12
Z
FAL216
F
SPR130
5
FAL161
F
SPR196
5
FAL476
F
SPR43
1
FAL162
F
SPR159
5
FAL231
FAL253
5
FAL122
FAL164
5
F
FAL6
FAL214
FAL120
5
F
FAL131
1
F
F
FAL133
1
F
FAL189
FAL256
1
F
FAL191
FAL219
1
F
FAL126
FAL165
F
F
FAL136
FAL346
F
F
FAL200
FAL373
F
F
FAL224
FAL220
F
F
FAL238
FAL340
F
1
FAL196
FAL156
F
5
FAL110
FAL251
1
F
FAL207
FAL298
F
5
FAL146
FAL197
F
F
FAL21
FAL163
1
F
FAL152
FAL454
5
F
FAL104
FAL119
5
1
SPR53
FAL75
1
F
SPR152
FAL283
1
F
SPR165
FAL211
F
F
SPR186
FAL142
1
Z
SPR128
FAL153
1
Z
SPR175
FAL187
5
F
SPR183
FAL240
5
SPR119
1 SPR138
1
SPR229
1 FAL215
1 SPR237
F
FAL190
5 SPR198
1 FAL85
5 SPR189
F FAL493
5 SPR186
F FAL121
5 SPR202
F FAL228
1 SPR159
F FAL106
F SPR201
F FAL123
1 FAL154
F FAL186
Z FAL213
F FAL113
1 FAL123
F SPR184
1 FAL117
F FAL33
Z FAL36
F FAL477
1 FAL245
F FAL124
5 FAL238
F SPR108
Z FAL11
1 SPR256
Z FAL47
F SPR123
5S
Z FAL25
OUTGROUP
1 FAL28
1 FAL209
Z FAL73
5 FAL97
5 FAL306
5 FAL23
5 FAL146
5 FAL7
1 FAL227
5 FAL94
Z FAL83
Z FAL221
5 FAL308
F FAL254
5 FAL17
5 FAL33
1 FAL45
5 FAL234
Z FAL211
F FAL227
F FAL246
5 FAL53
F FAL229
Z FAL133
Z FAL117
5 FAL3
Z SPR56
Z SPR52
Z FAL12
F FAL57
5 FAL153
5 SPR283
Z FAL55
Z SPR76
5 FAL240
Z SPR84
5 FAL36
Z SPR82
5 FAL200
Z SPR86
5 FAL227
Z SPR88
5 FAL101
1 SPR311
F FAL50
Z SPR80
5 FAL9
Z SPR18
5 FAL294
5 SPR123
5 FAL188
1 SPR131
5 FAL37
1 SPR14
F FAL449
Z SPR74
5 FAL74
5 SPR14
5 FAL77
Z SPR78
5 FAL255
Z SPR72
5 FAL163
Z SPR68
Z FAL29
Z SPR90
Z FAL131
Z SPR20
Z FAL101
5 SPR208
Z FAL69
5 SPR238
F FAL193
5 SPR28
5 FAL264
5 SPR268
5 FAL84
Z SPR12
Z FAL177
5 SPR133
F FAL201
5 SPR134
5 FAL226
Z FAL197
F FAL147
5 SPR135
1 FAL171
5 SPR185
F FAL94
5 SPR143
5 FAL257
Z SPR53
F FAL101
1 SPR129
F FAL202
Z SPR36
5 FAL195
5 SPR285
F FAL176
1 SPR153
F FAL204
Z SPR59
F FAL239
5 SPR267
1 FAL282
5 SPR232
F FAL339
Z SPR103
F FAL221
1 SPR309
5 FAL189
1 SPR70
F FAL234
5 SPR5
5 FAL99
F FAL30
Z FAL109
F FAL259
Z FAL173
5 SPR136
Z FAL189
5 SPR242
Z FAL201
5 SPR235
Z FAL81
5 SPR23
1 FAL77
5 SPR155
Z FAL129
1 SPR260
Z FAL50
1 SPR113
Z FAL257
1 SPR124
F FAL52
1 SPR134
Z FAL95
5 SPR223
5 FAL157
1 SPR130
Z FAL67
5 SPR161
Z FAL163
5 SPR228
1 FAL179
5 SPR209
1 FAL315
F SPR178
Z FAL27
1 SPR247
Z FAL107
F SPR274
Z SPR190
F FAL126
5 SPR8
F FAL125
Z SPR60
1 SPR146
Z SPR38
5 SPR272
5 SPR40
Z SPR117
Z SPR37
1 FAL29
Z SPR40
5 SPR210
5 SPR62
Z SPR7
Z SPR199
Z SPR171
Z SPR99
Z SPR125
5 SPR115
Z SPR107
Z SPR15
Z SPR115
Z SPR11
5 SPR192
Z SPR189
Z FAL59
5 SPR200
Z FAL31
Z SPR61
Z SPR2
5 SPR269
1 SPR317
Z SPR65
1 SPR319
Z SPR133
F SPR176
Z SPR139
F FAL352
5 SPR22
F FAL308
Z SPR33
F FAL143
1 SPR105
F FAL374
Z SPR209
F FAL191
1 SPR315
5 SPR114
5 SPR46
5 SPR111
Z SPR205
Z FAL259
5 SPR100
1 FAL197
Z SPR22
5 SPR151
F FAL170
Z SPR113
F FAL117
Z SPR163
F FAL371
1 SPR27
F FAL199
Z SPR180
F FAL108
Z SPR210
F FAL304
Z SPR181
F FAL115
Z SPR66
F FAL152
Z SPR193
Z FAL223
Z SPR187
Z FAL192
1 SPR141
Z FAL175
Z SPR198
5 FAL165
Z SPR195
5 FAL235
Z SPR158
Z FAL72
Z SPR213
Z FAL89
Z SPR168
Z FAL49
5 SPR4
Z FAL15
Z SPR176
Z FAL137
F FAL446
Z FAL167
F FAL91
Z FAL63
5 SPR81
1 FAL33
5 SPR2
5 SPR76
5 SPR92
1 FAL165
5 SPR1
5 SPR78
Z SPR16
5 FAL207
5 SPR226
1 FAL243
5 SPR249
Z FAL5
F FAL462
5 FAL142
1 SPR271
Z FAL269
5 SPR116
Z FAL21
1 SPR142
Z FAL33
Z SPR8
Z FAL199
1 SPR314
Z FAL32
F SPR258
5 FAL205
Z SPR47
5 FAL40
5 SPR225
1 SPR139
5 SPR156
Z FAL8
Z SPR185
5 FAL92
Z SPR208
1 FAL255
5 SPR245
5 SPR49
Z SPR93
5 FAL117
5 SPR215
Z FAL17
5 SPR57
5 FAL290
Z SPR10
5 SPR207
5 SPR252
Z FAL100
Z SPR29
Z FAL92
Z SPR31
Z FAL91
Z SPR19
5 FAL161
Z SPR46
5 FAL63
Z SPR58
5 FAL201
5 SPR122
5 FAL123
5 SPR124
Z FAL113
Z SPR41
Z FAL195
1 SPR285
Z FAL215
Z SPR178
Z FAL219
Z SPR211
1 FAL203
Z SPR138
5 FAL192
1 SPR63
5 FAL289
5 SPR16
F FAL192
1 SPR65
5 FAL213
1 SPR84
Z FAL159
5 SPR191
F FAL372
Z SPR192
1 SPR140
5 SPR118
F FAL58
5 SPR264
Z FAL10
5 SPR69
Z FAL135
Z SPR135
Z FAL9
5 FAL216
5 FAL122
5 SPR18
1 SPR297
1 FAL251
Z FAL115
5 FAL297
F FAL307
FAL12
F
FAL135
5 FAL284
1 FAL265
5 FAL51
F
FAL379
SPR213
F
SPR181
FAL275
1 SPR193
SPR33
1 SPR120
SPR89
5
SPR239
SPR139
5
SPR149
FAL97
1
SPR77
SPR86
5
SPR41
SPR214
F
SPR286
SPR28
5
SPR240
SPR127
F
SPR285
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Chapter 2
Rapid evolutionary innovation
during an Archean Genetic
Expansion
Lawrence A. David, Eric J. Alm
This chapter is presented as it was submitted for publication. Corresponding
Supplementary Material is appended.
Chapter 2
Rapid evolutionary innovation
during an Archean Genetic
Expansion
A natural history of Precambrian life remains elusive because of the rarity of microbial
fossils and biomarkers [79, 80]. The composition of modern day genomes, however,
may bear imprints of ancient biogeochemical events [81–83]. We have employed an
explicit model of macroevolution including gene birth, transfer, duplication and loss
to map the evolutionary history of 3,968 gene families across the three domains of life.
We observe that horizontal gene transfer (HGT) is the primary source of new genes
in prokaryotes, while duplication dominates in eukaryotes. Inter-domain gene transfer is rare compared to intra-domain transfer with the notable exception of massive
Bacteria-Eukarya transfer events that correspond to the endosymbiosis of the mitochondria and chloroplasts [84, 85]. Surprisingly, we find that a brief period of genetic
innovation during the Archean eon gave rise to 27% of major modern gene families.
Genes born during this period are especially likely to be involved in electron transport, while later genes exhibit a gradually increasing usage of molecular oxygen. Our
results demonstrate that reconstructing the complex interplay between organismal
and geochemical evolution over Earth history is becoming a tractable goal.
51
Introduction
Describing the emergence of life on our planet is one of the grand challenges of the
Biological and Earth sciences. Yet the roughly three-billion-year history of life preceding the emergence of hard-shelled metazoans remains obscure [79]. To date, the
best understood event in early Earth history is the Great Oxidation Event, which is
believed to follow the invention of oxygenic photosynthesis by the ancestors of modern
cyanobacteria [86] (though the precise timeline remains controversial [80]). If DNA
sequences from extant organisms bear an imprint of this event, then we can use them
to make and test predictions [81–83]; e.g., genes that use molecular oxygen will be
confined to a group of organisms emerging after the Great Oxidation Event. Transfer
of genes across species, however, can obscure patterns of descent and disrupt our ability to correlate gene histories with the geochemical record [87]. Widely distributed
genes, for example, may descend from a Last Universal Common Ancestor (LUCA)
as widely believed to be the case for the translational machinery [88], or may have
been dispersed by HGT [12, 89], as in the case of antibiotic resistance cassettes.
Methods Overview
We developed a new phylogenomic method, AnGST (Analyzer of Gene and Species
Trees), to account for the confounding effects of HGT by comparing individual gene
phylogenies to the phylogeny of organisms (the Tree of Life). We refer to this process
as tree reconciliation and provide a detailed description of the AnGST algorithm in
the Supplementary Information. Unlike some previous methods [24, 25, 27], AnGST
uses the topology of the gene family tree rather than just its presence/absence across
genomes and can infer duplication, HGT, and loss events. Importantly, AnGST also
accounts for uncertainty in gene trees by incorporating reconciliation into the treebuilding process: the tree that minimizes the evolutionary cost function, but is still
supported by the sequence data, is chosen as the best gene tree. Simulated trees
inferred with this method are more accurate than trees based on a maximum likelihood model of sequence data alone (Figure 2.5). Thus, tree building methods such as
52
AnGST that explicitly model macroevolutionary events may have utility in phylogenetic inference [33]. We used a previously described Tree of Life [90] to reconcile gene
families, although we note that our key results were consistent when using 30 alternative reference trees, including those that used the Archaea or Eukarya as outgroups
(Figs. 2.11, 2.12). Ensuring proper causality in a large reconciliation (i.e., avoiding
the “grandfather paradox” in which a gene is inferred to be its own ancestor) is a
computationally intractable problem in general [31], which we overcome by explicitly
modeling the timing of evolutionary events based on a chronogram constructed from
our reference tree. A conservative set of eight temporal constraints was selected from
the geochemical and paleontological literature (Table 2.1), and the PhyloBayes software package was used to infer a range of divergence times for each ancestral lineage
on the reference tree [91]. We did not apply temporal constraints to lineage ages on
the gene trees.
Results
Domain-specific Macroevolutionary Trends
For 3,968 extant gene families [106], AnGST predicted a total of 109,452 speciation,
38,575 HGT, 14,021 gene duplication, and 35,252 gene loss events (Figure 2.1A). The
abundance of HGT events (on average 9.7 per gene family) underscores the evolutionary importance of gene transfer in prokaryotic genome structure (Figure 2.15).
Domain-specific preferences in the types of macroevolutionary events emerge in Figure 2.1B. On a per-gene basis, gene transfer is 2.1 times more likely in bacteria than
in eukaryotes, while duplications are 4.4 times more likely in eukaryotes than in bacteria. The rate of HGT in eukaryotes is likely to be an overestimate because we did
not consider eukaryote-only gene families. The bias toward duplication in eukaryotes is consistent with known domain-specific traits, such as unequal crossing-over,
whole-genome duplication events, and reduced selection against large genome sizes
[16, 107, 108]. Interdomain transfers comprise a minority (16.1%) of HGT events
but exhibit significant over-representation of HGT from alpha-proteobacteria to an-
53
Event
Last universal common ancestor arises
Cyanobacteria emerge
Constraint
< 3850 Ma
> 2670 Ma
5
6
Eukaryotes diverge from Archaea
Akinetes
diverge
from
cyanobacteria lacking cell
differentiation
Archaeplastida emerge
Animals emerge
7
Tetrapods emerge
(a) < 385 Ma
(b) > 359 Ma
8
Buchnera diverge from Wigglesworthia
> 160 Ma
1
2
3
4
> 2500 Ma
> 1500 Ma
> 1198 Ma
> 635 Ma
Evidence
Carbon isotope fractionation
[92, 93]
Traces of an aerobic nitrogen cycle [94], changes in
redox-metal enrichments [95],
and sulfur isotope fractionation data [96, 97] indicate oxygenic photosynthesis; traces
of 2α-methylhopane biomarkers [98] indicate cyanobacterial
presence
Preserved sterane biomarkers
[98, 99]
Akinete microfossils [100]
Red algae microfossils [101]
Preserved demosponge steranes [102]
(a) Tetrapod precursor dating
[103]; (b) Tetrapod fossil dating [104]
Fossil history of Buchnera’s
aphid hosts [105]
Table 2.1: Temporal constraints used to construct chronogram. Eight temporal constraints that could be directly linked to fossil or geochemical evidence were used to estimate
divergence times on the Tree of Life (Figure 2.10).
54
cient eukaryotes (p=3.3 × 10−7 Wilcoxon rank sum test) and from cyanobacteria to
plants (p=8.3 × 10−6 Wilcoxon rank sum test). These results likely reflect the ancient endosymbioses that gave rise to the mitochondrial and chloroplast organelles
[84, 85]. Functional analysis of HGT from the alpha-proteobacteria to the ancestral
eukaryotes reveals significant enrichment for energy metabolism genes (p=1.6 × 10−6
Fisher’s Exact Test), further supporting an association between these HGT and an
energy-producing endosymbiosis (Figure 2.18). HGT from cyanobacteria to Arabidopsis thaliana are also enriched for energy-producing genes (p=3.9×10−3 Fisher’s Exact
Test), as well as translation-related genes (p=4.4 × 10−5 Fisher’s Exact Test) which
likely reflect the migration of 70S ribosomal proteins from the chloroplast to the plant
nucleus [109].
An Archean Genetic Expansion
Gene histories reveal dramatic changes in the rates of gene birth, duplication, loss,
and HGT over geologic time scales (Figure 2.2). The most striking feature of the
overall gene flux depicted in Figure 2 is a burst of de novo gene family birth between
3.33-2.85 Ga which we refer to as the Archean Genetic Expansion (AGE). This window gave rise to 26.8% of extant gene families and coincides with a rapid bacterial
cladogenesis. A spike in the rate of gene loss (∼3.1 Ga) follows the AGE and may
represent consolidation of newly evolved phenotypes, as ancestral genomes became
specialized for newly emerging niches. After 2.85 Ga, the rates of both gene loss and
gene transfer stabilize at roughly modern-day levels. The rates of de novo gene birth
and duplication after the AGE appear to show opposite trends: de novo gene family
birth rates decrease and duplication rates increase over time. The near absence of de
novo birth in modern times likely reflects the fact that ORFan gene families, which
are widespread across all major prokaryotic groups, are not considered in this study
[110]. The excess of gene duplications and ORFans in modern genomes suggests that
novel genes from both sources experience high turnover and rarely persist over long
evolutionary time scales.
What evolutionary factors were responsible for the period of innovation marked by
55
the AGE? While we cannot provide an unequivocal answer to this question using gene
birth dates alone, we can ask whether the functions of genes born during this time
suggest plausible hypotheses. In general, birth of metabolic genes is enriched during
the AGE, especially those involved in energy production and coenzyme metabolism
(Table 2.17), but further inspection also reveals an enrichment for metabolic gene
family birth prior to the AGE. To focus on specific metabolic changes linked to the
AGE we: (i) grouped genes according to the metabolites they used; and (ii) we directly
compared the occurrence of these metabolites in genes born during the AGE to their
abundance prior to the AGE. The results are striking: the AGE-specific metabolites
(positive bars, Figure 2.2 inset) include most of the compounds annotated as redox/e−
transfer (blue bars), with Fe-S-, Fe-, and O2 -binding gene families showing the most
significant enrichment (False Discovery Rate < 5%, Fisher’s exact test). Gene families
that use ubiquinone and FAD (key metabolites in respiration pathways) are also
enriched, albeit at slightly lower significance levels (False Discovery Rate < 10%).
The ubiquitous NADH and NADPH are a notable exception to this trend and appear
to have played a role early in life history. By contrast, enzymes linked to nucleotides
(green bars) exhibited strong enrichment in genes of more ancient origin than the
AGE.
The observed metabolite usage bias suggests that the AGE was associated with an
expansion in microbial respiratory and electron transport capabilities. Proving this
association to be causal is beyond the power of our phylogenomic model. Yet this
hypothesis is appealing because more efficient energy conservation pathways could
increase the total free energy budget available to the biosphere, possibly enabling
the support of more complex ecosystems and a concomitant expansion of species and
genetic diversity. We note, however, that while the use of oxygen as a terminal electron
acceptor would have significantly increased biological energy budgets, oxygen-utilizing
genes are only enriched toward the end of the AGE (Figure 2.14). Thus, the earliest
redox genes we identified as part of the AGE were likely to be used in anaerobic
respiration or oxygenic/anoxygenic photosynthesis, although some may have been
co-opted later for use in aerobic respiration pathways.
56
Phylogenomic evidence for ancient changes in global redox potential
Our metabolic analysis supports an increasingly oxygenated biosphere following the
AGE, as the fraction of proteins utilizing oxygen gradually increases from the AGE
until the present day (Figure 2.3; p=3.4 × 10−8 , two-sided Kolmogorov-Smirnov test).
Further indirect evidence of rising oxygen levels comes from compounds that are sensitive to global redox potential. We observe significant increases over time in the usage
of the transition metals copper and molybdenum (Figure 2.3; False Discovery Rate
< 5%, two-sided Kolmogorov-Smirnov test), which is in agreement with geochemical
models of these metals’ solubility in increasingly oxidizing oceans [82, 83] and with
the growth of molybdenum enrichments from black shales that suggests molybdenum
began accumulating in the oceans only after the Archean eon [111]. Our prediction of
a significant increase in nickel utilization accords with geochemical modeling predictions of a 10X increase in dissolved nickel concentration between the Proterozoic and
modern day [82], but conflicts with a recent analysis of banded iron formations that
inferred monotonically decreasing maximum deposited nickel concentrations from the
Archean onwards [112]. The abundance of enzymes using oxidized forms of nitrogen
(N2 O and NO3 ) also grows significantly over time, with 1/3 of nitrate-binding gene
families appearing at the beginning of the AGE and 3/4 of nitrous oxide-binding gene
families appearing by the AGEs end. The timing of these gene family births provides
phylogenomic evidence for an aerobic nitrogen cycle by the Late Archean [94].
One striking discrepancy between our phylogenomic patterns and geochemical predictions, however, is a modest but significant increase in iron-using genes over time
(Figure 2.3; False Discovery Rate < 5%, two-sided Kolmogorov-Smirnov test). The
cessation of iron formation deposition roughly 1.8 Ga and ocean chemistry models indicate that iron usage should decrease following the Archean, as declining iron solubility in oxygenated ocean surface waters and sulfide-mediated iron removal from anoxic
deeper waters combined to reduce overall iron bioavailability [113]. The counterintuitive phylogenomic prediction may reflect the confounding effect of evolutionary
inertia, whereby microbes in the face of declining iron availability could have found
57
more success evolving a handful of metal-acquisition proteins (e.g. siderophores),
rather than replacing a host of iron-binding proteins. Alternatively, the insolubility
of iron in modern oceans may be offset by large existing organic pools of reduced iron.
A precise timeline for oxygen availability is beyond the resolution of our relaxed
molecular clock approach and remains a contentious topic in organic geochemistry
[80, 98, 99]. Nonetheless, our results suggest an Archean biosphere containing some
of the basic components required for oxygenic photosynthesis and respiration, despite
the fact that appreciable oxygen levels do not appear in the geological record until
much later (roughly 2.5 Ga) [114, 115]. Although our results are consistent with recent
biomarker-based evidence for early oxygenesis [99], special caution should be used in
comparing the molecular and geological dates. Divergence times for deep nodes on
our reference chronogram are uncertain (Figure 2.16), and they are partially based on
the constraint that the LUCA could be as old as the earliest evidence for life (3.85 Ga)
[92], even though the LUCA is likely a descendant of the first life form. Furthermore,
although the PhyloBayes dates include uncertainty estimates that are accurate given
the assumptions of the CIR model [91], an alternative, semi-parametric approach
implemented in r8s [116] results in a much younger date of 2.75-2.5 Ga for the AGE
(compared to 3.33-2.85 Ga for PhyloBayes) which is closer to the Great Oxidation
Event (Figure 2.12). Here we present mainly results from the PhyloBayes program,
because it allowed us to explicitly account for uncertainty in the timing of inferred
events. With such disparate phylogenetic estimates of the timing of the most ancient
lineages, a chronology for the AGE and the evolution of oxygen-producing genes will
require a careful integration of both geochemical and genetic data.
Implications
Using just eight temporal constraints as our geochemical and paleontological guides,
we have shown that whole genome sequence data can be used to infer details of
microbial evolution during the Archean eon and can recount global changes in redoxsensitive compound bioavailability following the evolution of oxygenesis. Still, our
phylogenomic approach represents only a first step toward linking whole genome
58
sequence data to early Earth history. By connecting events in gene histories to events
in Earth history, hypotheses of enzyme or pathway presence/absence can be used
to make testable predictions about when metabolic signatures should first appear in
the geochemical record. Conversely, geochemical hypotheses may be tested against
predictions of extant metabolisms (as we demonstrate using the rise of oxygen). This
may admit useful new lines of evidence for geochemical theories that suffer from gaps
in the rock record. Successive refinement of phylogenomic models against geochemical
constraints may eventually yield an abundant and reliable source of Precambrian
fossils: modern-day genomes.
59
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%RUUH
įSURWHREDFWHULa
$FLGREDFWHULD
Euryarchaeota
Methan. kandle.
%UXFHODERUWX
İSURWHREDFWHULa
Nanoarchaeota
Crenarcheota
Transfer Origins
0.022
0.065
0.188
0.181
40%
57%
3%
A
0.016
0.039
0.171
0.151
5%
93%
2%
A
0.008
0.171
0.083
0.102
5%
61%
34%
A
B
E
B
E
B
E
Alm, Figure 1
Figure 2.1: Evolutionary events by lineage. (A) The number of macroevolutionary events
is mapped to each lineage on an ultrametric Tree of Life and visualized using the iToL
website [74]. Pie chart area denotes the number of events, and color indicates event type:
gene birth (red), duplication (blue), HGT (green), and loss (yellow). (B) The average
number of events per gene copy is separated by domain and the origins of HGT events are
depicted: Bacteria (green), Archaea (beige), Eukarya (violet).
60
[events / genome / 10 Ma]
Gene loss rate
4
2
0
2
3.5
4
Gene gain rate
8
Archean Gene Expansion
3.0
*Ubiquino.
Fructose
)Hï6
**Fe
Ferrocyt.
Time [Ga]
2.5
10
Archean
H2O2
Proterozoic
**O2
FADH2
*OF1$Fï
Glucose
Oxaloace.
Gen e- acceptor
Zn
*FAD
Glyceron.
Alcohol
2.0
Birth
Transfer
Duplication
Loss
6XFFLQDW.
Oxogluta.
Acetate
CoA
+&2ï
Carboxyl.
Phosphoe.
NAD+
Cysteine
Biotin
H2O
H+
Glyceral.
Methioni.
Glutamat.
CTP
CO2
Thiamin .
1.5
[log2(enrich)] in AGE
0
1
2
3
NH3
Fructose.
tRNA
NADPH
THF
Aspartat.
6HULQe
Pyruvate
DNA
GDP
NTP
**PRPP
Proterozoic
0.5
0.0
2
1
0
[log2(enrich)] pre-AGE
Phanerozoic
Redox / e- transfer
Carbohydrate
Nucleotide
Other
Present
Glutamin.
Pyridoxa.
*Orthopho.
Formate
1.0
AMP
GMP
ATP
Glycine
PPi
Mn
Fumarate
6$0
UDP
GTP
CMP
**UMP
TCA / Glycolysis
Carboxylic Acid
Amino Acid
Figure 2.2: Rates of macroevolutionary events over time. The figure shows the rates of
macroevolutionary events (colors as in Figure 2.1) as a function of time. Shown are the
average rates of evolutionary events per lineage (events / 10 Ma / lineage). Events that
increase gene count are plotted to the right, and gene loss events are shown to the left (yellow
curve). Genes already present at the LUCA are not included in the analysis of birth rates
because the time over which those genes formed is not known. The AGE was also detected
when alternative chronograms were considered (Figure 2.12). Inset: metabolites or classes of
metabolites ordered according to the number of gene families that use them that were born
during the AGE compared to the number born before the expansion. Metabolites whose
enrichments are statistically significant at a False Discovery Rate < 10% or < 5% (Fisher’s
Exact Test) are identified using one or two asterisks, respectively. Bars are colored by
functional annotation or compound type (functional annotations were assigned manually).
Metabolites were obtained from the KEGG database release 51.0 [117] and associated with
COGs using the MicrobesOnline September 2008 database [118]. Metabolites associated
with fewer than 20 COGs or sharing more than 2/3 of gene families with other included
metabolites are omitted.
61
Sulfur Comp.
(79)
Nitrogen Comp.
(115)
C1 Comp.
(214)
1.3
4.0
2.9
Fe*
36.0
43.4
44.7
Zn
Mn
24.0
24.7
22.8
40.0
19.2
15.0
Cu*
Mo*
Co
Ni*
0.0
2.7
4.8
0.0
2.8
4.6
0.0
4.8
4.1
0.0
2.5
4.0
S
SO4
42.0
50.6
54.0
1.2
6.5
17.0
16.2
1.1
SO3
19.3
14.4
14.7
H 2S
9.7
8.1
8.0
S2O3
22.6
9.9
7.1
NH3
100.0
94.6
90.8
NO3*
0.0
2.8
5.8
N2O*
0.0
1.6
2.4
N2
0.0
0.9
0.9
≥ 1.5
1.4
1.3
1.0
0.9
0.8
0.6
≤ 0.5
CO2
83.3
79.6
77.0
CHO2
10.0
14.0
14.0
CH2O
4.4
3.8
5.1
CH4O
2.2
2.6
3.7
CH4
0.0
0.0
0.2
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.7
Gene fraction normalized to present day
Transition Metals
(174)
Oxygen*
(88)
0.0
Time [Ga]
Alm, Figure 3
Figure 2.3: Genome utilization of redox-sensitive compounds over time. The first bar
illustrates a gradual increase in the fraction of enzymes that bind molecular oxygen predicted
to be present over Earth history (p=1.3×10−7 , two-sided Kolmogorov-Smirnov test). Colors
indicate abundance normalized to present-day values. The lower four panels group transition
metals, nitrogen compounds, sulfur compounds, and C1 compounds. The fraction of each
group’s associated genes that bind a given compound, normalized to present-day fractions,
is shown over time using a color gradient. Enclosed boxes show raw fractional values at
three time points: 3.5 Ga (left); 2.5 Ga (middle); and the present day (right). For example,
18.9% of transition metal-binding genes are predicted to have bound Mn at 2.5 Ga, a value
1.26 times the size of the modern day percentage of 15.0%. Values within parentheses
give the overall number of gene families in each group. To determine which compounds
showed divergent genome utilization over time, the timing of copy number changes for each
compound’s associated genes was compared to a background model derived from all other
compounds. Compounds whose utilization significantly differs from the background model
are marked with an asterisk (False Discovery Rate < 5%, two-sided Kolmogorov-Smirnov
test). Nitrite and nitric oxide are not shown due to their COG-binding similarity to nitrate
and nitrous oxide, respectively.
62
2.2
Supplementary Material
2.2.1
Overview
We developed a phylogenomic method that we named AnGST (Analyzer of Gene
and Species Trees), which “reconciles” any observed differences between a gene tree
and a reference tree (species tree) by inferring a minimal set of evolutionary events,
including horizontal gene transfer (HGT), gene duplication (DUP), gene loss (LOS),
speciation (SPC) and exactly one gene birth or genesis event (GEN). Each event type
is assigned a unique cost, and the overall sum of costs associated with a reconciliation
is minimized (i.e., we use a generalized parsimony criterion). We address previously
described shortcomings of similar parsimony-based models of host-parasite evolution
[119] by accounting for phylogenetic uncertainty (using a new approach described
below) and directly estimating event costs from our large dataset. We divide the
gene-tree/species-tree reconciliation process into two components:
• The basic reconciliation step assumes a known gene tree and species tree and
identifies the set of evolutionary events (HGT, DUP, LOS, SPC, GEN) needed
to explain any discordance between the trees
• The tree amalgamation step accounts for gene tree uncertainty by incorporating tree construction into the reconciliation process: multiple gene tree
bootstraps are provided to AnGST and the algorithm retains and combines
bootstrap subtrees which yield the most conservative reconciliation consistent
with the sequence data.
The estimation of event costs from the input data is based on reducing large fluctuations in ancient genome sizes. This method is presented in Section 2.2.5 together
with a sensitivity analysis for the resulting parameters.
The AnGST software package is implemented in the Python programming language and can be downloaded from: http://almlab.mit.edu/ALM/Software/.
63
2.2.2
Basic Reconciliation Algorithm
First assumptions
The basic reconciliation step requires a rooted, strictly bifurcating gene tree G and
species tree S. Each tree is composed of a set of nodes linked to one another by a
set of connecting edges. We assume that each node g in G can be mapped to a node
s in S, a mapping we abbreviate as g:s. This mapping describes which (extant or
ancestral) genome hosted a given (extant or ancestral) gene copy. Maps are known
with certainty for extant genes, but must be inferred for ancestral gene copies.
Algorithm explanation
Our goal in gene/species tree reconciliation is to recover the optimal set of evolutionary events that explain any topological discordance between the gene and species
trees. A brute-force search through all possible evolutionary histories is intractable,
as the number of possible histories grows exponentially with increasing tree size [120].
However, for a given gene and species tree pair, there are only |S| possible mappings
for the root node of the gene tree, gr . If the optimal reconciliation is already known
for each possible mapping gr :sr , where sr is a node in S, a new outgroup for the
gene tree can be added (making gr a child of the new root node gn ), and optimal
reconciliations for the larger gene tree can be quickly computed using the following
method:
1. For each possible pair of mappings (gr :sr , gn :sn ) where sr and sn are nodes in
S
(a) Choose the most parsimonious explanation for how a gene copy in sn descended into sr .
(b) Concatenate this history to the known optimal reconciliation for gr :sr , to
produce the optimal reconciliation for the (gr :sr , gn :sn ) pair
2. Identify optimal reconciliations for each mapping gn :sn by selecting the minimal
overall reconciliation cost associated with (gr :sr , gn :sn ) as sr is varied over the
64
nodes of S.
Using the above method, the reconciliation problem can be formulated in a dynamic programming framework, yielding computational complexity that is a polynomialtime function of gene tree size. The AnGST program implements this algorithm as a
post-fix traversal of the gene tree. At each node, reconciliations from child subtrees
are combined in mini-reconciliations, which explain how the gene copy at g coalesced
from two child copies c1 andc2 (i.e., whether HGT, speciation, or duplication occurred), assuming the mappings g:s, c1 :s1 , and c2 :s2 . This is repeated for each s, s1 ,
and s2 ∈ S. Mini-reconciliations return optimal duplication-loss or HGT scenarios if s
is the last common ancestor of s1 and s2 , or if s is identical to either s1 or s2 . All other
combinations of s, s1 , and s2 , yield mini-reconciliations that we refer to as complex
scenarios. We include these scenarios in the pseudocode below to aid understanding of basic reconciliation design, but we do not provide a method for their solution
since complex scenarios can be safely ignored without loss of reconciliation optimality (see Running Time discussion below). If g is a leaf node, mini-reconciliations are
unnecessary since the true mapping from g to the species tree is known. Once all
combinations have been evaluated, we retain the optimal reconciliation associated
with each possible mapping of g to the species tree. Pseudocode for the reconciliation
algorithm is provided on the following page in Python style.
65
Pseudocode:
%
•
%
•
Main %
Reconcile(gene_tree.root)
Methods %
define Reconcile(node):
• child_1, child_2 = ChildNodes(node) %strictly bifurcating tree
• if child_1 AND child_2 are null: %is a leaf node
• for node_map in AllNodes(species_tree):
• if node_map is KnownHostGenome(node):
• node.reconciliation_cost(node_map) = 0 %correct answer is known for leaves
• else:
• node.reconciliation_cost(node_map) = maxint
return
•
• Reconcile(child_1) %post-fix traversal
• Reconcile(child_2)
• for node_map in AllNodes(species_tree): %try all possible hosts for ancestor
• for child_1_map in AllNodes(species_tree): %try all possible hosts for children
• for child_2_map in AllNodes(species_tree):
• events = MiniReconcile(node_map, child_1_map, child_2_map)
• prior_events_1 = child_1.reconciliation_cost(child_1_map)
• prior_events_2 = child_2.reconciliation_cost(child_2_map)
• overall_cost = Cost(events + prior_events_1 + prior_events_2)
• cost_matrix(node_map, child_1_map, child_2_map) = overall_cost
• for node_map in AllNodes(species_tree):
• node.reconciliation_cost(node_map) = Min(cost_matrix(node_map, :, :))
• return
• define MiniReconcile(node_map, child_1_map, child_2_map):
• % compute DupLoss scenarios
• if node_map is ancestral to child_1_map AND child_2_map:
• if node_map is last_common_ancestor of child_1_map AND child_2_map:
• %%% See Page46 for DupLoss pseudocode
• duploss_events = DupLoss(node_map, child_1_map, child_2_map)
• else:
• duploss_events = ComplexScenario()
• %ComplexScenario() not implemented -- see Methods Section 1.1.2 Running Time
discussion for explanation
• else:
• duploss_events = maxint % impossible to reconcile with only dup-loss
%
• compute HGT scenarios
• if node_map is child_1_map:
• hgt_events = {HGT from node_map to child_2_map}
elif
node_map is child_2_map:
•
• hgt_events = {HGT from node_map to child_1_map}
• else:
• hgt_events = ComplexScenario()
• return MinCost(hgt_events,duploss_events)
5
An example reconciliation:
An AnGST reconciliation of two simple, but discordant, gene and species trees is
provided in Figure 2.4. Here, we assume that we know the true mappings from the
leaves in G to S: g1 :sA , g2 :sC , g3 :sB . Because AnGST uses a post-fix traversal of G
and the mapping of G’s leaves to S is trivial, we first investigate how g4 is mapped
to nodes in S. We initialize the algorithm by assigning infinite reconciliation cost to
leaf mappings which deviate from the known leaf mappings (e.g. g1 :sB ); thus, there
is only one valid mapping for g1 and g2 .
In Scenario α, g4 is mapped to sA (g4 :sA ) and we infer one HGT event using
the mini-reconciliation algorithm (since g4 is mapped to the same lineage as one
of its child nodes). Similarly, if we consider g4 :sC , we infer one HGT from sC to
sA (Scenario β). In the case of g4 :sE (Scenario γ), g4 is mapped to the LCA of
sA and sE and a duplication-loss scenario is invoked by the mini-reconciler. Other
more complex scenarios exist (e.g., s4 :sD ), but these can be ignored without affecting
overall reconciliation optimality (see Running Time section below). Once optimal
reconciliations have been found for each possible g4 :s mapping, AnGST recurses to
g5 and repeats the process. In the next mini-reconciliation, there are multiple valid
g4 :s mappings. Thus AnGST must iterate through prospective mappings for both g5
and g4 (although for the sake of illustrative simplicity, we only enumerate a fraction
of these scenarios).
In the first mapping shown for g5 (g5 :sD , g4 :sA , g3 :sB ), there is 1 SPC (since
sA and sB are direct vertical descendants of sD ) and this cost is added to the 1
HGT already inferred in Scenario α, which resulted in g4 :sA . For the combination
(g5 :sC ,g4 :sC ,g3 :sB ), a cost of 1 HGT (because g5 and g4 share the same mapping) is
added to the cost for Scenario β. The last mapping shown is (g5 :sE ,g4 :sE ,g3 :sB ). A
mini-reconciliation that posits HGT will imply forward-in-time gene transfers – an
evolutionary event we do not allow (see Temporal constraints on HGT below). Instead, a DUP in sE and subsequent losses among sA and sC are needed to correctly
explain the mapping of s5 :sE , s4 :sE , and g3 :sB . The g5 mapping that leads to the
67
optimal reconciliation is a function of the chosen evolutionary event costs. With a
cost structure: CSP C =0, CHGT =1, CLOS =2, CDU P =3, the optimal mappings would
be g5 :sD , g4 :sA , and the associated reconciliation would be a GEN event at sD , followed by SPC at sD , and an HGT from sA to sC . However, if CSP C =0, CHGT =10,
CLOS =2, CDU P =3, the optimal mapping would be g5 :sE , g4 :sE , and the associated
reconciliation would be an initial GEN event at sE , followed by a DUP in sE , 2 SPCs
each at sE and sD , and LOS in lineages sA , sB , and sC .
Running time
O(|G|*|S|3 ) is an upper bound on run-time complexity of AnGST, where |S| and
|G| are the number of nodes in those trees, respectively. Running times can be
significantly reduced without loss of reconciliation optimality, however, with a simple
speedup. When performing mini-reconciliations on all combinations of g:s, c1 :s1 , and
c2 :s2 for s, s1 , and s2 ∈ S, any complex scenario (s is not s1 or s2 , and s is not the
last common ancestor of both s1 or s2 ) will require at least two HGT (one to s1 and
another to s2 ), or one HGT to the last common ancestor of s1 and s2 followed by a
duplication-loss scenario originating at that ancestor. These more complex scenarios
will therefore always be suboptimal with respect to non-complex scenarios and their
evaluation can be skipped during the reconciliation process. The resulting reduction
in mapping search space lowers AnGST run-time complexity to O(|G|*|S|2 ). When
temporal constraints on HGT are enforced (see below), this speedup cannot be fully
exploited, as nodes ancestral to s1 and s2 are potentially optimal values for s in HGT
scenarios.
In practice, on 3.0Ghz single-cores with access to 8GB of memory, an AnGST run
reconciling 100 bootstrap trees from one gene family against a reference tree of 100
species would take roughly: 0.1 minutes for gene trees with 10 leaves, 4 minutes for
gene trees with 50 leaves, 13 minutes for gene trees with 100 leaves, 27 minutes for
gene trees with 150 leaves, 37 minutes for gene trees with 200 leaves.
68
Temporal constraints on HGT
If provided a chronogram as a reference tree, AnGST will restrict the set of possible
inferred gene transfers to only those between contemporaneous lineages. This feature
eliminates the possibility of inferring multiple HGT events which are chronologically
impossible [31]. Any non-zero chronological overlap is sufficient to allow transfers.
But, if a gene transfer is inferred from node s1 to node s2 , subsequent transfers of the
gene copy in s2 may only occur with lineages which exist during the range T1 ∩ T2 ,
where T1 and T2 are the times spanned by the parent edges of s1 and s2 , respectively. A
feature enabling transfers forward in time (which may represent “phantom transfers”
from unsampled taxa [121]) has been built into AnGST, but remains off by default
and was not used in our analyses.
Gene tree rooting
Bootstrap trees are assumed to be unrooted. All possible rootings of these bootstrap
trees are evaluated during the reconciliation process. The resulting gene tree is rooted
on the branch that results in the overall lowest reconciliation score.
2.2.3
Bootstrap tree amalgamation
Errors or uncertainty in gene phylogenies can lead to the inference of spurious macroevolutionary events [32] and is a particular concern for deeply branching phylogenies
[122]. AnGST resolves uncertainty by incorporating reconciliation into the treebuilding process: the tree with the lowest reconciliation cost is chosen from a large
ensemble of trees consistent with the sequence data. To generate an ensemble of
suitable trees, AnGST considers the set of all trees that contains only bipartitions
observed in a set of input trees, which we generate with non-parametric bootstrapping. Thus, AnGST typically outputs chimeric trees that do not match any of the
input bootstrap trees exactly, although every bipartition in the AnGST tree occurs
in at least one of the bootstraps. In simulations, we observe these trees to be significantly more accurate than trees based on sequence likelihood alone, although they
69
generally have lower likelihood (see Chimeric tree fidelity below and Figure 2.5). Any
number of bootstrap trees can be used, but we found limited increase in accuracy in
simulated data as a result of using more than 10 (data not shown).
We implement this approach in the following manner (see Figure 2.6 for an example). Given n gene tree bootstraps {G1 , G2 , ... Gn } and a reference tree S, AnGST
will begin the basic reconciliation algorithm starting on tree G1 . Each time AnGST
evaluates an internal node g of G1 , it also evaluates the set of internal nodes I = {g1 ,
g2 , ... gk } in other bootstrap gene trees that define the same bipartition as g. The
optimal reconciliation at this node is the lowest scoring scenario/topology observed
in any of the bootstrap trees. That is, a distinct solution is computed for each possible mapping (gi :s for gi ∈ I, s ∈ S), and only the best solution is retained for each
value of s. These |S| optimal mappings and their reconciliations are subsequently
shared across all the nodes in I. This last step creates “chimeric” gene trees, as the
reconciliation at g in G1 may now refer to a topology found in bootstrap Gi .
2.2.4
Simulation and Benchmarking
Simulation
Benchmarking
We used simulations to benchmark the performance of AnGST. Ten independent
gene trees births were simulated on each of the 199 extant and ancestral lineages of
the reference tree. A simple Poisson statistics-based model of HGT, DUP, and LOS
was used to generate random gene histories and associated gene trees; the average
simulated gene family underwent 0.21 HGT, 0.05 DUP, 0.76 SPC, and 0.26 LOS per
extant gene copy. (For comparison, our analysis of the COG dataset inferred 0.29
HGT, 0.10 DUP, 0.83 SPC, and 0.27 LOS per extant gene copy.) Synthetic amino
acid sequences were generated using these simulated trees and the SeqGen software
(v.1.3.2) [123]. Trees were reconstructed from the synthetic sequences using either the
BIONJ algorithm (implemented in PhyML), PhyML (v.2.4.5) [72], or AnGST via 100
PhyML-generated bootstrap topologies (see Section 2.2.8 for PhyML parameters). A
70
subset of 75 gene families were used to learn costs for HGT and DUP (see Methods
Section 2.2.5). A cost combination of CHGT =4,CDU P =3 minimized genome size flux
using this gene family subset (compared to CHGT =3, CDU P =2 learned for the COG
dataset).
Chimeric tree fidelity
Following reconciliation, nodes deep in the interior of the resultant gene tree can
contain topologies not found in any of the inputted bootstraps (although all possible
bipartitions of these subtrees will exist in at least one of the bootstraps). Thus, the
potential search space of topologies is vast. We tested the fidelity of the chimeric
gene trees learned during the reconciliation process using the Robinson-Foulds (RF)
statistic [124], which measures the number of bipartitions not shared by a pair of
trees. A 0 RF score indicates perfect concordance (all bipartitions of the candidate
and reference tree are identical) and increasing RF scores denote higher phylogenetic discordance. Analysis of the 225 gene trees with a minimal level of complexity
(more than 10 leaves) demonstrates that AnGST trees are significantly more accurate than trees generated by BIONJ (p=9.110-8 Wilcoxon rank sum test) or PhyML
alone (p=1.810-2 Wilcoxon rank sum test). Interestingly, this increase in topological
accuracy comes with a likelihood tradeoff in comparison to the PhyML algorithm
(p=2.710-39 Wilcoxon rank sum test). As an aside, we note that the PhyML likelihoods in these analyses are in agreement with previous simulations which showed
PhyML capable of constructing trees with higher likelihood than the true topologies
[72].
Inferred birth date accuracy
We benchmarked the accuracy of gene family birth dates predicted by AnGST using
the 747 synthetic gene families that included more than one extant gene copy. A
comparison of inferred birth events and the simulated age of birth events is shown in
Figure 2.7A. There is a strong correlation between inferred and simulated ages (0.88)
and 76% of births are predicted to within 250 My of their simulated age. These
71
results are especially promising given the noisy processes (sequence simulation and
phylogenetic inference) separating simulation of a gene family and its reconciliation.
Moreover, we see no obvious evidence of inference bias which may lead to the false
inference of a birth spike. Direct comparison of birth counts during the AGE (2.9-3.3
Ga) to simulated births during the same period (Figure 2.7B) did not show a bias
towards over-counting births. We did, however, observe a bias toward gene birth prior
to the AGE, suggesting that our set of very ancient genes (born prior to 3.3 Ga) may
be inflated.
2.2.5
Parameter learning
Minimizing genome size flux
We address the problem of assigning the costs to each event type in a manner similar
to some previous studies [25, 27, 125]: we use predictions of ancestral genome sizes
to constrain the costs CDU P and CHGT (these are the only free parameters as we
can assume CLOS =1 and CSP C =0 without loss of generality). However, we chose
to minimize differences in genome size between parent and child nodes (a metric we
refer to as genome size flux) rather than constraining overall genome size over time for
two reasons: first, gene acquisition rates may not have been constant over time and
ancient genomes may have been smaller (or larger) than modern day genomes [125];
second, the extinction of ancient gene families would lead to a trend of smaller inferred
ancestral genome sizes at earlier times even if actual genome sizes were constant. A
grid search of cost space showed genome size flux to be minimized at: CHGT =3 and
CDU P =2 (Figures 2.8A, 2.9).
Sensitivity analysis
We investigated the extent to which the high fraction of overall gene birth detected
during the Archean Gene Expansion was dependent on model parameters (Figure
2.8B). Gene birth patterns were invariant over a broad range of CDU P . Gene birth
from 2.8-3.4 Ga dissipated only at low CHGT values. However, this regime of CHGT
72
resulted in unrealistic genome size distributions: ancestral genomes were much smaller
than present day ones, and most genes were predicted to have been born on terminal
branches and spread via HGT.
2.2.6
Reference tree construction
Building a Chronogram of Life
We used a previously reported Tree of Life as the template for a reference chronogram
[90]. This template was constructed using a concatenation of 31 translation-related
orthologs. All of the species represented in our gene family dataset were present
in this template tree. Divergence times were estimated using PhyloBayes (v.2.3c)
[126]. Since autocorrelated molecular clock models have been shown to outperform
uncorrelated ones in some cases similar to this study [91], we ran PhyloBayes with
a CIR process model of rate correlation. Eight sets of temporal constraints that
could be directly linked to fossil or geochemical evidence were used and are displayed
in Figure 2.10. Benchmarking PhyloBayes runs in parallel (n=95) established that
predicted divergence times and model likelihood converged after a burn-in of roughly
1500 model cycles. Final divergence time estimates were estimated following a burnin of 2500 cycles, after which trees were sampled every 20 cycles until the 3500th
cycle.
2.2.7
Alternate reference trees
We tested the extent to which the AGE was sensitive to the topology of the reference
tree and to the molecular clock model used in chronogram construction. We built 10
separate reference phylogenies using non-parametric bootstrapping of the Ciccarelli et
al. gene alignment [90] (see Section 2.2.8 for PhyML parameters) and rooted each with
either the Bacteria, the Archaea, or the Eukarya as the outgroup. Unequivocal errors
in phylogeny that may be due to sequence alignment construction errors were observed
for the Bdellovibrio, Shigella, Treponema, and Helicobacter pylori taxa; these were
resolved by manual pruning and re-grafting. Each phylogeny was then converted to an
73
ultrametric tree using r8s (v.1.71) [116] under a penalized likelihood model (with an
additive penalty function, truncated Newton nonlinear optimization, cross-validation
enabled, a cross validation start value of 10, a cross validation smoothing increment
of 3, and the number of smoothing values tried set to 4). The same set of temporal
constraints was used as for PhyloBayes. For the purposes of computational economy,
a subset of 250 COGs was randomly selected from our dataset and reconciled against
each of the 10 alternate chronograms.
Predicted birth ages were robust to the usage of alternative chronograms. The
median gene family birth date difference between any two alternative chronograms
is 0.09 Ga (Fig. 2.11) Elevated rates of gene birth during the Late Archean were
observed in all 30 of the alternative chronograms and on average, 19% of the 250
chosen COGs were predicted to be born during a 200-My window (Fig. 2.12). However, the timing of AGE-like window diverged from that reported by PhyloBayes
and spanned 2.7-2.5 Ga (compared to 3.3-2.9 Ga for PhyloBayes). Inspection of the
r8s chronograms suggests this temporal discrepancy may be related to differences in
dating the cyanobacteria under the two models. For both the r8s and PhyloBayes
chronograms, AGE-like events coincided with the relatively brief period during which
the major bacterial phyla, such as the Firmicutes and Proteobacteria, diverged from
the last bacterial common ancestor. This period of compressed cladogenesis predates
the appearance of the cyanobacteria by roughly 100-200 My in both models. Our
r8s analysis places the initial occurrence of the cyanobacteria at 2.5 Ga, which is
precisely the minimum age constraint for the appearance of this clade (see Section
2.2.6). Using the same constraints, PhyloBayes predicts the cyanobacteria to have
emerged 3.0 Ga. We confirmed the importance of the cyanobacteria in dating the
AGE by reducing the minimum constrained age of this clade during r8s chronogram
construction; the resultant model yielded a younger AGE (data not shown).
2.2.8
Gene tree construction
Families of orthologous genes used in this study are based upon functionally annotated orthologous groups from the COG database [127], as extended to a wider set
74
of genomes in the eggNOG database [106]. Due to computational limitations, we restricted this study to a subset of 100 of these genomes (11 eukaryotic, 12 archaeal, and
67 bacterial) broadly distributed across the Tree of Life. Sequences were downloaded
from the eggNOG database in September of 2008.
eggNOG-derived families were filtered to ensure usable levels of sequence conservation with the aim of excluding the most error-prone phylogenies. We performed
this filtering in an iterative fashion: First, we excised poorly aligned regions of sequence [90, 128], using Gblocks (0.91b) [129] with the minimum number of sequences
for a flank position set to half the number of sequences in the alignment, the maximum number of contiguous non-conserved positions set to 8, the minimum length of
a block set to 2, and the allowed gap positions set to all. Second, we excluded genes
with more than 20% of their sequence in these excised regions from each gene family.
Third, Muscle (v3.7) [130] was used with default settings to realign the remaining sequences. This process then returned to the first step, unless no sequences or regions
were removed in the first or second steps in which case the process terminated. Of
the original 4872 COGs, 788 lost more than 25% of their original gene copies during
this process; these COGs were considered likely to be error-prone and thus excluded
from further analysis. Another 101 COGs were not analyzed due to their high gene
copy numbers and the extreme computational demands of running AnGST on those
large families. A distribution of gene copy numbers within each gene family is shown
in Figure 2.13.
Phylogenetic trees were constructed for the remaining gene families using version 2.4.5 of PhyML [72] and the following parameters: 100 bootstrap trees, a JTT
substitution model, 0.0 percentage of the sites were invariable, 4 substitution rate
categories, a gamma distribution parameter of 1.0, a BIONJ-based starting tree, and
both tree topology and branch length optimization were enabled.
75
Figure 2.4: Example of a basic reconciliation. An AnGST reconciliation of two
simple, but discordant, gene (G) and species (S) trees is shown. The mapping of leaves of
G to S: g1 :sA , g2 :sC , g3 :sB is indicated with color (e.g., g1 and sA are both shown in blue).
Reconciliation proceeds in a post-fix manner through the gene tree, first evaluating possible
mappings from g4 to nodes in the S. Once the reconciliation process is completed at g4 ,
the algorithm continues at g5 . A detailed explanation of this reconciliation is provided in
Section 2.2.2.
76
10
AnGST
BIONJ
PhyML
RF Score
8
6
4
2
0
−20
−10
0
! log likelihood
10
Figure 2.5: AnGST trees are more accurate than likelihood trees in simulation studies.
We simulated the evolution of sequence data using 225 randomly generated gene trees
with more than 10 leaves. Gene trees were reconstructed from synthetic sequence data
using either BIONJ (red), PhyML (black), or AnGST (blue). Phylogenetic accuracy was
evaluated by Robinson-Foulds (RF) score. A 0 RF score indicates perfect concordance
(all bipartitions of the candidate and reference tree are identical) and increasing RF scores
denote higher phylogenetic discordance.The logarithm of sequence likelihood given each tree
model, relative to the likelihood calculated with the true gene topology, is plotted on the X
axis. Mean RF scores and relative log likelihoods are drawn with rectangles whose height
and width reflect standard errors of the mean; protruding lines are standard deviations.
PhyML-based trees enjoy significantly higher likelihood scores than the AnGST chimeric
trees (p = 2.7×10−39 Wilcoxon rank sum test), but the AnGST-based trees are significantly
more similar to the correct gene tree topologies (p = 1.8 × 10−2 Wilcoxon rank sum test).
Outlying points beyond axes were not drawn to facilitate viewing mean values, but were
included in mean and significance estimations.
77
Figure 2.6: Amalgamation algorithm for phylogenetic uncertainty. An AnGST
reconciliation of four gene tree bootstrap topologies {G1 , G2 , G3 , and G4 } and species tree
S is shown. Leaf nodes on each bootstrap map to leaves on S according to color. The
reconciliation begins on one of the bootstrap trees, G1 (Step 1) and proceeds to an interior
node (Step 2). The reconciliation does not consider other topologies for this subtree, as it
only contains two leaves. When the reconciliation reaches the parent node node g1 (Step 3),
AnGST considers subtrees from other bootstraps with alternative topologies (but identical
leaves). Corresponding subtrees are found on G3 and G4 and rooted at nodes g3 and g4
respectively (Step 4). Reconciliations are performed in parallel at g1 , g3 , and g4 . For the
mapping of these internal nodes to lineage A on the species tree, the reconciliation at g3 is
optimal (since its topology matches the reference one) and the corresponding subtree in G3
is substituted for the mappings g1 :sA and g4 :sA .
78
0
A
−0.5
Inferred Birthdate [Ga]
−1
−1.5
−2
−2.5
−3
−3.5
−4
−4
−3.5
−3
−2.5
−2
−1.5
Simulated Birthdate [Ga]
−3.5
−3
−2.5
−1
−0.5
0
2.5
B
Birth bias ratio
2
1.5
1
0.5
0
−4
−2
Birthdate [Ga]
−1.5
−1
−0.5
0
Figure 2.7: Benchmarking AnGST inference accuracy. A) A scatter plot of simulated gene family birth dates and inferred birth dates. Points drawn signify midpoints of
branches associated with birth events. A slight amount of Gaussian noise with distribution
N (µ=0, σ=0.025) has been added to each point so that overlapping points can be distinguished. The correlation coefficient is 0.88 and 76% of predicted births are within 250 My
(bounded by red dashed lines) of their true ages. B) Birth prediction bias is plotted as a
function of time. Predicted births have been normalized by the number of simulated births
associated with a given age. The AGE (2.9-3.3 Ga) is highlighted in pink.
79
Genome size flux sensitivity to HGT and DUP costs
35
30
30
25
15
20
Genome size flux
25
20
10
5
0
15
5
A
10
6
7
8
CDUP
2
3
4
5
0
1
10
CHGT
Gene birth spike sensitivity to HGT and DUP costs
0.35
0.4
0.3
0.25
0.25
0.2
0.15
0.2
0.1
0.05
0.15
0
10
8
B
0.1
Fraction of all COGs born from 2.8−3.4 Ga
0.3
0.35
6
0.05
4
2
CDUP
0
0
1
2
4
3
5
6
7
8
9
C
HGT
Figure 2.8: AnGST parameter learning and sensitivity analysis. A) We performed
a grid search over the costs CHGT and CDU P with the intention of minimizing average
genome size flux between inferred ancestral genomes. The costs CLOS and CSP C were fixed
at 1.0 and 0.0, respectively. Flux can clearly be minimized along the CHGT axis, but is
less sensitive to changes in CDU P . A minimum point does exist, however, at CHGT =3.0,
CDU P =2.0. B) A sensitivity analysis for our detection of a high fraction of births from 2.83.4 Ga was performed over the same parameter space evaluated in A). Comparable fractions
of overall gene birth to the AGE were detected in parameter space near the genome size
flux minimum. Note that axes are reversed in panels A and B in order to facilitate viewing
parameter sensitivity landscapes.
80
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Figure 2.9: Inferred ancient genome sizes for CHGT =3.0, CDU P =2.0. Circle areas
scale absolutely with genome sizes. Our optimization metric, genome size flux, aims to
minimize the average difference between parent and child genomes. Ancestral genomes are
predicted to be smaller than modern day ones; this may reflect the evolution of increasingly
complex genomes, and/or the extinction of ancestral gene families. Genome sizes for the
LUCA (183 genes) and the modern-day genome of E. coli (2507 genes) are labeled in dark
blue. Metazoan genomes appear only slightly larger than prokaryotic ones because the
COGs used in this study were originally defined using only unicellular organismsTatusov
2000, which thus biased our analyses of eukaryotic genomes towards only microbially-related
genes.
81
10
Color ranges:
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Buchnera diverge
from Wigglesworthia
> 160 Ma
Figure 2.10: Temporal constraints. Eight fossil and biogeochemical constraints were
used to constrain the chronogram (evidence cited can be found in Table 2.1). Those constraints are overlaid onto the reference phylogeny.
82
2010-03-03429A-Z
4
Gene family birth using topology B [Ga]
3.5
3
2.5
2
1.5
1
0.5
0
0
0.5
1
1.5
2
2.5
Gene family birth using topology A [Ga]
3
3.5
4
Supplementary
Figure
8: Sensitivity
of predicted
birth
to variation
in reference
tree
Figure 2.11:
Sensitivity
of predicted
birth ages
to ages
variation
in reference
tree
topology. Ten bootstraps of the reference tree were rooted using either the Bacteria,
Archaea,
or Eukaryaof
asthe
an reference
outgroup and
with r8s,
producing Archaea,
30
topology.
Ten bootstraps
treesubsequently
were rootedprocessed
using either
the Bacteria,
or
alternative reference chronograms. Birth dates were inferred for 250 gene families using
each chronogram. We graph 1000 random combinations of alternative chronogram pairs
Eukaryaand
as an
outgroup
andthesubsequently
processed
with r8s,
producing
30 alternative
gene
families in
scatter plot above
(correlation
coefficient
= 0.86).
The medianreference
gene family birth date difference between any two alternative chronograms is 0.09 Ga.
chronograms. Birth dates were inferred for 250 gene families using each chronogram. We graph
1000 random combinations of alternative chronogram pairs and gene families in the scatter plot
above (correlation coefficient = 0.86). The median gene family birth date difference between
any two alternative chronograms is 0.09 Ga.
83
2010-03-03429A-Z
1
0.9
Existing COG fraction
0.8
Overall average CDF
Average CDF w/ euk. outgroup
Average CDF w/ bac. outgroup
Average CDF w/ arc. outgroup
Individual chronogram CDF
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
4.0
−3.5
3.5
−3
3.0
−2.5
2.5
−2
2.0
−1.5
1.5
−1
1.0
−0.5
0.5
0.0
Time [Ga]
Figure 2.12: Gene family birth using 30 alternative reference tree topologies.
Shown above
are cumulative
functions
of total COG
birth over tree
time for
Supplementary
Figure
9: Genedistribution
family birth
using(CDFs)
30 alternative
reference
topologies.
the 30 alternative reference chronograms (light gray lines). Mean CDFs for the Bacteria,
Archaea, and Eukarya as outgroups are shown using green, red, and blue dashed lines,
Shown above
are cumulative
distribution
ofwitnesses
total COG
birth
overbirth
time for the
respectively.
Overall (solid
black line), functions
the period (CDFs)
2.7-2.5 Ga
a gene
family
spike of on average 0.23 families born per 1 Ma and accounts for the birth of 19% of the
COG families
studied.
By contrast,
birthgray
rateslines).
average Mean
0.07 families
per 1Bacteria,
Ma from Archaea,
30 alternative
reference
chronograms
(light
CDFsborn
for the
2.5 Ga-present day.
and Eukarya as outgroups are shown using green, red, and blue dashed lines, respectively.
Overall (solid black line), the period 2.7-2.5 Ga witnesses a gene family birth spike of on
average 0.23 families born per 1 Ma and accounts for the birth of 19% of the COG families
studied. By contrast, birth rates average 0.07 families born per 1 Ma from 2.5 Ga-present day.
84
1400
1200
Number of gene families
1000
800
600
400
200
0
0
50
100
150
200
Gene family size
250
300
350
400
Figure 2.13: Histogram of COG family sizes. The median COG family in our dataset
possesses 18 gene copies, and 93% of COG families have 100 or fewer gene copies.
85
O2−binding COG fraction
0.1
0.05
0
4.0
**
3.5
3.0
Time [Ga]
2.5
2.0
Figure 2.14: O2 -utilizing gene birth over time. The fraction of compound-binding
COG births which bind O2 is shown over time. A chi-square test was used to compare
the overall number of COGs born and the number of O2-binding COGs born in 100 My
windows: prior to the AGE (3.7 Ga), at the height of the AGE (3.25 Ga), and at the tail of
the AGE (2.85 Ga). Comparisons with p < 0.05 are denoted with asterisks on the graph.
These data suggest that changes in O2 usage came toward the end of the AGE.
86
120
LCA age > 2.8 Ga
LCA age <= 2.8 Ga
100
y = 0.303x
HGT events
80
60
40
20
0
y = 0.085x
0
50
100
150
200
Gene family size
250
300
350
400
Figure 2.15: HGT counts vs. gene family size. The average gene family reconciliation
yields 9.7 inferred HGT events. The number of HGT events inferred grows with the number
of gene copies in a COG family. Gene family HGT counts also grow with the age of the last
common ancestor of all genomes represented in the family, suggesting that HGT is more
frequent among gene families spanning wider phyletic range. We note that y-intercepts for
the above line fittings have been forced to equal 0.
87
2010-03-03429A-Z
Thermoplas.acido
Archaeoglo.fulgi
Halobacter.sp.
Methanosar.aceti
1.8-1.25
Methanocal.janna
1.27-0.83
Methanopyr.kandl
2.33-2.05
1.05-0.62
1.62-1.04
Methanothe.therm
Pyrococcus.furio
Nanoarchae.equit
Aeropyrum.perni
2.01-1.65
2.82-2.67
1.56-1.0
Sulfolobus.solfa
1.77-1.37
Pyrobaculu.aerop
Giardia.lambl
Plasmodium.falci
2.08-1.77
1.26-0.82
Cryptospor.homin
1.77-1.37
Arabidopsi.thali
1.61-1.24
Saccharomy.cerev
1.4-1.04
Caenorhabd.elega
Drosophila.melan
1.1-0.82
Danio.rerio
0.97-0.7
Homo.sapie
0.57-0.37 0.19-0.1
Pan.trogl
0.34-0.21
Mus.muscu
Thermoanae.tengc
1.95-1.57
Clostridiu.tetan
Onion.yello
2.03-1.51
Mycoplasma.genit
3.15-2.87
0.72-0.35
Mycoplasma.galli
Staphyloco.aureu
2.54-2.17
1.6-1.05
Listeria.innoc
1.19-0.71
Bacillus.anthr
0.74-0.27
Bacillus.subti
0.9-0.41
1.9-1.36
Oceanobaci.iheye
Lactobacil.plant
1.26-0.77
Enterococc.faeca
0.95-0.57
0.44-0.21Lactococcu.lacti
Streptococ.pneum
Desulfovib.vulga
2.64-2.36
Bdellovibr.bacte
2.51-2.33
Geobacter.sulfu
2.97-2.78
Solibacter.usita
1.96-1.31
Acidobacte.sp.
Wolinella.succi
1.42-1.28
Helicobact.pylor
1.4-1.26
1.89-1.71
3.14-2.93
Helicobact.hepat
Campylobac.jejun
Wolbachia.sp.
2.14-1.14
Rickettsia.prowa
2.65-2.23
Caulobacte.cresc
3.03-2.84
Rhodopseud.palus
2.02-1.25
0.61-0.17 Bradyrhizo.japon
1.53-0.7
Mesorhizob.loti
3.39-3.21
0.82-0.27
Brucella.abort
0.99-0.35
2.94-2.73
0.6-0.16 Sinorhizob.melil
Agrobacter.tumef
Nitrosomon.europ
Bordetella.pertu
1.98-1.68
1.11-0.61
Ralstonia.solan
1.72-1.23
Chromobact.viola
1.07-0.63
2.5-2.21
Neisseria.menin
Coxiella.burne
3.29-3.09
Xanthomona.campe
2.34-2.08
0.8-0.41
Xylella.fasti
2.25-1.97
Pseudomona.aerug
Shewanella.oneid
1.94-1.73
Photobacte.profu
0.84-0.6
1.2-1.06
Vibrio.chole
Haemophilu.influ
1.09-0.95
Salmonella.enter
Escherichi.coli
1.0-0.86 0.67-0.6
0.66-0.59
0.83-0.74
Shigella.flexn
Yersinia.pesti
0.9-0.78
Wiggleswor.gloss
0.54-0.39
Buchnera.aphid
Thermotoga.marit
2.43-1.85
Aquifex.aeoli
3.0-2.75
Fusobacter.nucle
3.37-3.18
Thermus.therm
1.68-1.07
3.22-3.03
Deinococcu.radio
2.98-2.76
Dehalococc.ethen
3.14-2.96
Gloeobacte.viola
Synechococ.sp.
0.71-0.24
2.09-1.92
Prochloroc.marin
1.16-0.7
Prochloroc.marin
1.77-1.67
Synechococ.elong
1.54-1.5
Nostoc.sp.
0.58-0.19 Porphyromo.gingi
Bacteroide.theta
2.63-1.38
Chlorobium.tepid
3.14-2.42
Chlamydia.trach
0.88-0.23
Chlamydoph.pneum
Treponema.palli
2.7-2.19
3.29-3.05
Borrelia.burgd
2.76-2.3
Leptospira.inter
3.05-2.61
Rhodopirel.balti
3.18-2.76
Bifidobact.longu
1.36-0.72
Tropheryma.whipp
2.01-1.36
Mycobacter.tuber
0.87-0.47
Corynebact.gluta
1.62-1.06
Streptomyc.coeli
2.02-1.59
3.85-3.77
100
0.1
Ma
1.34-0.78
1.06-0.48
Supplementary Figure 13: Confidence intervals for divergence times on reference
Figure 2.16: Confidence intervals for divergence times on reference chronogram.
chronogram.
Confidence
intervals
(95%) wereby
estimated
by PhyloBayes
are shown
to
Confidence
intervals
(95%)
were estimated
PhyloBayes
and areand
shown
next next
to each
divergence point on the tree. Values are in units of Ga.
each divergence point on the tree. Values are in units of Ga.
30
88
Meta-function
Function
COG Code
Translation
Information storage &
processing
AGE birth
enrichment
Fraction of
pre-AGE
births
pre-AGE birth pre-AGE vs.
enrichment
AGE p-value
0.049
0.004
0.043
0.039
0.003
0.061
0.001
0.030
0.034
0.000
1.234
0.219
0.681
0.868
0.000
0.150
0.002
0.042
0.068
0.002
3.042
0.377
0.962
1.746
0.655
0.000
1.000
0.246
0.002
0.338
Cell cycle control
D
V
T
M
N
Z
U
O
56
29
106
141
82
5
122
157
0.014
0.007
0.027
0.035
0.021
0.001
0.031
0.039
0.013
0.008
0.028
0.050
0.031
0.000
0.032
0.043
0.903
1.083
1.044
1.424
1.493
0.000
1.047
1.101
0.012
0.001
0.020
0.060
0.015
0.000
0.022
0.042
0.854
0.097
0.762
1.702
0.718
0.000
0.710
1.070
1.000
0.033
0.405
0.487
0.064
1.000
0.274
1.000
C
G
Amino acid trans. & met.
E
Nucleotide trans. & met.
F
Coenzyme trans. & met.
H
Lipid trans. & met.
I
Inorganic ion trans. & met. P
Secondary metabolites
Q
211
186
226
83
155
72
182
70
0.053
0.047
0.057
0.021
0.039
0.018
0.046
0.018
0.079
0.056
0.079
0.026
0.056
0.024
0.058
0.015
1.499
1.205
1.394
1.228
1.439
1.306
1.276
0.847
0.068
0.051
0.109
0.059
0.069
0.032
0.046
0.004
1.289
1.087
1.923
2.835
1.772
1.795
0.998
0.216
0.490
0.730
0.054
0.001
0.326
0.334
0.298
0.045
Func. unknown
1186
560
0.298
0.141
0.183
0.142
0.615
1.010
0.071
0.113
0.238
0.804
0.000
0.105
Cell wall/membrane
Cell motility
Cytoskeleton
Intracell. trafficking
Post-trans. modification
Energy prod. & conv.
Carb. trans. & met.
Poorly characterized
Fraction of
AGE births
197
17
173
155
11
Signal transduction
Metabolism
Fraction
of genes
studied
J
A
Transcription
K
Replication, recombination L
Chromatin struct.
B
RNA proc.
Defense mech.
Cellular processes &
signaling
Number of
COGs
General func. pred.
S
R
Figure 2.17: Function of gene births prior to and during the AGE. Functional
enrichment of gene birth from 2.8-3.3 Ga is shown for the 20 COG functional categories. A
two-tailed Fisher exact test was used to compute the p-value of a difference in total COG
births prior to the AGE vs. during the AGE, for each functional category (last column).
89
Meta-function
Function
Translation
RNA proc.
Information storage
Transcription
& processing
Replication, recombination
Chromatin struct.
Cell cycle control
Defense mech.
Signal transduction
Cellular processes Cell wall/membrane
& signaling
Cell motility
Cytoskeleton
Intracell. trafficking
Post-trans. modification
Energy prod. & conv.
Carb. trans. & met.
Amino acid trans. & met.
Metabolism
Nucleotide trans. & met.
Coenzyme trans. & met.
Lipid trans. & met.
Inorganic ion trans. & met.
Secondary metabolites
Poorly
characterized
Func. unknown
General func. pred.
COG
Code
HGT out of
cyano into
plant
HGT out
of cyano
Fisher pvalue
HGT out of
HGT out of
alpha-proteos
alpha-proteos
into euks
J
A
K
L
B
38
0
2
4
0
1225
1
297
550
7
4.41E-05
1.00E+00
3.34E-01
1.52E-01
1.00E+00
13
0
0
0
0
779
0
231
324
8
9.34E-02
1.00E+00
1.78E-01
8.23E-02
1.00E+00
D
V
T
M
N
Z
U
O
3
0
2
5
0
0
6
11
164
116
341
735
80
0
273
549
7.42E-01
4.25E-01
2.54E-01
6.06E-02
6.36E-01
1.00E+00
3.20E-01
3.72E-01
0
0
3
0
0
0
1
9
98
59
209
513
168
0
305
367
6.28E-01
1.00E+00
4.84E-01
6.24E-03
4.23E-01
1.00E+00
3.79E-01
1.55E-02
C
G
E
F
H
I
P
Q
26
9
16
6
13
9
12
3
949
541
1205
486
1057
295
918
163
3.95E-03
7.21E-01
6.23E-01
8.49E-01
5.12E-01
5.06E-02
6.76E-01
7.41E-01
21
2
5
1
7
4
5
0
621
335
701
270
397
247
472
106
1.55E-06
5.86E-01
5.57E-01
5.32E-01
1.98E-01
3.33E-01
1.00E+00
6.30E-01
S
R
16
19
1601
1496
8.04E-02
4.34E-01
5
9
1103
797
3.73E-02
7.17E-01
Fisher pvalue
Figure 2.18: Biases in gene function associated with ancient endosymbioses.
Shown here is a functional breakdown of HGT from cyanobacteria into Arabidopsis thaliana,
and from the alpha-proteobacteria to ancient eukaryotes (which we defined as eukaryotic
lineages predating the divergence of Arabidopsis). The significance of functional enrichments
for genes associated with the chloroplast are calculated by comparing the observed number
of HGT from cyanobacteria into the plant lineage, to the total number of HGT originating
in the cyanobacteria, so as to account for functional biases associated with HGT out of the
cyanobacteria. Similar statistics are performed on mitochondria-related genes. P-values
that fall below a 5% False Discovery Rate cutoff in are underlined.
90
Chapter 3
Building prokaryotic species trees
from thousands of gene trees
Lawrence A. David, Albert Y. Wang, Eric J. Alm
Experiments in this chapter were performed in collaboration with Albert Wang in
the Alm laboratory.
Chapter 3
Building prokaryotic species trees
from thousands of gene trees
3.1
Abstract
Sequenced-based approaches for reconstructing prokaryotic species trees from more
than one gene utilize either the concatenation of sequences (supermatrices) or the
merger of separate gene trees (supertrees) [131]. Yet, supermatrices ignore differences in evolutionary rate and nucleotide compositional bias between concatenated
genes, which can ultimately reduce the accuracy of inferred phylogenetic trees [132].
Supertree methods can account for these inter-gene heterogeneities, but a commonlyused supertree technique, matrix representation, violates the site-independence assumption underlying many phylogenetic construction algorithms. Here, I extend an
alternative supertree method known as Gene Tree Parsimony (GTP), which chooses
the species tree as the topology that requires the least number of duplication events
to be inferred when compared to a set of gene trees [133]. My GTP model, named
GAnG, can account for horizontal gene transfer events (HGT) and is suitable for
use with prokaryotic gene trees. Preliminary GAnG analysis of 250 archaeal gene
trees built from a subset of the sequenced archaeal genomes supports hypotheses that
Nanoarchaeota diverged from the last ancestor of the Archaea prior to the Crenarchaeota/Euryarchaeota split, but also appears sensitive to HGT artifacts between the
92
Crenarchaeota and the Thermoplasma.
93
3.2
Introduction
Prokaryotic species phylogenies are frequently built by concatenating sequences from
more than one gene [131], in order to sample a range of phylogenetically-informative
characters [134], and to mitigate the role of topological artifacts caused by long branch
attraction [122, 135] and nucleotide composition bias [136, 137]. These genes can
be restricted to “core” sets that are topologically congruent [90, 138], under the
assumption that horizontal gene transfer (HGT) artifacts would only be present if
identical HGTs affected all of the genes in the core set. However, reconstructions of
organismal histories using core genes have been criticized for ignoring the majority
of phylogenetic signal present in genomes and have been referred to as “The tree[s]
of one percent” [139]. In order to claim that species trees represent overall genome
evolution, computational tools are required that can incorporate genetic information
from hundreds, or even thousands, of genes.
Previous studies that have built prokaryotic species trees from multiple gene families have utilized either “supertree” or “supermatrix” approaches [90, 138, 140, 141].
Under the supermatrix approach, sequences from orthologous gene families are concatenated into a single sequence, which can subsequently be inputted into a standard
phylogenetic construction algorithm. This method is robust to gene families with limited taxonomic distribution, as simulations have shown supermatrices to perform well
even in regimes where only 10% of species possess a given gene copy [142]. However,
the concatenation of prokaryotic genes risks assembly of sequences with divergent
evolutionary histories due to HGT [143]. Supermatrix approaches must therefore assume that the phylogenetic signal from vertically-descended genes overwhelms any
signal from transferred regions of the alignment. Moreover, species trees cannot be
constructed in a computationally-efficient way if differences in gene evolutionary rate
or nucleotide composition are taken into account. Simulations have shown that partitioning concatenations and fitting these parameters on a per-gene basis improved
the fidelity of reconstructed phylogenies in nearly all reported scenarios, and in some
cases enabled the reliable recovery of clades that were never inferred by a conven-
94
tional supermatrix procedure [132]. However, supermatrices that model evolutionary
heterogeneity between genes cannot utilize existing tree building algorithms and have
so far required exhaustively searching tree space in order to identify an optimal tree
[132, 134].
Supertree methods provide an alternative phylogenetic framework that can model
evolutionary parameters on a per-gene basis, while still constructing a species phylogeny in a computationally-efficient manner. Under this framework, gene trees are
built separately for each orthologous gene family and subsequently merged to form
a species tree. Supertree methods are distinguished by how gene tree merger takes
place. According to the most popular merger method [144], matrix representation
using parsimony (MRP), a binary character matrix is built that possesses a column
for each internal node in the set of gene trees, and a row for each of the species sampled. Species with a gene copy descended from a given internal node all share a “1”
in the corresponding column of the matrix, and all other species share a “0” [145]. A
consensus tree is formed by inputting the character matrix into a sequence-based phylogenetic construction algorithm. Because the supertree framework allows trees to be
built for individual gene families, the model is capable of accounting for differential
evolutionary rates and nucleotide compositions between genes, unlike conventional
supermatrices.
However, the MRP algorithm in particular has been criticized for its analogy
between the tree matrix and a nucleic acid sequence matrix [133]. This analogy
violates the site-independence model assumption of many phylogenetic reconstruction
algorithms [76], since sibling leaves on a gene tree will be partitioned similarly for
each MRP matrix column that corresponds to one of these leaves’ ancestral nodes.
In practice, this site dependence should manifest as a supertree bias towards subtree
topologies from large gene trees with many ancestral nodes and therefore more sites
in the tree matrix. This bias is likely deleterious, as large gene trees can be caused
by frequent HGT and duplication events that impede partitioning gene families into
orthologous groups.
Gene tree parsimony (GTP) provides an alternative supertree merger criterion
95
that has received less statistical criticism than MRP, but that also cannot be used
on prokaryotic gene trees in present implementations. First introduced by Slowinski
in 1997, the GTP approach chooses the species tree as the topology that requires
the least number of duplication events to be inferred when species and gene trees are
compared (a step called a reconciliation) [146]. This reconciliation-based approach
avoids the matrix construction step that has led to criticism of MRP supertree methods. However, existing implementations of GTP can only model gene duplication and
gene loss events [147, 148], limiting their usage to eukaryotic phylogenies.
Here, I enable the use of GTP supertrees on prokaryotic trees, through a new
program that I have named GAnG. This algorithm reconciles gene trees and species
trees using a generalized parsimony model I previously developed, the Analyzer of
Gene & Species Trees, or AnGST [149], which accounts for HGTs, as well as gene
duplication and gene loss events, in gene family evolution. Experiments with simulated data show that GAnG can accurately quantify species tree accuracy. As a proof
of concept, I also used the algorithm to learn a phylogeny of 12 sequenced archaeal
species from 250 gene trees with divergent topologies and that have likely undergone
HGT. Results of this analysis support the basal position of Nanoarchaeum equitans
on the archaeal tree.
96
3.3
Methods
3.3.1
Approach overview
The GAnG algorithm is an iterative process composed of four primary steps:
1. Initial topology generation and reconciliation: An initial reference tree
is constructed using one gene, or a concatenation of multiple genes. Individual
gene trees are also constructed for all orthologous gene families. After trees
have been built, each of the gene trees is reconciled against the species tree
using AnGST.
2. Proposal of tree refinements: The HGTs inferred from reconciliations between the species tree and gene trees are mined to propose subtree-prune-andregraft (SPR) moves. These SPRs are used to generate candidate species tree
topologies (Section 3.3.2). Alternatively, new rootings of the species tree can
be evaluated (Section 3.3.3).
3. Evaluation of candidate refinements: Each candidate species tree is evaluated by comparison to the set of gene trees using AnGST, as described in
Section 3.3.4.
4. Selection of a new species tree: The best scoring candidate tree is identified.
If this tree has a better reconciliation score than the current species tree, the
species tree is replaced with the candidate tree and the algorithm returns to
Step 2. Otherwise, the process terminates and returns the present species tree.
3.3.2
Generating candidate species trees
GAnG searches species tree space using an iterative subtree-prune-and-regraft (SPR)
strategy that generates a candidate species trees from an existing species tree by
pruning off subtrees and regrafting them onto new locations on the tree. This heuristic
approach is necessary because it is unlikely that a polynomial-time algorithm exists
for finding an optimal species tree using AnGST [150]. An exhaustive search strategy
97
is also not possible, since the number of possible rooted binary trees grows superexponentially with the number of species s, following the function (2s − 3)!! [151] (for
the 14 genomes in Fig. 3.2, there are nearly eight trillion possible organismal trees).
SPR moves enable dramatic changes in tree topology using relatively few topological operations and are used by tree construction algorithms like PhyML 3.0 [152],
FastTree [153] and RaxML [154] to avoid being caught in local minima in tree space.
An SPR-based strategy for refining species trees has the added advantage of complementing AnGST reconciliation process. High-frequency HGTs can be eliminated
if an SPR between the transferred nodes (in the reverse direction of the HGT) is
performed on the species tree. Consequently, GAnG generates a candidate species
tree for each of the SPR moves associated with the 100 most frequently inferred HGT
on the present species tree.
3.3.3
Rooting the species tree
Unlike typical supermatrix or supertree methods, GAnG does not require an outgroup
sequence to to infer a rooted species tree. Since the species tree root orients the
direction of vertical inheritance at internal nodes, alternative rootings of the same
unrooted species tree will have distinct AnGST scores when reconciled against a
gene tree. Therefore, in addition to incorporating SPR moves, the candidate tree
generation routine can also vary the position of the root node on the species tree.
3.3.4
Scoring a candidate species tree
A score S for a putative species tree topology T quantifies how well the species tree
fits the set of gene trees according to the AnGST model:
S(T ) =
�
Rec(G|T )
(3.1)
G
where Rec(G|T ) is the AnGST reconciliation score for a gene tree G, given the candidate species tree T . The AnGST model assigns a score of 3, 2, and 1 to each HGT,
duplication, and loss event inferred, respectively [149].
98
3.4
Preliminary results
I have performed two preliminary tests using the GAnG algorithm, using simulated
and real-world data generated from my previous study of microbial genome evolution
[149]. These tests suggest:
1. The AnGST-based scoring function can discern between species trees of varying
accuracy (Section 3.4.1)
2. HGTs can be used to propose SPR moves on the species tree that lead to lower
AnGST reconciliation scores (Section 3.4.2).
3.4.1
AnGST scores increase with true species tree permutation
I evaluated how AnGST reconciliation scores changed as increasing phylogenetic noise
was added to the Tree of Life [90], in order to test with real-world gene trees if the
GAnG tree search process could be attracted towards the correct species tree. This
experiment used 125 microbial gene families randomly selected from my previous
study of microbial genome evolution [149]. A total of 100 sequenced genomes spanning
all three domains of life were represented in these gene trees. Because the true species
tree for the 100 sampled genomes is not known, I could not directly evaluate whether
or not the GAnG algorithm could use the gene trees to recover the true species tree.
Instead, I tested if the GAnG algorithm behaved in a manner consistent with less
accurate species trees receiving higher reconciliation scores than more accurate species
trees. I simulated a continuum of species tree accuracy by taking a topology (the Tree
of Life) likely similar to the true tree, and randomly permuting it with between 1 and
10 SPR random moves. This procedure was used to generate 300 alternate species
trees. Gene tree reconciliation scores against the species trees increased linearly with
the number of SPRs performed on the Tree of Life (Figure 3.1; R2 = 0.67), suggesting
that more accurate species trees will receive lower reconciliation scores than less
accurate species trees.
99
3.4.2
HGTs can be used to find a better-scoring tree
The GAnG algorithm was run to completion on a set of 12 sequenced archaeal species
and 250 gene trees randomly chosen from my previous study on microbial genome
evolution [149]. The initial species tree topology was pruned from the Ciccarelli &
Bork Tree of Life (Fig. 3.2A) [90]. The algorithm terminated after four iterations,
yielding a refined species tree whose reconciliations with gene trees were an average of
3.7% lower than the initial tree (Figure 3.2B). A single SPR move of the Crenarchaeota
to a basal euryarchaeal lineage provided the most dramatic change in the refined
topology, shifting N. equitans to a basal position on the archaeal tree.
100
3.5
Discussion
GAnG employs the AnGST reconciliation model to become the first GTP-based supertree method capable of accounting for HGT events and therefore appropriate for
inferring prokaryotic species trees. Usage of the AnGST model carries two additional
benefits. First, the AnGST algorithm includes a bootstrap amalgamation step that
constructs a chimeric gene tree from the bootstrap subtrees that best conform to the
species tree. As a result, gene families that evolved by vertical descent, but whose
phylogenetic reconstructions are sensitive to sequence sampling variation, will be less
likely to mislead the GTP process. Second, HGT events inferred by AnGST are
directed between specific branches on the species tree and can therefore provide a
heuristic guide for refining species trees. Frequent HGTs between two lineages may
be the result of a topological error on the species tree and can be resolved by making
these lineages sibling to one another on an updated tree.
Preliminary analyses of GAnG on simulated data demonstrate that the algorithm’s
scoring function will be attracted towards correct species topologies (Fig. 3.1). The
observed linear relationship between species tree score and tree randomization demonstrates also suggests that GAnG can accurately quantify relative differences in inaccuracy among trees. Thus, future implementations of GAnG’s tree search algorithm
may be able to employ more sophisticated search algorithms, such as gradient descent.
The GAnG analysis on real-world data showed the algorithm capable of inferring
a prokaryotic species tree largely in line with prior archaeal phylogenies, despite not
relying on a “core” set of gene families free of HGT (Fig. 3.2). A single SPR move of
the Crenarchaeota to a basal euryarchaeal lineage provided the most dramatic refinement to the starting Ciccarelli & Bork archaeal tree. This SPR shifted N. equitans
from its initial sibling position with the Crenarchaeota (a position not supported by
the archaeal phylogenetic literature) to a more basal position outgrouping both the
Euryarchaeota and the Crenarchaeota. This outgroup location of N. equitans has
been reported previously [155], but remains controversial [156]. The refined archaeal
tree also includes a paraphyletic euryarchaeal clade that features the Thermoplas-
101
matales and Crenarchaeota as sibling to one another. A close relationship between
the Thermoplasmatales and the Crenarchaeota has been reported by previous phylogenetic studies [157–159] and has been attributed to HGT between thermoplasma
species and the Crenarchaeota [138].
Finally, these experiments both indicate that GAnG could be run on datasets
composed of thousands of gene trees. The analysis of the archaeal tree with 250 gene
trees took on the order of 3 days on a computer cluster. GAnG running time increases
linearly with the number of gene trees, so analysis of a 1000 gene tree set would
require approximately two weeks of compute time – a time-consuming experiment,
but not prohibitively long. Moreover, a potential running time speedup is currently
in development (see Future Directions below).
102
3.6
Future Directions
Future studies will investigate several potential improvements to the GAnG model,
specifically in species tree scoring, running time improvement, and gene tree weighting. The benefits of each of these model changes will be investigated using gene and
species tree simulation software that I have previously written during the development
of AnGST [149].
A more accurate and more efficient version of GAnG will also be tested on a
much larger archaeal dataset of 9053 gene families drawn from 70 sequenced archaeal
genomes [160]. The resulting archaeal tree will be used to test hypotheses involving
the basal position of the Korarchaeota and Thaumarchaeota on archaeal phylogenies
and whether N. equitans represents a separate archaeal phylum.
3.6.1
Model improvements
Scoring modifications
The scoring function Eq. 3.1 may be biased by larger gene trees, which are likely to
have a higher variance in reconciliation scores. An alternative scoring function robust
to this bias is:
S(T ) =
�
G
Sign(Rec(G|T ) − Rec(G|R))
(3.2)
This score is negative if there are more gene trees whose reconciliation scores are lower
with the candidate tree than with the present species tree R. In this case, a candidate
species tree would be accepted and become the new present species tree. Evidence
that S(T ) will be positive for incorrect candidate trees is presented in Section 3.4.1
of the Preliminary Results. Summary of a tree’s fitness using the sign function also
facilitates an algorithmic speedup described below in Reducing running time.
Alternatively, if we assume that reconciliation score variance grows with reconciliation score, exceptional changes in reconciliation score can be captured by the scoring
103
metric:
S(T ) =
�
G
Log
�
Rec(G|T ) − Rec(G|R)
Rec(G|R)
�
(3.3)
In contrast to Equation 3.1, however, this scoring function may be biased by smaller
gene trees, which are likely to have smaller reconciliation scores.
Reducing running time
I can avoid reconciling the entire set of gene trees if it can be quickly determined
that a candidate species tree is less fit than the current species tree. I can perform
this speedup using the scoring function in Equation 3.2 and by assuming that gene
trees evolve independently from one another. Under this model, determining the sign
of S(T ) can be likened to the problem of determining if a coin is fair using a finite
number of coin tosses. To compute if there is a bias towards [Rec(G|T ) − Rec(G|R)]
being positive or negative, we can count the total number of G for which this value is
negative, N− , or non-zero, N , and estimate the probability that a candidate species
tree that changes the reconciliation score of G causes a lower reconciliation score:
p=
N−
N
(3.4)
However, p is an estimated probability with a standard error sp
sp =
�
p(1 − p)
N
(3.5)
and a maximum error of
E = Z × sp
(3.6)
where Z is taken from a table of Z-values and associated confidence levels, calculated
using a normal distribution [75].
If p − E > 0.5, the candidate topology can be accepted at the chosen confidence
level. Similarly, if p + E > 0.5, the candidate topology can be rejected. Otherwise,
the maximum error is too high to determine if the tree should be accepted or rejected
104
and more gene trees need to be reconciled. One way to determine the additional
number of reconciliations to perform in this case would be to calculate E = |0.5 − p|,
or the maximum error associated with p so that it can be determined if p is positive
or negative. It can be shown that the number of reconciliations necessary to achieve
this error, NE , is equal to:
NE =
Z 2 p(1 − p)
E2
(3.7)
Gene tree weighting
Core gene approaches to species tree construction usually exclude gene families that
show evidence of HGT [90]. However, the strict exclusion of gene families that carry
only weak signals of HGT may be overly conservative. A single HGT event causes
only one bifurcation on a gene tree to not be explained by vertical inheritance (i.e.
a speciation event). A large gene tree with relatively few HGT can thus still be
informative for phylogenomic purposes. This intuition can be incorporated into a
scoring function by multiplying each gene tree score by a weighting factor w(G). One
method for calculating this weight would be to take the ratio of the number of HGT
possible given a gene family’s taxonomic distribution (which can be approximated by
the square of the number of species L represented in the gene family), to the number
of HGT inferred on the gene tree, HGT(G):
w(G) =
L(G)2
HGT(G)
(3.8)
Translation-related gene families, which have been previously been relied upon for
constructing archaeal phylogenies [138], are weighted more highly according to this
scheme than the average gene family (p < 10−30 , Rank Sum test; Fig. 3.3). A caveat
to this weighting scheme, however, is that HGT(G) will change as the species tree is
refined by GAnG.
I will also evaluate via simulation an alternative gene tree weighting function that
uses gene tree branch lengths to downweight the influence of gene families suspected of
105
bearing transferred or duplicated genes. This approach relies on the assumption that
genetic distances between gene copies descended from an HGT event will be shorter
than expected, whereas genetic distances between certain gene copies descended from
a duplication/loss scenarios will be longer than expected (see Figure 3.4 for an illustration of these phenomena). The following weighting function accounts for this
evidence of non-vertical descent:
w(G) = 1/ min
r
�
si ,sj
(rD(si , sj |G) − D(si , sj |R))
(3.9)
This metric iterates over all pairs of species si and sj represented in a gene tree G,
and computes their pairwise distance on G using the function D. The difference
between this distance and the expected distance (taken from the reference tree R) is
then summed. To account for gene-specific rates of evolution, a rate term r is fit to
G using a minimization function.
3.6.2
A tree of all sequenced archaea
Once I have completed optimizing the GAnG algorithm, I will construct an archaeal
phylogeny using the arCOG dataset of 9504 archaeal gene families, which span 88%
of sequenced archaeal genomes [160]. Due in part to the challenges of cultivating
archaeal species [161], only 70 genomes have so far been sequenced from this domain
of life; 4 of these genomes are the sole representatives of 3 of the 5 known archaeal
phyla [155, 162–164] (Fig. 3.5). This uneven taxon sampling, along with suspected
HGT events and unequal rates of lineage evolution, makes resolution of deep nodes
on the archaeal phylogeny sensitive to the choice of analyzed genes. For example, a
phylogeny built from a core set of 27 large and 23 small ribosomal subunit proteins
supports a basal position of N. equitans on the archaeal tree, but the phylogeny of
just the small subunit proteins suggests this species may only be a rapidly-evolving
euryarchaeote [156].
An archaeal species tree built using the full arCOG dataset should correctly position N. equitans, unless phylogenetic artifacts systematically bias a large proportion
106
of genes in archaeal genomes. This tree may help resolve ongoing debates regarding
the origins of other archaeal phyla. Previous studies with varying sets of core genes
have placed the Korarchaeota either basal on the archaeal tree [165, 166], deep within
the Crenarchaeota [162], or sibling to the Euryarchaeota [167]. Core gene studies have
also reported conflicting positions for the Thaumarchaeota, positioning the phylum
either within the Crenarchaeota [162] or basal on the archaeal tree [167, 168]. The
GAnG algorithm offers a quantitative method for testing hypotheses of korarchaeal
and thaumarchaeal origins against thousands of gene trees. Lessons learned from resolving these questions in archaeal history may also prove insightful for future efforts
that use GAnG to reconstruct a larger three domain Tree of Life.
107
65
AnGST score
60
55
50
0
1
2
3
4
5
6
7
Degree of reference tree randomization [SPRs]
8
9
10
Figure 3.1: Average AnGST gene tree reconciliation scores as species tree randomization increases: Between 1-10 random SPR moves were made to 300 copies of the
Tree of Life [90], to produce a continuum of species tree accuracy. Each of the these species
trees was reconciled against a set of 125 gene trees and the average AnGST score for each
reconciliation is plotted on the y-axis. The R2 of the best fit line (shown in red) to these
data is 0.67.
108
njplottemp_2 Mon May 03 22:14:04 2010 Page 1 of 1
njplottemp_3 Mon May 03 22:14:21 2010 Page 1 of 1
5e+002
5e+002
3823.194
Escherichia coli
2713.764
3850.000
Ma
2221.716
Homo Sapiens
1500.290
Escherichia coli
Homo Sapiens
1751.248
Pyrobaculum aerophilum
Ma
Nanoarchaeum equitans
1628.284
303.570
1244.660
992.469
Sulfolobus solfataricus
369.573
Thermoplasma acidophilum
470.469
255.626
1244.660
1109.430
699.424
Aeropyrum pernix
758.779
1803.860
272.256
621.057
Nanoarchaeum equitans
Sulfolobus solfataricus
Aeropyrum pernix
78.368
621.057
1349.790
Pyrococcus furiosus
Pyrobaculum aerophilum
20.789
857.400
207.521
540.331
825.998
Methanothermobacter thermautotrophicus
Pyrococcus furiosus
235.052
857.400
257.340
247.419
1092.450
663.877
Methanopyrus kandleri
145.682
101.286
556.634
Methanocaldococcus jannaschii
Methanocaldococcus jannaschii
Methanothermobacter thermautotrophicus
107.244
760.385
556.634
60.834
Methanosarcina acetivorans
Methanopyrus kandleri
327.167
368.697
760.385
469.760
1087.550
647.947
Halobacterium sp.
117.217
535.540
Archaeoglobus fulgidus
Archaeoglobus fulgidus
Methanosarcina acetivorans
112.407
A
1804.730
535.540
B
Thermoplasma acidophilum
Halobacterium sp.
Figure 3.2: An archaeal phylogeny refined using 250 gene trees and HGTproposed SPRs: (A) Initial phylogeny of archaeal species pruned from the Ciccarelli &
Bork Tree of Life [90]. Crenarchaeal lineages are labeled in blue, euryarchaeal lineages are
labeled in red, and the nanoarchaeal lineage is labeled in green. The most dramatic accepted
SPR move on this topology is shown using a black arrow. (B) The resulting archaeal tree
after 4 iterations of tree refinement. The average AnGST gene tree reconciliation score
using this refined tree decreased 3.7% relative to the initial phylogeny.
109
100
All Genes
Translation Genes
90
80
70
HGT
60
50
40
30
20
10
0
0
1
2
3
4
5
Log(SpeciesCount2)
6
7
8
Figure 3.3: Inferred HGT as a function of the number of species represented
in a gene tree: Each of the 9053 arCOG gene trees was reconciled against an archaeal
species tree (Fig. 3.5). The number of inferred HGT for each gene family is plotted against
the square of the number of species represented in the gene family (a rough approximation
of the number of possible HGT) on a log-scale. The 201 gene families annotated by the
arCOG database as involved in translation are shown in red, and all other gene families
are shown in blue. Gene tree weights, as calculated by Eq. 3.8 are significantly higher for
translation-associated gene families (p < 10−30 , Rank Sum test).
110
Duplication
HGT
Gene Tree
Species Tree
T
D
L
L
L
L
A
T
B
C
D
A
B
L
L
C
D
E
E
F
F
A
A
C
C
B
B
D
D
E
E
F
F
Figure 3.4: Expected gene tree branch lengths following duplication or HGT:
Two hypothetical evolutionary histories are shown for a gene family. On the left, an ancestral duplication event (D), followed by four loss events (L), creates higher than expected
genetic distance between species A and B, and between species C and D. On the right,
HGT events (T) cause lower than expected genetic distance between species A and C, and
between species B and D.
111
Nitrosopumilus maritimus SCM1
Cenarchaeum symbiosum
Nanoarchaeum equitans
Candidatus Korarchaeum cryptofilum OPF8
Metallosphaera sedula DSM 5348
Sulfolobus islandicus Y.G.57.14
Sulfolobus islandicus L.S.2.15
Sulfolobus islandicus M.14.25
Sulfolobus islandicus M.16.27
Sulfolobus islandicus M.16.4
Sulfolobus solfataricus
Sulfolobus islandicus Y.N.15.51
Sulfolobus tokodaii
Sulfolobus acidocaldarius DSM 639
Staphylothermus marinus F1
Desulfurococcus kamchatkensis 1221n
Aeropyrum pernix
Ignicoccus hospitalis KIN4/I
Hyperthermus butylicus
Thermofilum pendens Hrk 5
Caldivirga maquilingensis IC-167
Pyrobaculum islandicum DSM 4184
Pyrobaculum aerophilum
Pyrobaculum calidifontis JCM 11548
Thermoproteus neutrophilus V24Sta
Pyrobaculum arsenaticum DSM 13514
Picrophilus torridus DSM 9790
Thermoplasma acidophilum
Thermoplasma volcanium
Thermococcus kodakarensis KOD1
Thermococcus onnurineus NA1
Thermococcus gammatolerans EJ3
Thermococcus sibiricus MM 739
Pyrococcus abyssi
Pyrococcus furiosus
Pyrococcus horikoshii
Methanopyrus kandleri
Methanocaldococcus fervens AG86
Methanocaldococcus jannaschii
Methanocaldococcus vulcanius M7
Methanococcus aeolicus Nankai-3
Methanococcus maripaludis S2
Methanococcus maripaludis C6
Methanococcus maripaludis C5
Methanococcus vannielii SB
Methanococcus maripaludis C7
Methanothermobacter thermautotrophicus
Methanosphaera stadtmanae
Methanobrevibacter smithii ATCC 35061
Archaeoglobus fulgidus
Methanosaeta thermophila PT
Methanococcoides burtonii DSM 6242
Methanosarcina barkeri str. Fusaro
Methanosarcina mazei
Methanosarcina acetivorans
uncultured methanogenic archaeon
Methanospirillum hungatei JF-1
Methanocorpusculum labreanum Z
Candidatus Methanoregula boonei 6A8
Methanosphaerula palustris E1-9c
Methanoculleus marisnigri JR1
Halorhabdus utahensis DSM 12940
Halorubrum lacusprofundi ATCC 49239
Natronomonas pharaonis
Halomicrobium mukohataei DSM 12286
Haloquadratum walsbyi
Haloarcula marismortui ATCC 43049
Halobacterium salinarum R1
Halobacterium sp.
0.6
Figure 3.5: Maximum likelihood tree of archaeal species: This tree of 70 archaeal
species will be the starting topology for the GAnG analysis of 9504 arCOG gene trees. The
tree was built using a maximum likelihood analysis of 53 concatenated ribosomal proteins
[138, 160].
112
Part III
Conclusion
113
This thesis contributes new algorithms for comparative microbial genomics, particularly in the subfields of microbial ecology and microbial genome evolution. In Chapter 1 of Part II, I presented the algorithm AdaptML, which can identify geneticallyand ecologically-distinct bacterial populations. I used this tool to identify clusters of
marine vibrio that appear to have differentiated in response to their nutrient preferences. Code for AdaptML has been made public and with the help of Albert Wang, an
undergraduate researcher in Eric Alm’s laboratory, I have created a webserver where
users can submit their own phylogenetic and ecological data for AdaptML to process online (http://almlab.mit.edu/adaptml/). Outside investigators have used these
tools to run AnGST on their own datasets. Fred Cohan’s group ran the algorithm to
find ecotypes in the genus Bacillus that would have been overlooked with traditional
sequence divergence-based thresholds for calling species [11]. Oakley and colleagues
used AdaptML to help confirm evidence for niche partitioning among sampled Desulfobulbus [169]. Other investigators have promoted using AdaptML for future studies
of microbial evolution and ecology. Daniel Falush has suggested using AdaptML to
infer the evolutionary history of microbe-host adaptation [170], and Ford Doolittle &
Olga Zhaxybayeva have pointed out the algorithm’s advantages over strict thresholdbased approaches to microbial ecology [171].
In Chapter 2, I introduced the algorithm AnGST, which reconciles an ultrametric
reference tree and a gene tree to infer HGT, gene duplication, and gene family birth
events in a chronological context. Implicit in these reconciliations are known constraints on organismal history drawn from paleontological and geochemical records.
Analysis of 100 sequenced genomes with AnGST produced evidence for a massive expansion of microbial genetic diversity during the Archean eon, as well as the gradual
oxygenation of the biosphere over the past 3 Ga. This later finding is in agreement
with other studies that suggest secular changes in earth geochemistry were recorded
in, and can now be mined from, microbial genomes [81, 83, 172, 173]. Further evidence
of the link between the AnGST analysis and biogeochemistry could be uncovered by
follow up studies on enzyme families whose first appearance can be dated using the
sedimentary record. For example, Form 1 RuBisCO makes specific contacts with
114
molecular oxygen and first appeared 2.7-2.9 Ga according to carbon isotope fractionation results [174]. AnGST’s predicted birth date for this enzyme’s small subunit,
between 2.0-3.0 Ga, is wide, but not incompatible with the fractionation results.
Identification and analysis of other biomarkers whose age can be independently verified by AnGST will lead to potentially fruitful collaborations between genomicists,
paleontologists, and biogeochemists.
Lastly, in Chapter 3, I introduced the supertree algorithm GAnG, which is capable
of constructing prokaryotic species trees from thousands of gene trees. Preliminary
GAnG analysis of 250 archaeal gene trees built from a subset of the sequenced archaeal genomes supports the hypothesis that Nanoarchaeota diverged from the last
ancestor of the Archaea prior to the Crenarchaeota/Euryarchaeota split, but also
appears sensitive to HGT from the Crenarchaeota to the Thermoplasma. Further
improvements to the GAnG scoring function and gene tree weighting scheme are ongoing. An improved version of GAnG will be used to build a species tree relating
70 sequenced Archaea from all five known phyla. One important observation during
that tree’s construction will be the topology of the species tree scoring landscape near
variations on deep node branching order. A relatively flat landscape in that regime
may suggest that the phyla radiated so rapidly during early archaeal evolution that
their branching order cannot be resolved [141] or that HGT dominated vertical descent during the formation of the archaeal phyla [175]. By contrast, a bowl-shaped
landscape with a clear scoring minimum will provide support that a Tree of Life exists
for the Archaea, and will encourage attempts to construct a universal Tree of Life
with sequenced genomes from all three domains of life.
115
Part IV
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116
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