JOURNAL OF BACTERIOLOGY, July 2007, p. 4578–4586
Copyright © 2007, American Society for Microbiology. All Rights Reserved.
Vol. 189, No. 13
A Robust Species Tree for the Alphaproteobacteria?†
Kelly P. Williams,* Bruno W. Sobral, and Allan W. Dickerman
Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061
Received 16 February 2007/Accepted 24 April 2007
The branching order and coherence of the alphaproteobacterial orders have not been well established, and
not all studies have agreed that mitochondria arose from within the Rickettsiales. A species tree for 72
alphaproteobacteria was produced from a concatenation of alignments for 104 well-behaved protein families.
Coherence was upheld for four of the five orders with current standing that were represented here by more than
one species. However, the family Hyphomonadaceae was split from the other Rhodobacterales, forming an
expanded group with Caulobacterales that also included Parvularcula. The three earliest-branching alphapro-
teobacterial orders were the Rickettsiales, followed by the Rhodospirillales and then the Sphingomonadales. The
principal uncertainty is whether the expanded Caulobacterales group is more closely associated with the
Rhodobacterales or the Rhizobiales. The mitochondrial branch was placed within the Rickettsiales as a sister to
the combined Anaplasmataceae and Rickettsiaceae, all subtended by the Pelagibacter branch. Pelagibacter genes
will serve as useful additions to the bacterial outgroup in future evolutionary studies of mitochondrial genes,
including those that have transferred to the eukaryotic nucleus.
The Alphaproteobacteria are a diverse class of organisms
within the phylum Proteobacteria, with many important biolog-
ical roles. They frequently adopt an intracellular lifestyle as
plant mutualists or plant or animal pathogens (5). This has led
to independent paths of genome reduction in several alpha-
proteobacterial lineages, but lineage-specific genome expan-
sions are also apparent, with some genomes divided among
multiple replicons that can include linear chromosomes (9).
The Alphaproteobacteria include the most abundant of marine
cellular organisms (20). A variety of metabolic strategies are
found in the class, including photosynthesis, nitrogen fixation,
ammonia oxidation, and methylotrophy. Stalked, stellate, and
spiral morphologies are found. Developmental programs occur
that switch between cell types, controlled by a web of regula-
tory systems (31).
Special interest attaches to the Alphaproteobacteria as the
ancestral group for mitochondria. The Rickettsiales are most
often cited as the alphaproteobacterial subgroup from which
mitochondria arose, but there has been disagreement on this
point (17, 19, 32). Proper placement of the mitochondrial
branch on the alphaproteobacterial species tree benefits the
study of eukaryotic nuclear genomes, which house many genes
of mitochondrial origin.
Improvement in phylogenetic tree reconstruction can come
from increasing the number of characters used, which whole-
genome sequences have provided in abundance. However,
even long character matrices can produce artifacts, for exam-
ple, when taxon sampling is limited. Recently, the number of
completely or nearly completely sequenced alphaproteobacte-
rial genomes has become large, allowing for the assembly of a
carefully selected character matrix that is both long and broad,
in turn enabling robust phylogenetic inference. With such a
matrix, we have generated a species tree for these bacteria,
into which we have placed the mitochondrial branch. The re-
liability of the tree is indicated not only by its high Bayesian
and bootstrap support values, which are generally very high for
such large matrices, but by the agreement of maximum-likeli-
hood (ML) and Bayesian methods, by convergence to this
same tree for subsets of the full protein set, and by its good
agreement with the highly supported bipartitions among the
MATERIALS AND METHODS
Organisms. We chose 72 alphaproteobacteria, 8 outgroup proteobacteria, and
8 mitochondria for study (see Table S1 in the supplemental material). This
included all alphaproteobacterial strains with a completed genome sequence, or
with an incomplete genome sequence in fewer than 100 contigs, that were
available with annotation at NCBI on 1 May 2006. Maricaulis maris was included
later due to special interest in the Hyphomonadaceae. Two strains, Agrobacterium
tumefaciens C58 and Ehrlichia ruminantium Welgevonden, had each been se-
quenced in two independent projects; in these cases, we used data only from the
project that had annotated the largest number of proteins (by the University of
Washington and CIRAD, respectively). Because a recent genome-based study
(13) including 63 proteobacteria showed that the sister group of the Alphapro-
teobacteria was the combined Betaproteobacteria and Gammaproteobacteria,
seven outgroup species with completed genomes were chosen from these two
classes, and an eighth outgroup species was selected from the Deltaproteobac-
teria, Geobacter sulfurreducens, which appeared to be the least derived of all
Proteobacteria in that study. We included eight mitochondrial genomes that have
been deemed primitive in various studies (summarized in reference 21) from two
alveolates, four streptophytes, and two chlorophytes.
Key programs for phylogenetic analysis. Unless otherwise stated, the follow-
ing programs were employed. MUSCLE (15) was used with up to 100 iterations
for sequence alignment. Gblocks (11) was used for masking gapped and other
noisy portions of the alignments, with a minimum block length of 10 amino acids
and gaps allowed in any alignment position for no more than half of the
sequences. We performed ML phylogenetic analysis with PHYML v2.4.4
primed with the BIONJ tree (22), using the Whelan and Golding (WAG)
amino acid substitution matrix, estimating all parameters, and using four
substitution rate categories. We performed Bayesian phylogenetic analysis
using MrBayes v3.1.2 (28) in model-jumping mode with a single chain and
primed with the BIONJ tree, assessing burn-in (arrival at a likelihood pla-
teau) as described previously (7).
* Corresponding author. Mailing address: Virginia Bioinformatics
Institute, Virginia Polytechnic Institute and State University, Blacks-
burg, VA 24061. Phone: (540) 231-7121. Fax: (540) 231-2606. E-mail:
† Supplemental material for this article may be found at http://jb
?Published ahead of print on 4 May 2007.
rRNA gene sequences and trees. The 16S and 23S rRNA gene sequences of the
bacteria under study were identified using author annotations or homology
searching and were trimmed or extended so that their endpoints corresponded to
those of the E. coli RNAs. The initial trees showed that multiple versions of an
rRNA gene within any genome were more closely related to each other than to
those of any other genome (except for 23S rRNA gene sequences in Brucella
melitensis 16 M and Brucella melitensis Abortus), so single representative 16S and
23S rRNA genes were taken from each genome. The rRNA gene sequences from
all study genomes were aligned and guided by secondary structure, using ClustalW
with a profile. Seed profiles came from structure-based prealignments: a 16S
alignment for 59 ingroup and 8 outgroup species from the Ribosomal Database
Project II (RDP) release 9.39 (14) and a 23S alignment for 20 species from the
comparative RNA website (10). Masking by Gblocks (with a minimum block
length of 5 nucleotides) left 1,337 and 2,449 positions for 16S and 23S sequences,
respectively. The concatenation of these two masked rRNA alignments was also
Relationships among aligned sequences were examined by ML and Bayesian
phylogeny inference analyses using the Hasegawa-Kishino-Yano substitution
matrix. ML analysis employed 100 bootstrap resamplings. We employed Bayes-
ian analysis with two MrBayes runs, each with four chains for 100,000 genera-
tions, taking the best (highest likelihood) and the consensus of trees after burn-in
within 10,000 to 15,000 generations.
Bacterial protein families and trees. Homology groups suitable for multipro-
tein phylogenetic analysis were identified using the GeneTrees database (29),
which at the time covered 14 alphaproteobacteria and 254 other prokaryotes.
Searching this large set of protein trees for subtrees spanning all 14 alphapro-
teobacteria and containing no more than four additional taxa yielded 216 seed
groups. Hidden Markov models were built for each seed group (4) and used to
search the full protein sets for all genomes under study. We filtered the resulting
gene families, rejecting those missing an ortholog in more than four ingroup
strains, those with more than four strains with multiple members, and those with
the outgroup widely dispersed upon visual inspection of the initial trees. This
resulted in a set of 115 homology groups that approximated the ideal of repre-
senting each strain once (see Table S2 in the supplemental material). TBLASTN
helped to find 8 missing proteins that were previously unannotated and helped to
reconstruct 22 missing or incomplete proteins that had a putative single-nucle-
otide frameshifting sequencing error.
Because the criteria for determining N termini can vary among genome
projects, we used conservation as a criterion for their revision. All protein
sequences were extended in the N-terminal direction until blocked by an in-
frame stop codon. The N-extended sequences were aligned and masked. We
used the N-terminal conserved sequence block to trim the extended sequences,
identifying 225 genes in which the true N terminus may be further upstream than
the annotated one; these trimmed N-extended sequences were used in further
The integrity of the 115 families with respect to possible paralogs was reex-
amined by first allowing each family to repopulate even with second-best BLAST
hits. Each family member protein was used as a BLASTP query against the whole
set of proteins for each strain. Any protein that was either a best hit for a query
or a second-best hit with a bit score at least 80% of that of the best hit for that
strain was added to the protein family. For the ingroup, this analysis left 80
families with no paralogs, 27 slightly expanded families (1 to 8 paralogs), and 8
greatly expanded families (41 or more paralogs). The sequences of the expanded
families were aligned and masked, and the resulting neighbor-joining (BIONJ)
trees were rooted with outgroup species. For the slightly expanded families, the
cases of multiple-ortholog candidates for a strain were resolved by retaining the
candidate with the shortest distance to the shared node if this distance was at
least 20% shorter than any other; otherwise, no candidate was retained for that
family. This simple technique always identified the candidate that best fit our
emerging picture of the species tree and, for ingroup strains, resulted in 50
retentions of the original family member, five replacements of the original, and
nine ambiguous cases in which neither candidate was retained. Among the
greatly expanded families, the tree for one of them could be resolved into two
subtrees corresponding to the ?32and ?70RNA polymerase subunit families and
a third small group that was rejected. The trees for the other seven greatly
expanded families could not be readily resolved, and these families were entirely
The tests of Novichkov et al. (25) were applied to identify protein families that
might follow evolutionary models other than vertical inheritance. Distance scores
were taken for each family alone and for the concatenation of all 109 remaining
families, using TreePuzzle with the WAG amino acid substitution model and a
setting of 1.0 for the shape parameter of the gamma distribution. As a tentative
species tree, the neighbor-joining tree was prepared for the concatenated align-
ment by using PHYLIP with jumbling (18). For each family, distances were fit to
each of three models: (i) no relationship between family member distances and
species distances (noise), (ii) proportionality between family member distances
and species distances (vertical inheritance), and (iii) a model for a single hori-
zontal transfer evaluated for each branch of the tentative species tree. Five
families were eliminated by these tests because they did not reach the critical
value of the F distribution (95% confidence) for rejection of the noise model.
The ability of this method to detect horizontal transfer within the Alphapro-
teobacteria was tested by switching a single protein within a family; not all such
artificial transfers were detected.
The remaining 104 families closely approached 100% representation by the
genomes, reaching 99.8% for the ingroup and 96.7% for the outgroup (see Table
S2 in the supplemental material). No ingroup strain was missing more than 2 of
the 104 families (see Table S1 in the supplemental material).
Ten amino acid substitution models were tested, and WAG was found to be
ideal or nearly so for each family. For each masked alignment, two independent
MrBayes runs proceeded in the model-jumping mode for 500,000 generations
primed with the neighbor-joining tree for the same family. Most families (85)
used the WAG matrix exclusively, and for four more families, WAG was pre-
ferred in more than 75% of the post-burn-in trees. For the remaining 15 families,
which preferred a variety of other models, ML trees were generated with either
the preferred substitution model or WAG. The log likelihood for the WAG-
constrained ML tree was lower than for the preferred-model ML tree, on aver-
age only by 0.24% and by no more than 1.0%, justifying an expedient of the fixed
use of the WAG substitution model in further ML analyses of these families.
Sets of families. Several subsets of the 104 alignments were selected: the group
of 16 protein families found in mitochondria (see below), 26 mutually exclusive
groups of 4 randomly selected proteins, 5 mutually exclusive groups of 10 ran-
domly selected proteins, 4 mutually exclusive groups of 26 randomly selected
proteins, and the 5 groups based on similar alpha-versus-Pinvar values (see
below). Alignments were concatenated for each subset, and BIONJ trees were
prepared and used to prime Bayesian and ML analyses. A single MrBayes run
proceeded for 200,000 generations, reaching burn-in at 10,000 to 80,000 gener-
To further explore the validity of concatenating protein alignments, parame-
ters were evaluated for the plateau set of trees from a WAG-constrained
MrBayes run for each family. In particular, the shape of the gamma distribution
(alpha) had a 3.3-fold range (0.76 to 2.52) and the proportion of invariant sites
(Pinvar) had a 15.6-fold range (0.016 to 0.25). Families were segregated into five
partitions containing from 7 to 40 proteins that fell into approximately equal-
sized areas in an alpha-versus-Pinvar plot.
Full concatenated alignment. For the full concatenation of all 104 alignments,
a neighbor-joining tree was determined using PHYLIP with jumbling. This was
used to prime a MrBayes run of 100,000 generations for the partitioned align-
ment, unlinking the statefreq, alpha, and Pinvar parameters for the partitions;
the topology reached by 8,180 generations was unchanged by the end of the run.
A second MrBayes run for 100,000 generations with the unpartitioned alignment
was primed with the ML tree for the concatenated rRNAs; the topology reached
by 5,480 generations was identical to that of the previous run and was unchanged
by the end of the run. Fifty bootstrap resamplings of the full concatenation were
generated and subjected to ML analysis primed with the neighbor-joining tree.
The extended majority-rule consensus tree matched the topology of the two
To examine concordance, the 5,320 highly supported bipartitions (found in
?95% of post-burn-in trees) from each of the 106 single-gene (104 proteins and
two rRNAs) MrBayes analyses were identified. All trees generated in the course
of this work, in ML bootstrap and MrBayes runs for single genes and concate-
nations, comprising at least 2,200 different topologies, were evaluated by taking
the bipartitions of the tree and counting the number of matches to the highly
supported bipartitions from the single-gene trees.
Links between nodes on the species tree. We used the EEEP program (6) to
determine, when possible, the minimal edit paths between each MrBayes con-
sensus protein tree (considering only nodes with ?95% posterior probability)
and the final species tree, applying the built-in ratchets when necessary. The
same minimum number of edits may be shared by different edit paths such that
two types of edits may occur for any tree comparison, obligate and nonobligate
edits (6). The edits specify links between nodes on the species tree, and the links
were scored by adding the counts of the corresponding obligate edits and the
weighted nonobligate edits for all tree comparisons.
Mitochondrial protein families. Sixteen of our 104 alphaproteobacterial pro-
tein families had homologs in at least one of the mitochondrial genomes, and
each of the eight mitochondria were represented in at least 6 of these families.
Mitochondrial homologs were added to each family, and sequences were aligned,
VOL. 189, 2007TREE FOR THE ALPHAPROTEOBACTERIA 4579
masking ambiguous portions by using Gblocks in two steps. Two steps were
applied because a single application of Gblocks either (i) trimmed too heavily,
removing informative portions of the alphaproteobacterial alignment, or (ii) left
unrelated portions of the more-divergent mitochondrial sequences (mainly at the
termini) inappropriately aligned to the alphaproteobacterial sequence. The first
Gblocks run was for ingroup Alphaproteobacteria and mitochondrial members of
the alignment, and the two outermost endpoints of the resulting blocks were used
for trimming only terminal portions of mitochondrial sequences. The second
Gblocks run was for ingroup Alphaproteobacteria only, creating a mask for the
ambiguous positions in all sequences in the alignment.
Based on the results of initial MrBayes runs for each family, the 16 families
were segregated into five partitions containing one to five proteins that fell into
approximately equal-sized areas in an alpha-versus-Pinvar plot. A BIONJ neigh-
bor-joining tree was prepared and used to prime two parallel MrBayes runs of
200,000 generations, unlinking statefreq, alpha, and Pinvar parameters for the
partitions, in the model-jumping mode, with burn-in by 80,000 generations. The
BIONJ tree was also used to prime ML analysis for 100 bootstrap resamplings of
Uncertainty among rRNA trees. Alphaproteobacterial strains
with complete or nearly complete genome sequences are now
numerous; 72 of these and a large outgroup of eight diverse
strains from the Betaproteobacteria, Gammaproteobacteria, and
Deltaproteobacteria were chosen for further analysis. Seven orders
of Alphaproteobacteria were represented among these strains,
with five represented by more than one strain. Sequences for both
16S and 23S rRNAs were collected and aligned based on second-
ary structure, and ambiguous regions of the alignment were
masked. Trees were generated for the two masked alignments
and for their concatenation, using both the ML algorithm imple-
mented in PHYML and the Bayesian approach implemented in
MrBayes. These trees were discordant, as illustrated by compar-
ing the topologies of the basal branches that were well supported
by either Bayesian posterior probability or ML bootstrap analysis
(Fig. 1). The outgroup was consistently separated from the in-
group, and each of the five multiply represented alphaproteobac-
terial orders was retained intact in at least one of these trees.
However, only one multiply represented order, Sphingomo-
nadales, was retained intact in all the trees. Comparing the basal
branching patterns of the pair of ML and Bayesian trees, we
found one incompatibility for the 16S rRNA tree pair (regarding
the integrity of the Rickettsiales), no incompatibilities for the 23S
rRNA tree pair, and one incompatibility for the rRNA concate-
nation tree pair (regarding the affiliation of Parvularcula). Al-
though the ML and Bayesian methods explore tree space in
different ways, they do both evaluate likelihood by using the same
substitution matrix and therefore could be expected to produce
similar trees for a given alignment. When they instead produce
trees whose highly supported nodes conflict, the common inter-
pretation of support values as probabilities is called into question;
these values may be artificially high. Comparing the trees among
the three different rRNA alignments shows that none of their
basal topologies are compatible. The 16S trees split the Aceto-
bacteraceae from the other Rhodospirillales and the 23S trees
group the Rhodospirillales but split the Hyphomonadaceae from
the other Rhodobacterales, while the rRNA concatenation tree
splits both the Rhodospirillales and Rhodobacterales. Discordance
between 16S and 23S rRNA trees would be expected if the gene
for one of the molecules had undergone lateral transfer, as has
other explanations, such as imperfect sequence alignment and
masking. With the incompatibilities between molecular phylog-
enies and between methods and the frequent failure to resolve
basal nodes with high support, our rRNA analysis does not pro-
vide a robust species tree.
Collection of protein families. We turned to collecting protein
families that together might reveal a major vertical component of
species phylogeny (27). Ideally, such families should represent
FIG. 1. Basal branching patterns from various analyses. ML bootstrap and Bayesian (underlined) support values are shown when ?100%, with
nodes collapsed when support was ?50%. Taxa: Z, Rhizobiales (b, Bradyrhizobiaceae; x, Xanthobacter; o, other Rhizobiales); B, Rhodobacterales (r,
Rhodobacteraceae; h, Hyphomonadaceae); P, Parvularculales; C, Caulobacterales; S, Sphingomonadales; L, Rhodospirillales (r, Rhodospirillum; m,
Magnetospirillum; a, Acetobacteraceae); K, Rickettsiales (r, Rickettsiaceae; a, Anaplasmataceae; p, Pelagibacter); M, mitochondria; O, outgroup
4580 WILLIAMS ET AL. J. BACTERIOL.
each species once and only once, not mix members of paralogous
families, and not show evidence of horizontal gene transfer. A
search of GeneTrees (29), a large database of trees for prokary-
otic protein families, identified 216 families in which the Alpha-
proteobacteria were found together in a subtree containing no
more than four non-Alphaproteobacteria. This simple criterion
was designed to exclude families affected by horizontal gene
transfer from a nonalphaproteobacterial group into the Alphapro-
teobacteria; indeed, it excluded all eight protein families previ-
strain but with evidence of lateral gene transfer (25). The align-
ments for these seed groups were then used to build hidden
Markov models with which to search for the homologs among all
proteins of all the genomes under study. Expanded families that
were missing an ortholog in more than four ingroup strains, had
more than four strains with multiple members, or had the out-
group widely dispersed in initial trees were rejected, resulting in a
set of 115 homology groups that approximated the ideal of rep-
resenting each strain once and only once.
We used a novel approach to standardize the starting posi-
tion for protein sequences by extending protein sequences N
terminally according to genome sequence, realigning, and
marking the N-terminal endpoint of conservation. As a second
test of whether the remaining families might mix members of
paralogous subgroups, the families were enlarged again by the
addition of high-scoring second-best BLAST hits. Of eight
greatly enlarged families, only one could be resolved into two
orthologous subfamilies and the others were rejected. Among
the families still under consideration, 27 had one or more
paralogs. These were resolved, when possible, by a simple test
based on tree branch lengths (55 of 64 cases); otherwise, no
candidate was retained. The families were then subjected to
tests for both noisy phylogenetic signal and possible horizontal
gene transfer (25); five families were rejected due to noisy
signal and none due to possible horizontal transfer.
The molecular functions of the 104 retained protein families
were approximately equally distributed among ribosome struc-
ture, other protein synthesis roles, protein fate, RNA and
DNA metabolism, and other metabolism; one family had no
functional characterization (see Table S2 in the supplemental
Tree building. For each protein family, sequences were
aligned, ambiguous portions of the alignments were masked
out, and trees were built using ML and Bayesian methods. As
with the rRNA trees, tree topologies did not agree either
between the two methods or between any two families and
support for nodes was often weak. Of 7,968 nodes among the
ML trees, 2,201 (28%) had ?50% bootstrap support.
It was observed that the pair of tree topologies produced by
the two methods had better agreement for the families with
longer alignments (Fig. 2A). This suggested that part of the
problem with single families is that they have insufficient in-
formation content and that trees from even longer alignments
would be more reliable. Producing a longer alignment by the
concatenation of alignments for multiple-protein families is
justified if the families have evolved under similar parameters
or if those parameters are allowed to vary independently in
partitions of the family set. The WAG amino acid substitution
matrix was found to be either the best available or near optimal
in each of the single-protein analyses, so this matrix was used
for analyzing concatenated alignments. Initial trees also
showed that two parameters, the shape parameter of the
gamma distribution and the proportion of invariant sites, var-
ied somewhat among the protein families. The families were
sorted into five subgroups according to their position in a plot
of these two parameters, and the concatenation of all 104
alignments was partitioned accordingly. Bayesian analysis,
primed with either a neighbor-joining tree for the concate-
nated alignment or an rRNA tree, quickly and stably settled
upon a single tree topology. The consensus tree from ML
analysis of bootstrap samples of the concatenated alignment
had an identical topology. Thus, a single tree topology
emerged from multiple analyses of the concatenated align-
ment, and the support values were very high: 100% for each
node by Bayesian analysis and nearly so for ML bootstrap
analysis. Additional measures described below provided fur-
ther support for this topology, and we term the most likely
Bayesian tree (Fig. 3) our “final tree.”
FIG. 2. Topology convergence for trees from subsets of all single-molecule families. For each masked sequence alignment, the Robinson-
Foulds symmetric difference was used to compare topologies between the Bayesian tree and either (A) the corresponding ML tree or (B) the final
tree (Fig. 3). The data for the protein alignments were fit to the power law curves (A) y ? 1,110x?0.70(R2? 0.61) and (B) y ? 1,150x?0.55(R2?
0.74). The values (zero in both cases) for the final tree were not used in the curve fitting, but their positions are marked.
VOL. 189, 2007TREE FOR THE ALPHAPROTEOBACTERIA4581
Support for the tree. Extremely high Bayesian and bootstrap
support values such as those obtained here are common in
studies with long concatenated protein alignments and have
been regarded as misleading (26). Additional methods were
applied to assess the reliability of the tree. Its overall concor-
dance with the 106 single-molecule (104 proteins and two
rRNAs) Bayesian trees was measured. For the 5,320 nodes in
the single-molecule trees that had ?95% support, 4,362
(82.0%) were in agreement with the final tree (7). Thus, the
genes for these molecules show a strong trend toward the same
pattern of vertical inheritance. Concordance was also used to
assess each of the 77 nontrivial nodes in the final tree. Values
for the percentage of single-molecule trees that showed high
(?95%) support for a node were low (53.6%) on average and
FIG. 3. Tree for the Alphaproteobacteria. This tree was the most likely found by Bayesian analysis of the concatenation of masked alignments
for 104 selected protein families (33,730 characters) and had topology identical to that of the ML bootstrap consensus tree. Note that unusual
support values are displayed. Traditional support values were extremely high, with 100% posterior probability Bayesian and ML bootstrap support
values for each node, except for the nodes marked by values in parentheses, which show bootstrap support when ?100%. The main support values
presented here instead show concordance with the 106 single-gene trees (104 proteins and two rRNAs), given as the percentage of single-gene trees
with very high (?95%) Bayesian support for the node. Node concordance is an extremely stringent criterion for support that should not be
interpreted as the probability that the bipartition is true. The point at which the mitochondria branch in Fig. 5 is indicated. The dashed arrow is
an edit (grouping the Caulobacterales/Parvularculales/Hyphomonadaceae with the Rhizobiales rather than with the Rhodobacterales) that would
increase by 1 the total concordance, with highly supported nodes from single-gene trees. The taxon used to root the tree was Geobacter
sulfurreducens from the Deltaproteobacteria.
4582 WILLIAMS ET AL.J. BACTERIOL.
ranged from 7 to 100% (Fig. 3). Node concordance should be
considered an extremely stringent criterion for evaluating sup-
port within the tree and should not be interpreted as support
values usually are, i.e., as the probability that a node is correct.
Nonetheless, the values do appear to provide some measure of
the reliability of the nodes, in that the nodes that have ML
bootstrap values below 100% are among those with the lowest
As a second approach to evaluating the reliability of the tree,
we examined how well its topology was reproduced by sub-
groups of the full set of 104 proteins, in what might be con-
sidered whole-protein bootstrap analysis of the concatenated
alignment (Fig. 2B). Random groups of 4, 10, or 26 of the
proteins and other subgroups were taken, and their concate-
nated alignments were evaluated by ML and Bayesian meth-
ods. With longer alignments, the trees built by the two methods
agreed better and also agreed better with the final tree. The
curves fitting these two trends were extrapolated to the number
of characters in the full concatenated alignment, showing that
the full alignment was expected to produce a very reliable
topology (Fig. 2). Comparison with the trends for the rRNA
alignments indicates that the phylogenetic resolving power of
nucleotide characters was six- to ninefold lower than for amino
acids in our masked alignments.
Overall concordance with the single-molecule trees, a crite-
rion quite different from the original one (likelihood), was
used to reassess all the thousands of tree topologies encoun-
tered in all the MrBayes runs and ML studies for individual
sequence alignments and concatenations. The final tree had
the second-best concordance; another tree exceeded its con-
cordance by 1 (agreeing with 4,363 of the highly supported
nodes from the single-molecule trees). A single subtree prune-
FIG. 4. Links between nodes on species tree. The top 34 links, from an analysis of edit paths between single-protein family trees and the final
tree, are mapped onto the final tree of Fig. 3. Noninteger scores for links were allowed because nonobligate edits were weighted and added to the
count of obligate edits.
VOL. 189, 2007 TREE FOR THE ALPHAPROTEOBACTERIA4583
and-regraft operation (edit) to the final tree, grouping the
Rhizobiales rather than with the Rhodobacterales, was sufficient
to produce the most concordant tree. No additional edits to the
final tree that improved its concordance were identified among
the next 10 most concordant topologies.
From tree to network. When a single-protein tree disagrees
with a species tree, a series of edits can often be proposed that
bring the trees into agreement (6). An extreme interpretation
of the minimal set of such edits is that they represent a mini-
mum number of horizontal transfers in the history of the pro-
tein gene, although it should be noted that discordance be-
tween protein and species trees may have other causes, such as
inappropriate alignment and masking. These edits create links
between nodes of the species tree, converting the tree to a
network, and the links can be ranked by the edit counts for all
the protein trees under study. The systematic determination of
such links, which have been termed “highways of gene shar-
ing,” on a large prokaryotic species tree showed that most edits
occurred within the major phylogenetic divisions but that a
large number of edits occurred between divisions (7). We ex-
amined the minimal edit series that converted our final species
tree into each of the single-protein trees of our study (except
for 13 protein trees for which no series could be determined);
the number of minimum edits averaged 7.5 per tree. The edits
defined 556 links on the species tree, with 243 scoring at least
1.0. Figure 4 shows that the top links tend to cross the nodes
with poorer concordance support. They illustrate some of the
more likely phylogenetic avenues along which even the highly
conserved genes in our collection may have been successfully
transferred during the evolution of these genomes.
Placement of the mitochondrial branch. Many studies have
placed the ancestor of mitochondria within the Alphapro-
teobacteria, usually within the Rickettsiales, but one study sug-
gested that it might have arisen in the Rhodospirillales (17, 19,
32). Most of the original gene complement of mitochondria
has either been lost or migrated to the eukaryotic nuclear
genome, such that the most primitive and gene-rich mitochon-
drial genome known, from the jakobid Reclinomonas ameri-
cana, has only 67 protein-coding genes. Yet, 16 of these pro-
teins are among the set of 104 in our alphaproteobacterial
analysis, a number high enough that the concatenated align-
ment should have sufficient information content for reasonably
accurate phylogenetic analysis. Seven additional mitochondrial
genomes that have been considered primitive each had from 6
to 9 of these same proteins (and no others from the 104-
protein set). These mitochondria do contain additional con-
served protein families that could be useful if the goal were
solely to produce a mitochondrial phylogeny. However, by
restricting the analysis to the families that were already shown
to be well behaved among Alphaproteobacteria alone, we
sought to optimize the survey of alphaproteobacterial phylog-
eny for the point at which mitochondria diverged.
For these 16 protein families, the representative sequences
from outgroup Proteobacteria, Alphaproteobacteria, and mito-
chondria were aligned, masked, and concatenated. Two trees
were generated using MrBayes and ML bootstrap analyses;
their topologies were compatible and, in the bacterial portion,
matched that of the 104-protein tree. The mitochondria
grouped together on a single branch that emerged from within
the Rickettsiales, with the combined Anaplasmataceae/Rickett-
siales as a sister group, and were subtended by the Pelagibacter
branch (Fig. 5).
The multiprotein tree presented here (Fig. 3) received per-
fect Bayesian support and nearly perfect bootstrap ML sup-
port. Such support values may be misleadingly high for long
concatenated alignments, but this tree was also the second best
among all the thousands of trees encountered in the course of
this study, as judged by concordance with the highly supported
FIG. 5. Mitochondrial branch. The portion for Rickettsiales is shown for the most likely tree found by Bayesian analysis of the concatenation
of masked alignments for 16 selected protein families (4,830 characters), in which each node received 100% Bayesian support, except those
indicated with underlined values. Identical topologies for bacterial strains arose as the ML bootstrap consensus, from which all nodes received
100% support, except those indicated in parentheses. The inset shows the topology for the mitochondrial branch from the ML bootstrap consensus,
collapsing nodes with ?50% support. The outgroup and alphaproteobacterial portions of the tree that are collapsed in this depiction had the same
topology as those shown in Fig. 3.
4584WILLIAMS ET AL. J. BACTERIOL.
nodes of the single-gene trees. The residual discordance be-
tween the species tree and the individual protein trees was
used to explore non-tree-like aspects of the inheritance for
even the “well-behaved” genes selected here (Fig. 4) that may
have resulted from horizontal transfers (3).
This tree splits the Hyphomonadaceae from the Rhodobac-
terales and places them with the combined Caulobacterales and
Parvularculales. The clustering of Hyphomonadaceae with
Caulobacterales has been noted before. In one study, the
Hyphomonadaceae were observed to cluster with the Cau-
lobacterales in protein-based trees, yet with the Rhodobactera-
les in 16S rRNA trees (1). We obtained these same results;
indeed,the unfavored Hyphomonadaceae/Rhodobacterales
clustering was obtained for 16S rRNA trees whether we used
(i) an alignment based on a profile from RDP and masked
using Gblocks (Fig. 1), (ii) a masked alignment prepared di-
rectly by a server at RDP, or (iii) a de novo alignment by
MUSCLE masked using Gblocks (data not shown). Horizontal
transfer of the 16S rRNA gene has been invoked for other
alphaproteobacteria and specifically for Hyphomonas (1, 30).
However, the favored Hyphomonadaceae/Caulobacterales clus-
tering did appear in a recent analysis of a manual alignment of
16S rRNA sequences (24), suggesting that horizontal transfer
need not be invoked and that RDP-based and other 16S rRNA
sequence alignments may have been misleading. Members of the
Hyphomonadaceae and Caulobacterales share an unusual di-
morphism, exhibiting both a nonmotile stalked reproductive
cell type and a motile cell type, and comparison of Hyphomo-
nas and Caulobacter genomes have revealed several close re-
lationships. There have been calls to unite these groups within
the order Caulobacterales (1, 2, 24). Our tree supports this
unification and suggests that the order should additionally in-
clude the Parvularculaceae, with the abandonment of the order
Parvularculales, which was designated mainly on the basis of
16S rRNA analysis (12). It can be noted that one characteristic
linking the Hyphomonadaceae and Caulobacterales, a prolifera-
tion of TonB-dependent receptors (2), is shared by Parvularcula;
29 of its proteins are so annotated. However, this characteristic
may not be highly distinctive; 18 of 34 non-Rickettsiales Alpha-
proteobacteria in a recent study had more than 10 TonB-
dependent receptors, although only 6 had more than 25 (8).
Comprehensive comparative genome analysis of the single
Parvularcula genome will be an important next step in under-
standing its affiliation, especially since no candidate strain for
a second genome project has been described that is both
closely related and sufficiently distinct to provide a phyloge-
netic perspective. It has been further suggested that the re-
maining Rhodobacterales (exclusive of the Hyphomonadaceae)
should be subsumed within the Caulobacterales (24); however,
neither our data nor the data in that study strongly support this
Despite its success in grouping Hyphomonadaceae with Cau-
lobacterales, the 16S tree used in the study of Lee et al. dis-
agreed in several important ways with our multiprotein tree
(24). It split the Rhodospirillales. It grouped Parvularcula with
Sphingomonadales and Sphingomonadales with Rhizobiales.
These discrepancies may be due to the usual problems with
inference based on 16S rRNA gene sequences, possibly insuf-
ficient information content, or difficulties in proper alignment
and masking. By concatenating masked alignments for multi-
ple sequence families, the information content is increased and
the effects of nonsystematic errors in alignment and masking
The evolutionary branching order for six alphaproteobacte-
rial orders inferred based on conservation patterns for indels in
protein sequences (22) agrees with the branching order ob-
tained for our tree, although that study did not resolve the
branching order of the Rhodospirillales and Sphingomonadales
as ours has. Our tree also confirms the deep split in the
Rhizobiales noted frequently in previous studies (23, 24).
Much evidence supports the origin of mitochondria from
within the Alphaproteobacteria, although various positions
within the alphaproteobacterial phylogeny have been pro-
posed. One study has pointed outside the Rickettsiales to
Rhodospirillum (17), but most have placed the mitochondrial
ancestor within or basal to the Rickettsiales, some specifically
within the Rickettsiaceae (16), and others, like ours, as a sister
to the combined Rickettsiaceae and Anaplasmataceae (19, 32).
The availability of many recently sequenced genomes for our
analysis adds new perspective to this placement, showing the
free-living marine bacterium Pelagibacter closely subtending
the mitochondria/Rickettsiaceae/Anaplasmataceae group. Thus,
Pelagibacter will serve as a useful member of the outgroup in
future phylogenetic analysis of mitochondrial genes.
This work was supported by the Virginia Bioinformatics Institute
and by U.S. Department of Defense grant W911SR-04-0045 to B.W.S.
and USDA CSREES grant 3602-22000-013-02 to A.W.D.
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