Rhodopseudomonas palustris Regulons Detected by Cross-Species Analysis of Alphaproteobacterial Genomes

Abstract

Rhodopseudomonas palustris, an alpha-proteobacterium, carries out three of the chemical reactions that support life on this planet: the conversion of sunlight to chemical-potential energy; the absorption of carbon dioxide, which it converts to cellular material; and the fixation of atmospheric nitrogen into ammonia. Insight into the transcription-regulatory network that coordinates these processes is fundamental to understanding the biology of this versatile bacterium. With this goal in mind, we predicted regulatory signals genomewide, using a two-step phylogenetic-footprinting and clustering process that we had developed previously. In the first step, 4,963 putative transcription factor binding sites, upstream of 2,044 genes and operons, were identified using cross-species Gibbs sampling. Bayesian motif clustering was then employed to group the cross-species motifs into regulons. We have identified 101 putative regulons in R. palustris, including 8 that are of particular interest: a photosynthetic regulon, a flagellar regulon, an organic hydroperoxide resistance regulon, the LexA regulon, and four regulons related to nitrogen metabolism (FixK2, NnrR, NtrC, and sigma54). In some cases, clustering allowed us to assign functions to proteins that previously had been annotated with only putative functions; we have identified RPA0828 as the organic hydroperoxide resistance regulator and RPA1026 as a cell cycle methylase. In addition to predicting regulons, we identified a novel inverted repeat that likely forms a highly conserved stem-loop and that occurs downstream of over 100 genes.

Full text

APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Nov. 2005, p. 7442–7452 Vol. 71, No. 11
0099-2240/05/$08.00⫹0 doi:10.1128/AEM.71.11.7442–7452.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.
Rhodopseudomonas palustris Regulons Detected by Cross-Species
Analysis of Alphaproteobacterial Genomes
Sean Conlan,
1
Charles Lawrence,
1,2
and Lee Ann McCue
1
*
The Wadsworth Center, New York State Department of Health, Albany, New York 12201,
1
and Center for
Computational Molecular Biology, Brown University, Providence, Rhode Island 02912
2
Received 17 January 2005/Accepted 14 June 2005
Rhodopseudomonas palustris,an␣-proteobacterium, carries out three of the chemical reactions that support
life on this planet: the conversion of sunlight to chemical-potential energy; the absorption of carbon dioxide,
which it converts to cellular material; and the fixation of atmospheric nitrogen into ammonia. Insight into the
transcription-regulatory network that coordinates these processes is fundamental to understanding the biology
of this versatile bacterium. With this goal in mind, we predicted regulatory signals genomewide, using a
two-step phylogenetic-footprinting and clustering process that we had developed previously. In the first step,
4,963 putative transcription factor binding sites, upstream of 2,044 genes and operons, were identified using
cross-species Gibbs sampling. Bayesian motif clustering was then employed to group the cross-species motifs
into regulons. We have identified 101 putative regulons in R. palustris, including 8 that are of particular
interest: a photosynthetic regulon, a flagellar regulon, an organic hydroperoxide resistance regulon, the LexA
regulon, and four regulons related to nitrogen metabolism (FixK
2
, NnrR, NtrC, and ␴
54
). In some cases,
clustering allowed us to assign functions to proteins that previously had been annotated with only putative
functions; we have identified RPA0828 as the organic hydroperoxide resistance regulator and RPA1026 as a cell
cycle methylase. In addition to predicting regulons, we identified a novel inverted repeat that likely forms a
highly conserved stem-loop and that occurs downstream of over 100 genes.
The alpha division of the proteobacteria is defined by a
conserved 16S rRNA gene sequence and insertions/deletions
in conserved protein sequences (20). Despite being phylo-
genetically related, the ␣-proteobacteria have no defining
phenotypic characteristics. Among the photosynthetic ␣-pro-
teobacteria (i.e., purple bacteria) are Rhodobacter sphaeroides,
Rhodospirillum rubrum, and Rhodopseudomonas palustris.
These metabolically adaptable species are found in a variety of
niches, including marine environments, freshwater sediments,
and soil, and they have the ability to photosynthesize, fix car-
bon dioxide, and fix nitrogen. Other members of the ␣-pro-
teobacteria live in association with eukaryotes, both plants and
animals. Plant-associated species include Bradyrhizobium ja-
ponicum, a soybean symbiote, and Agrobacterium tumefaciens,
the causative agent of crown gall disease. Animal pathogens
include Bartonella henselae,Rickettsia prowazekii, and Brucella
suis, the causative agents of cat scratch disease, typhus fever,
and porcine brucellosis, respectively. At the time of this study,
complete genome sequence data are available for 13 ␣-pro-
teobacterial species, and partial genome sequence data are
available for an additional 16 species. Given the wealth of
sequence data available and the metabolic and environmental
diversity of these bacterial species, the ␣-proteobacteria con-
stitute an excellent target for a comparative genomics study.
The purple photosynthetic bacterium R. palustris exhibits a
highly flexible metabolic lifestyle, reflecting the diversity
present in the ␣-proteobacteria, and was therefore an obvious
choice as the reference species for this comparative study.
In addition to its ability to photosynthesize, fix carbon dioxide,
and fix nitrogen, R. palustris can degrade a variety of organic
aromatic compounds, including those found in plant matter and
industrial waste (10, 21). R. palustris is able to respire both aero-
bically and anaerobically. Under anaerobic conditions in the pres-
ence of light, the bacterium forms stacked membrane structures
that house the photosynthetic machinery and generate energy
(51). It can produce hydrogen using a nitrogenase-dependent
mechanism, thus making it a potential bioenergy source (2). The
R. palustris genome encodes many alternate or redundant sys-
tems, including four complete LH
2
(light-harvesting) com-
plexes, two NADH-dependent dehydrogenases, and three ni-
trogenases (24). R. palustris and other ␣-proteobacterial
species, like Caulobacter crescentus and Hyphomonas neptu-
nium, share an asymmetric cell division phenotype, wherein
cell division is initiated only by surface-adherent cells, resulting
in an adherent cell and a motile cell (22). It has been suggested
that the immobilized cells may be exploited in the fabrication
of biocatalysts (24). The potential applications in energy pro-
duction, bioremediation, and biocatalysis make R. palustris a
bacterial species of considerable environmental and biotech-
nological interest. In order to realize that potential, it is im-
portant that we first understand the regulation of R. palustris’
complement of metabolic pathways.
A consequence of R. palustris’ metabolic flexibility is the
need for the organism to have an extensive transcription-reg-
ulatory network. Cellular processes, such as nitrogen fixation
and carbon dioxide fixation, are energy intensive; thus, the
enzymes for these pathways must be regulated with respect to
nutrient availability and other environmental factors (for re-
views, see references 7, 8, and 43). For example, it is most
efficient for the bacterium to regulate the synthesis of its pho-
* Corresponding author. Present address: Wadsworth Center, New
York State Department of Health, Center for Medical Sciences, 150
New Scotland Ave., Albany, NY 12208. Phone: (509) 375-2912. E-mail:
rpalustris@wadsworth.org.
7442

tosynthetic machinery with respect to light intensity and wave-
length (18). The presence of a sophisticated regulatory net-
work is supported by the large number of annotated
transcription-regulatory and signaling proteins (451 genes) en-
coded in the R. palustris genome (24).
In order to begin to dissect this complex regulatory network,
we undertook a comparative study of the ␣-proteobacteria,
focusing on R. palustris. The first goal of this study was to
identify transcription factor binding sites (TFBSs) genomewide
by phylogenetic footprinting. Phylogenetic footprinting is an
approach to discover regulatory motifs in orthologous inter-
genic regions. The assumption is that among a phylogenetically
close group of species, orthologous genes are likely to be reg-
ulated by a common transcription factor (TF). Under this
assumption, the TFBSs will be conserved, while other noncod-
ing DNA that is not under selective pressure will be free to
mutate over time. Thus, the promoter regions upstream of
orthologous genes are analyzed for putative regulatory motifs
by computationally searching for conserved sequence ele-
ments. In the current study, conserved sequences were found
using a Gibbs sampling strategy. This approach predicts regu-
latory motifs de novo without any prior knowledge regarding
binding sites and exploits the diversity of the contributing spe-
cies in order to increase the power of the search for regulatory
motifs.
An advantage of using the Gibbs sampler for these studies is
that it implements a rigorous Bayesian method to infer the
number of sites and their locations (49). This means that not all
of the species contributing orthologous promoter data are re-
quired to contribute to a motif prediction (detailed discussion
and examples can be found at http://bayesweb.wadsworth.org
/web_help.PF.html). Accordingly, a TFBS that is present in an
intergenic region of the target species and some of the species
in the collection may be absent in others. This feature is im-
portant, since the selection of species for phylogenetic foot-
printing is empirical; we require only that the species be phy-
logenetically related so that they have a significant number of
common TFs (30). This requirement was met for the set of
␣-proteobacteria selected for the current study (Table 1), al-
lowing us to predict regulatory motifs for the promoter regions
of 2,044 operons. We have previously demonstrated the power
of Gibbs sampling (49) for phylogenetic footprinting within the
␥-proteobacteria (29, 30). Specifically, among promoters with
experimentally verified TFBSs, ⬃74% of the predictions cor-
responded to the known sites. Furthermore, among the re-
maining novel predictions were TFBSs predicted upstream of
fatty acid biosynthesis genes; these sites were used to affinity
purify a previously uncharacterized TF (YijC) that has since
been shown to regulate fatty acid biosynthesis in vivo (55).
The second goal of this study was to cluster these predicted
cis regulatory elements into regulons. Clustering provides a
mechanism by which motifs that represent binding sites for the
same TF are grouped; this combined evidence improves the
reliability of pathway and cognate TF identification. We used a
Bayesian clustering algorithm developed previously that was
found to accurately cluster Escherichia coli regulatory motifs
(38). A total of 101 putative R. palustris regulons were identi-
fied, including 8 that are of particular interest.
MATERIALS AND METHODS
Genome sequence data. The following genome sequences were obtained from
the NCBI reference sequence database (ftp://ftp.ncbi.nih.gov/genome/Bacteria/):
Rhodopseudomonas palustris CGA009 (NC_005296), Bradyrhizobium japonicum
USDA 110 (NC_004463), Brucella suis 1330 (NC_004310 and NC_004311), and
Caulobacter crescentus CB15 (NC_002696). Incomplete genome sequence data
for Rhodobacter sphaeroides (v 11/9/2003), Rhodospirillum rubrum (v 11/9/2003),
and Novosphingobium aromaticivorans (v 11/12/2003) were obtained from the
Department of Energy Joint Genome Institute (http://www.jgi.doe.gov). The
sequence of Hyphomonas neptunium ATCC 15444 (March 2004) was obtained
from The Institute for Genome Research (http://www.tigr.org). R. palustris
operon predictions were downloaded from the Department of Energy Joint
Genome Institute’s site.
Phylogenetic footprinting. Orthologous intergenic regions were identified as
previously described (29). Briefly, TBLASTN (1) was used to identify orthologs
of 4,814 R. palustris proteins (excluding 18 of the 4,832 CDS entries in
NC_005296 that are pseudogenes) in a database composed of the genome se-
quence data of the eight species under study. The following criteria were applied
in order to select hits corresponding to bona fide orthologs: (i) the expectation
value must be ⬍10
⫺20
; (ii) the expectation value must be better than the expec-
tation value of the second-best hit in R. palustris; (iii) a hit must start within the
first 20 amino acids of the R. palustris query sequence. Upstream orthologous
intergenic regions were extracted from the database with a maximum length of
500 bp and a minimum length of 50 bp. In some cases, it was obvious that the
start codons for genes in R. palustris were annotated upstream of a more likely
5⬘end of the gene (e.g., lexA). To prevent the loss of regulatory signals near the
5⬘ends of genes, we reevaluated the start codons using the cross-species BLAST
data. In 240 cases, we identified a more likely downstream start codon. The
TABLE 1. Selected features shared between the ␣-proteobacterial
species used in this study
Species and description Characteristics No. of
TFs
R. palustris; widely distributed Photosynthesizes 168
a
Fixes carbon dioxide
Fixes nitrogen
Degrades aromatic
compounds
Asymmetric cell
division phenotype
B. japonicum; soybean symbiote Fixes nitrogen 139
C. crescentus; aquatic Degrades aromatic
compounds
51
Asymmetric cell
division phenotype
B. suis; intracellular animal
pathogen
Degrades aromatic
compounds
61
R. sphaeroides; widely distributed Photosynthesizes 54
b
Fixes carbon dioxide
Fixes nitrogen
R. rubrum; widely distributed Photosynthesizes 69
b
Fixes carbon dioxide
Fixes nitrogen
N. aromaticivorans; widely
distributed, opportunistic
pathogen
Degrades aromatic
compounds
44
b
H. neptunium; marine Asymmetric cell
division phenotype
46
b
a
Count of TFs identified in each species for the 168 that are found in
R. palustris and at least one other species (70 of the 238 R. palustris TFs did not
have an ortholog in any of the above species by our criteria).
b
These counts are from incomplete genomes and are therefore preliminary.
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BLAST procedure described above was then rerun with the revised (i.e., short-
ened) sequences. Potential artifacts arising from the shifting of these start codons
were minimized by tracking the revised genes throughout the phylogenetic-
footprinting and clustering process.
The recursive Gibbs sampler (Gibbs v 2.06.015) (50) was used to exhaustively
search for palindromic and nonpalindromic motifs in each orthologous inter-
genic data set. A Bayesian segmentation algorithm was used to generate a
position-specific background composition model for each sequence in a data set
(Unifiedcpp) (26). All models consisted of 16 active columns that were allowed
to fragment to a maximum width of 24 columns. For the detection of palin-
dromes, the Gibbs sampler was allowed to choose an even- or odd-width palin-
dromic model, based on the sequence evidence. The sampler was allowed to run
for 2,000 iterations, with a plateau period of 200 iterations, and it was reinitial-
ized 40 times using random seeds. The most significant motif found by the
recursive Gibbs sampler in a data set was masked, and the sampling procedure
was repeated until no motifs were found with an average maximum a posteriori
probability (avgMAP) of ⬎1. This average MAP cutoff was chosen empirically.
For reference, MAP values of ⬎0 provide a measure of how much more likely
the alignment is than the unaligned background. The entire process (sampling
and masking) was repeated three times to take advantage of the stochastic nature
of Gibbs sampling and to maximize the number of motifs found. Motifs that
contained unique sites in R. palustris were then extracted and used for further
analysis.
Clustering. We selected motifs for clustering by first applying a critical-value
criterion; palindromic and nonpalindromic motifs were compared to model-
specific critical-value criteria derived from random data simulations (30). Coding
sequence contamination in the extracted intergenic regions was detected through
comparison of the coordinates of each site in a motif to the available genome
annotations (R. palustris,B. japonicum,B. suis, and C. crescentus). If more than
half of the sites from annotated species overlapped a coding region, the motif
was eliminated from clustering. Motifs were also analyzed for the presence of
shared sites. If two motifs contained the same site from more than one species,
or if motifs contained the same site from R. palustris (which sometimes occurs
with divergently transcribed genes), one motif was eliminated from the cluster-
ing input.
Clustering was carried out using the Bayesian motif clustering algorithm
(BMC v 1.5x) (38). Even-, odd-, and nonpalindromic models were clustered
separately using a tuning parameter (q) of 100. This parameter, which affects
whether a motif forms a cluster on its own or whether the motif joins an existing
cluster, was determined empirically. BMC was run for 10 iterations without
fragmentation, followed by 25 iterations with the fragmentation option enabled,
to produce the optimal solution. BMC was then allowed to sample for an
additional 500 iterations to produce the frequency solution described in this
study. All clusters were initially allowed to shift, meaning that motifs could
realign with respect to each other within a cluster. The shifting option did not
influence the alignment of motifs in palindromic clusters and was turned off.
Shifting did, however, improve the alignment of nonpalindromic motifs within a
cluster; thus, we allowed nonpalindromic motifs to shift left or right by up to two
columns.
Scanning. Cluster models from the frequency solution were used as input to
the Dscan algorithm (Dscan revision 2.3) (34), using either palindromic models
(the base counts in paired palindromic columns were summed) or nonpalin-
dromic models, as indicated by the cluster model parity. The complete set of
R. palustris intergenic regions was scanned to detect sites that matched the
model. Given the input model and database, Dscan uses the approach described
by Staden (44) to report sites that match the model above a given level of
statistical significance. In this study, we report sites at a Pvalue of ⬍0.05.
RESULTS
Phylogenetic footprinting to identify transcription factor
binding sites. The choice of species to be used for phylogenetic
footprinting was governed by two factors: their phylogenetic
relatedness and the presence of common metabolic pathways.
First, the phylogenetic relatedness of ␣-proteobacterial species
for which a complete or nearly complete genome sequence was
available was inferred using the sequences of their 16S rRNA
genes. Current alignment algorithms, including Gibbs sam-
pling, do not account for the phylogenetic relatedness (corre-
lation) of the input data, which leads to overestimation of the
significance of motifs. Therefore, to achieve sufficient phylo-
genetic diversity and minimize losses to the power of the cross-
species technique due to phylogenetic correlations among the
sequence data, we chose R. palustris and seven additional spe-
cies (Fig. 1), using the technique of Newberg and Lawrence
(35). This was done in a “greedy” fashion, so as to maximize
the effective number of independent sequences. This means
that, although additional genome sequences were available,
they were not expected to improve the reliability of predictions
in R. palustris and in fact would be likely to skew the results by
including highly correlated data. This is why, for instance, only
one of the sequenced Brucella species was retained.
Six of the eight species shown in Fig. 1 approximate inde-
pendent observations; that is, for each possible species pair
comparison, there is no more correlation, measured as the
percent identity in the orthologous intergenic regions, than
would be expected by chance (⬃40% ⫾4%). R. palustris and
B. japonicum, however, exhibit some correlation in sequence,
with an average of 47% ⫾8% sequence identity in the inter-
genic regions. B. japonicum was retained, nevertheless, be-
cause the inclusion of a correlated species has been shown to
improve reference species predictions in a cross-species study
(30), and this low level of correlation can be accounted for in
the critical value (Pvalue) calculations (see Materials and
Methods). A second factor in our choice of species was the
presence of common metabolic pathways and TFs; Table 1
highlights features of each species that are expected to con-
tribute to the analysis of R. palustris. The conservation of TFs
across species was examined, and of the 238 R. palustris TFs
(24), 168 were found to have an ortholog in at least one of the
other seven species.
Using the set of eight species, we compiled orthologous
upstream intergenic sequence data sets representing 2,411
R. palustris genes/operons and used them as input to the Gibbs
sampler (see Materials and Methods). A total of 4,963 motifs
were found for 2,044 of these data sets by phylogenetic foot-
printing. Statistical-significance criteria were determined using
FIG. 1. Phylogenetic tree of the ␣-proteobacterial species used in
this study (Rhodopseudomonas palustris,Bradyrhizobium japonicum,
Caulobacter crescentus,Brucella suis,Rhodobacter sphaeroides,Hy-
phomonas neptunium,Rhodospirillum rubrum, and Novosphingobium
aromaticivorans) generated using 16S rRNA sequences and the maxi-
mum likelihood method (12).
7444 CONLAN ET AL. APPL.ENVIRON.MICROBIOL.

a random data simulation as described by McCue and cowork-
ers (30). By these critical-value criteria, over half of the motifs
(2,722) were marginally significant (P⬍0.2); of those, 442
were highly significant (P⬍0.01). The full set of phylogeneti-
cally footprinted motifs, along with significance criteria, can be
viewed online (http://bayesweb.wadsworth.org/prokreg.html).
Clustering predicted sites into regulons. A BMC algorithm
(38) was used to infer regulons from the collection of motifs
found by phylogenetic footprinting. After applying filtering
heuristics (see Materials and Methods) to the 2,722 motifs
described above, we identified 1,730 motifs for clustering. At
the selected level of statistical significance (P⬍0.2), many of
these 1,730 motifs were only marginally significant. However,
we have found that the noise introduced into the clustering
procedure by false-positive motif predictions has little effect on
true regulons; specifically, the false-positive motif predictions
do not join clusters reproducibly (L. A. McCue, unpublished
data). This is supported by the observation that only 472 of the
input motifs reproducibly joined a cluster and are reported as
members of regulons. Furthermore, we calculate a Bayes ratio
as a measure of cluster strength and have found true regulons
to rank among the highest scoring. The Bayes ratio is the ratio
of the probability of the data belonging in a cluster to the
probability of the data existing as separate motifs. Among the
101 clusters identified here, several of the high-scoring clusters
are supported by biochemical and genetic data from the liter-
ature as bona fide regulons; they are shown, along with se-
quence logos (41), in Table 2 and are described in detail below.
The clusters represent partial regulons, including only those
regulatory sites that could be detected by our cross-species
approach. For example, R. palustris genes lacking orthologs in
other species were never analyzed. It is also the case that the
heuristics used to filter out motifs prior to clustering may
eliminate some legitimate motifs. One way in which to address
this problem is to scan the complete set of intergenic regions
for a species, using position weight matrices built from the
clusters. This approach often results in the prediction of addi-
tional members of a regulon (48). We employed a rigorous
statistical algorithm based on Staden’s method (44) as imple-
mented by Neuwald and coworkers (34) to scan for additional
TFBSs in R. palustris. The algorithm yields a Pvalue for a motif
match that represents the probability that a site of equal or
greater strength would be found in a random data set of the
same size and composition. In scans of the set of all intergenic
regions of R. palustris with the cluster matrices, we identified
additional members of regulons at a Pvalue of ⬍0.05 (Table 2).
Phylogenetic footprinting, clustering, and scanning are sum-
marized in Fig. 2.
Nomenclature. The regulon descriptions presented here use
the following naming conventions. A motif is defined as a
collection of aligned DNA sequences (i.e., putative TFBSs)
from phylogenetic footprinting; motifs are simply given the
name of the R. palustris gene whose upstream intergenic se-
quence data were analyzed during motif prediction. A cluster
is defined as a collection of aligned motifs. Clusters are re-
ferred to as Even, Odd, or NonP depending on whether an
even-palindromic, odd-palindromic, or nonpalindromic model
best describes them. Each cluster also has a numerical desig-
nation that is produced during the clustering process; it is used
here solely as a unique identifier (e.g., Even-239). Annotated
gene functions were extracted from the published R. palustris
CGA009 genome (24). Eight of the clusters have particular
relevance to R. palustris biology and are described below.
OhrR cluster. Cluster Even-627 was the highest-scoring
cluster by the Bayes ratio criterion. Two of the four genes in
the cluster (ohr and rpa4101) are annotated as organic hy-
droperoxide resistance genes. These two R. palustris genes are
homologous to the ohr gene from Xyella fastidiosa, which en-
codes a thiol-dependent peroxidase that decomposes organic
hydroperoxides (5). R. palustris Ohr and RPA4101 are 60%
and 45% identical, respectively, to X. fastidiosa Ohr at the
protein level and have two conserved cysteines that are re-
quired for peroxidase activity. rpa4101 forms an operon with
rpa4102 and rpa4103, which, respectively, encode a MarR fam-
ily TF and a possible glutathione S-transferase. One of the
remaining genes in the cluster, rpa0828, is annotated as a
MarR family TF.
Organic hydroperoxide resistance genes are regulated by
OhrR in Xanthomonas campestris. OhrR is a MarR family TF
that senses oxidative stress through a single conserved cysteine
residue; this cysteine, when oxidized, disrupts DNA binding
(36). An amino acid sequence alignment of X. campestris
OhrR, RPA0828, RPA4102, and orthologs from B. japonicum,
B. suis, and H. neptunium shows that all contain the critical
reactive cysteine surrounded by a number of other well-con-
served residues (Fig. 3A). Additionally, it is interesting that the
ohr and rpa0828 genes in R. palustris are syntenic with ohr and
ohrR in X. campestris. Specifically, the rpa0828 gene is imme-
diately upstream of and on the same strand as ohr, with a
regulatory site predicted in each upstream region. In contrast,
among B. japonicum,B. suis, and H. neptunium, the ohr or-
thologs are divergently transcribed from the ohrR orthologs,
with a regulatory site located between them (Fig. 3B). These
results suggest that RPA0828 is likely the cognate TF of the
regulon, although RPA4102 is a probable paralog of RPA0828
and could provide redundancy in the regulatory circuit.
FixK
2
and NnrR clusters. The six motifs that made up clus-
ter Even-623 have a strongly conserved palindromic sequence;
when this cluster model was used to scan the database of all
R. palustris intergenic regions, 12 additional sites were identi-
fied (Table 2). Several of the genes in this expanded regulon
have predicted functions related to respiration, specifically, in
cytochrome assembly and porphyrin biosynthesis. The motif
model for this cluster (TTGA-N
6
-TCAA) resembles the
known binding sequence for the E. coli TF, Fnr (TTGAT-N
4
-
ATCAA) (17), a global regulator of anaerobic respiration. In
B. japonicum, nitrogen fixation genes and genes for anaerobic
or microaerobic metabolism, including the types of the genes
found in this cluster, are controlled by a Crp/Fnr family TF,
FixK
2
(14, 33), which is regulated by the FixLJ two-component
system. It is likely that R. palustris employs a regulatory circuit
similar to the B. japonicum FixLJ-FixK
2
cascade and that this
cluster corresponds to the R. palustris FixK
2
regulon. This
assertion is supported by the presence of an intact FixLJ two-
component system in R. palustris. In addition, when the E. coli
Fnr protein sequence is used to search the R. palustris pro-
teome, FixK
2
is returned as the top hit and shows 68% identity
in the helix-turn-helix region, supporting the observed similar-
ity in binding sites. The presence of three TFs in the cluster
(rpa2339,rpa1015, and rpa1090) may thus indicate the exis-
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TABLE 2. Selected clusters of phylogenetically footprinted motifs
ID
a
,TF
b
ABR
c
Logo
d
Cluster
e
Dscan
e,f
a
ID, cluster identifier.
b
Predicted TF.
c
ABR, average Bayes ratio.
d
Cluster logo.
e
Divergently transcribed genes and operons are separated by a slash. Operons are shown as follows. A dash indicates an inclusive range of gene
names. Large operons are denoted as a range between the first and last genes. A superscript indicates the number of genes in each operon.
f
Additional members of the regulon found by Dscan.
7446 CONLAN ET AL. APPL.ENVIRON.MICROBIOL.

tence of downstream regulatory circuits and possible intercon-
nections with other pathways.
Another Crp/Fnr family TF in B. japonicum, NnrR, is reg-
ulated as part of the FixLJ-FixK
2
cascade and controls genes
involved in response to nitric oxide and related free radicals
(31). Predicted motifs upstream of the nitric oxide-regulated
norCB and norE-rpa1454 operons formed a separate cluster
(Even-592), with an average Bayes ratio of 7.779, and are likely
regulated by NnrR. This nitric oxide regulatory cluster was
used as a model for Dscan, detecting the nosRZDFYLX-
rpa2067 operon that is involved in nitric oxide uptake. The fact
that FixK
2
and NnrR are closely related members of the same
TF family is reflected by the high degree of similarity between
their binding site consensus sequences (Table 2).
PpsR cluster. Cluster Even-602 is made up of genes involved
in pigment synthesis and photocenter assembly. When the
cross-species motif was used as a model for Dscan, several
additional motif sites were predicted, including sites upstream
of two LH
2
complexes (pucB
e
A
e
and pucB
d
A
d
) and an addi-
tional pigment synthesis gene, crtI. One interesting member of
the expanded regulon is cbbY, which encodes a haloacid deha-
logenase-like hydrolase thought to be involved in the Calvin-
Benson-Bassham cycle (15). However, statistically significant
sites were not found upstream of any additional genes of the
Calvin-Benson-Bassham cycle.
Transcription of the photosynthetic genes in R. sphaeroides
(37) and in the unusual photosynthetic Bradyrhizobium sp.
strain ORS278 (16, 23) is controlled by the product(s) of the
ppsR gene(s). Both R. palustris and Bradyrhizobium sp. strain
ORS278 have two forms of PpsR (encoded by ppsR
1
and
ppsR
2
), while R. sphaeroides has only one (encoded by ppsR).
The binding site consensus for PpsR from R. sphaeroides 2.4.1
and Rhodobacter capsulatus (3), TGT-N
10
-ACA, matches the
sequence logo for this cluster, suggesting that one (or both) of
the R. palustris PpsR isoforms is likely the cognate TF for this
regulon.
NtrC cluster. Cluster Odd-529 is made up of two nitrogen-
regulated genes, glnB and nifR
3
. The motifs upstream of these
two genes are present in all eight species, indicating that they
are part of a regulatory circuit that is highly conserved among
the ␣-proteobacteria. The glnB gene encodes a P
II
nitrogen-
regulatory protein and is located upstream of the glutamine
synthase gene, glnA. The nifR
3
gene forms an operon with
ntrBC, which encodes a two-component system. When the clus-
ter model was used to scan the complete set of R. palustris
intergenic regions, an additional site was found upstream of a
second P
II
nitrogen regulatory gene, glnK
2
.
Given that the TF NtrC is a member of this regulon (in the
nifR
3
-ntrBC operon), it was an obvious candidate for the cog-
nate, autoregulatory TF. There are three lines of evidence that
support this hypothesis. The first is that NtrC sites have been
found in the promoters of both glnB and nifR
3
in R. capsulatus
(4). Secondly, two closely spaced copies of the NtrC site are
found by Dscan in the promoters of glnB,nifR
3
, and glnK
2
; this
tandem arrangement is a feature of NtrC-regulated promoters,
which are cooperatively bound by NtrC octamers (25, 40).
Finally, when DNase I-footprinted NtrC sites from R. capsu-
latus (4, 27) are aligned, the resulting sequences have a pattern
similar to that shown in Table 2 for this cluster, specifically,
conserved GC pairs separated by an 11-bp AT-rich region.
FIG. 2. Flowchart of phylogenetic footprinting, clustering, and scan-
ning. The phylogenetic-footprinting procedure is shown above the dashed
line, and the clustering procedure is shown below. Input data and inter-
mediate data sets are shown in boxes. Operations are shown in rounded
boxes.
VOL. 71, 2005 PREDICTION OF RHODOPSEUDOMONAS PALUSTRIS REGULATION 7447

FlbD cluster. Cluster Odd-740 is made up of motifs associ-
ated with flagellar synthesis genes, as well as a putative adenine
methyltransferase. Since DNA methylation and flagellar as-
sembly are both regulated in a cell-cycle dependent manner in
C. crescentus (28), it seemed likely that RPA1026 (the adenine
methyltransferase) could have a cell cycle-regulatory function.
In fact, a BLAST search identified rpa1026 as an ortholog of
ccrMI, the cell cycle methylase of C. crescentus. The ccrM gene
is widely distributed among the ␣-proteobacteria and has been
shown to be essential for the viability of C. crescentus (45).
Orthology with ccrMI and membership in the flagellar cluster,
combined with evidence that ccrMI is under the control of a class
II flagellar promoter in C. crescentus (46), support the hypothesis
that RPA1026 is part of the flagellar cluster and has a cell cycle-
regulatory function. The likely cognate TF for this cluster is FlbD,
which is encoded in the fliFG-rpa1266-fliY-flbD operon and is
known to be a negative regulator of its own transcription. In
addition, the fliF motif found by phylogenetic footprinting and
included in this cluster, contains a C. crescentus site (GGTAAA
TCCTGCC) that has been shown to be bound by FlbD (32).
LexA cluster. Cluster NonP-396 contains motifs upstream of
both recA and lexA from seven of the eight species used for
phylogenetic footprinting. These genes encode proteins in-
volved in the SOS pathway for repairing DNA damage (52).
When we used this cluster motif as a model to scan the entire
set of R. palustris intergenic regions, we identified five addi-
tional statistically significant sites, including sites upstream of
ssb and RadA, which are also involved in responding to DNA
damage. The sequence logo for this cluster matches the pre-
viously reported nonpalindromic LexA binding motif de-
scribed for R. palustris and other ␣-proteobacteria (9, 13).
Sigma
54
cluster. Cluster NonP-401 is made up primarily of
motifs occurring upstream of genes involved in nitrogen and
hydrogen metabolism. The anfHDGK operon encodes the
alternate nitrogenase, and the hupSLC operon encodes a
hydrogenase. When this cluster motif was used as a model
for Dscan, a significant site was found upstream of vnfH,
which encodes a vanadium-dependent nitrogenase subunit.
Since the predicted regulon included subunits for two of the
three R. palustris nitrogenases, as well as a nitrogen-regula-
tory protein (glnK
2
), we compared the logo for the cluster
with the motifs for known nitrogen regulators. The results
suggest that this motif is bound by the alternative sigma
factor ␴
54
, which is known to regulate many genes involved
in nitrogen assimilation and metabolism (54).
A novel inverted repeat. Two high-scoring (by the Bayes
ratio criterion) Even clusters had a strongly conserved central
CCGG sequence and were composed largely of motifs consist-
ing of sites from only R. palustris and B. japonicum (Fig. 4).
When used in Dscan, both of the cluster models identified
dozens of intergenic regions in R. palustris with significant
matches. Examination of the sequences in these clusters re-
vealed that they were part of a conserved inverted repeat of the
form ATTCCGGG-N
10–52
-CCCGGAAT. The 8-bp ends are
identical in all copies of the repeat and are separated by an
unconserved, but typically palindromic, region of variable
length. The genome sequence of R. palustris was analyzed
using simple pattern matching for occurrences of this repeat
pattern. Repeats were found almost exclusively in intergenic
regions at the 3⬘ends of gene sequences. Specifically, these
repeats were found with high frequency in intergenic regions
between genes transcribed in the same direction (n⫽124; 69
intergenic regions) and intergenic regions between conver-
gently transcribed genes (n⫽107; 63 intergenic regions) but
occurred rarely in intergenic regions separating divergently
transcribed genes (n⫽5) or in coding regions (n⫽7). The
other species in this study were analyzed for the presence of
the repeats. The B. japonicum genome was found to have a
similar density of this repeat, with a similar preference for
intergenic regions at the 3⬘ends of genes. The other six species
do not carry any copies of this repeat, consistent with our
observation that these species did not contribute to the motifs
making up the clusters represented in Fig. 4A.
The composition of the R. palustris repeat was examined in
greater detail. The length of the region separating the 8-bp
bounding sequences varied from 10 to 52 bases; however, the
distribution of lengths was not uniform. Total repeat lengths of
32 bases, 36 bases, and 37 bases were the most common,
accounting for 71% of the repeats. The central variable regions
were analyzed using the palindrome tool from EMBOSS (39);
89% of the sequences were found to be palindromic. Random-
ized data were also analyzed, and fewer than 10% of these
sequences were found to be palindromic when the same meth-
odology was used. Thus, conservation of the central region may
FIG. 3. Partial alignment of OhrR orthologs. (A) Alignment of the region surrounding the oxidation-sensitive cysteine residue (in boldface)
from X. campestris OhrR and its orthologs. Gene names are noted except in the case of the ortholog from H. neptunium, as this species’ genome
is not yet annotated. (B) Arrangement of the ohr (black arrows) and ohrR (dark-gray arrows) orthologs for the corresponding species in panel A.
Motifs found upstream of genes are shown as hatched boxes.
7448 CONLAN ET AL. APPL.ENVIRON.MICROBIOL.

be based on secondary structure (i.e., hairpin formation) rather
than primary sequence. This is supported by the observation that
many of the repeats examined are predicted to fold into a stem-
loop structure composed of a perfectly conserved 8-bp helix, fol-
lowed by a bulge and then a variable-sequence hairpin (Fig. 4C).
DISCUSSION
In the present work, we have described a cross-species anal-
ysis of transcription regulation in R. palustris. In previous phy-
logenetic-footprinting studies, using E. coli as the reference
species and several additional ␥-proteobacteria, we were able
to compare our findings to a large number of DNase I-foot-
printed TFBSs from the literature (29, 30). Since little infor-
mation is available regarding experimentally identified TFBSs
in R. palustris, we focused on clustering the motifs to infer
regulons, rather than investigating motifs individually. When
the ␥-proteobacterial phylogenetic-footprinting data were clus-
tered (38), the majority of clusters with an average Bayes ratio
of ⱖ8 were identified as regulons that had been described in
the literature. Among 101 R. palustris clusters, 63 had an av-
erage Bayes ratio of ⱖ8. The clustering results described here,
however, differ from the ␥-proteobacterial results in that we
chose motifs for clustering using a liberal cutoff for statistical
significance (P⬍0.2), instead of the more stringent cutoff (P⬍
0.05) used previously (38), and allowed the clustering algorithm
to identify those motifs that reproducibly formed clusters instead
of identifying a single near-optimal clustering solution. Although
the two studies are not directly comparable, we expect that many
of the 63 high-scoring (average Bayes ratio, ⱖ8) clusters from this
study merit investigation. All 101 reproducible clusters, as well as
the individual motifs with their associated statistical significances,
are available at http://bayesweb.wadsworth.org/prokreg.html.
FIG. 4. Features of an R. palustris inverted repeat. (A) Two sequence logos (41) from clusters composed primarily of inverted repeats.
(B) Aligned examples of repeats that are 32, 36, and 37 bp in total length from R. palustris intergenic regions. Gene names refer to the flanking
genes. Internal palindromic regions are shown in gray boxes (exceptions are in lowercase). The CCGG sequence contributing to the central region
of the logos in panel A is found in the flanking inverted repeats. (C) Predicted secondary structures (lowest free energy and most probable) for
four repeats having different total lengths. The first three structures correspond to R. palustris sequences labeled in panel B. The fourth structure
is an example of one of the largest repeat sequences (68 bp) detected. All structures were inferred using Sfold (6).
VOL. 71, 2005 PREDICTION OF RHODOPSEUDOMONAS PALUSTRIS REGULATION 7449

The comparative-genomics approach. Some caveats associ-
ated with phylogenetic-footprinting approaches should be con-
sidered, particularly when choosing targets for experimental
validation. During either phylogenetic footprinting or scanning
of intergenic regions for additional regulon members, TFBSs
may be predicted between divergently transcribed genes. Fre-
quently, it is not possible to determine which of the divergently
transcribed genes is regulated by the TFBS(s). This is the case
during phylogenetic footprinting, when synteny of the two
genes in question is conserved among several of the species
included in the data set. In addition, when intergenic regions of
R. palustris are scanned, the identity of the regulated gene(s)
cannot be inferred. In cases where the identity of the regulated
gene is unclear, both divergently transcribed genes were listed
in Table 2. Another current limitation of the phylogenetic-
footprinting approach used here is that it does not reliably
detect riboswitches or attenuators; the implications of this are
discussed below, in the context of the methionine biosynthesis
genes. Finally, phylogenetic footprinting addresses only the
regulation of genes that have an ortholog in at least one of the
other species, have a similar operon structure, and are regu-
lated by a TF found in at least one of the other species.
Rather than focus on individual motifs from phylogenetic
footprinting, we clustered the footprinted motifs into putative
regulons. This allowed us to make inferences about their reg-
ulatory roles. In addition, putative regulons were expanded by
scanning R. palustris intergenic regions. The results presented
in this report highlight three advantages of this approach: (i)
the ability to make cis-trans connections, (ii) the ability to
discriminate between members of a TF family, and (iii) the
ability to make predictions in the absence of prior information.
Tan et al. (47) have shown that the presence of autoregula-
tory sites in the promoters of TFs provides key information to
link a particular motif to its cognate TF (i.e., cis-trans connec-
tions). In addition, a network analysis of E. coli transcription
regulation has shown that ⬃50% of TFs are autoregulated
(42). In the clusters described in Results, four of the eight
contained sites upstream of the likely cognate TF (RPA0828/
RPA4102, NtrC, FlbD, and LexA). While there is evidence
in the literature of autoregulatory sites for NtrC, FlbD, and
LexA among the ␣-proteobacteria, the connection between
RPA0828/RPA4102 and the organic hydroperoxide resistance
gene cluster was made de novo by phylogenetic footprinting
and clustering.
The R. palustris genome is predicted to encode 15 Crp/Fnr
family TFs (24). Some of these family members likely bind to
similar TFBSs due to homology in their DNA-binding do-
mains. It was therefore possible that motif clustering could
produce “mixed” regulons containing motifs upstream of genes
regulated by two or more closely related TFs. It was known
from clustering E. coli motifs (38) that the BMC algorithm
could correctly separate motifs bound by Crp and Fnr, which
have similar binding site consensus sequences. In this study,
the separation of FixK
2
- and NnrR-regulated genes into dis-
tinct clusters further demonstrates the ability of the BMC
algorithm to detect subtle differences between motifs. Never-
theless, we cannot exclude the possibility that some of our
clusters may be mixed regulons.
An advantage of phylogenetic footprinting is that no prior
knowledge of the regulatory network is required. This is useful,
since regulatory-network information from other species can,
in some cases, be misleading. For instance, the motifs that
make up the R. palustris LexA cluster identified in this study
are upstream of genes likely involved in the SOS response
(lexA,recA, and ssb), as they are in E. coli. However, the LexA
binding motif identified here, de novo, is distinct from that of
E. coli. Specifically, in the ␣-proteobacteria, LexA binds to a
completely different site (GTTC-N
7
-GTTC) than that of the
␥-proteobacteria (CTG-N
10
-CAG) (13).
We also detected a novel repeat element in R. palustris and
B. japonicum with features reminiscent of both a transcrip-
tional terminator and a mobile DNA element. Specifically, the
repeat occurs preferentially at the 3⬘ends of gene sequences,
suggesting a role in terminating transcription, but also has
perfectly conserved flanking sequences like those observed for
mobile DNA elements (e.g., a transposon). Given the nonuni-
form distribution of the repeat in the R. palustris genome, we
hypothesize that it may be involved in a novel type of tran-
scription termination or perhaps mRNA stability.
Regulation in the ␣-proteobacteria compared to the ␥-pro-
teobacteria. Our results illustrate some differences in transcrip-
tion regulation between the ␥-proteobacteria and ␣-proteobac-
teria. For example, there were a number of regulons that we
might have expected to detect, based on our previous experi-
ence with phylogenetic footprinting and clustering in the
␥-proteobacteria and a general knowledge of prokaryotic gene
regulation. However, important amino acid biosynthetic regu-
latory clusters, such as those for methionine, tyrosine, and
tryptophan, were noticeably absent. In fact, orthologs of the
E. coli TFs that regulate these pathways (metJ,tyrR, and trpR)
are missing in R. palustris, despite the presence of complete
metabolic pathways for the synthesis of these amino acids.
Because the motif predictions described here were made based
on sequence conservation alone, our cross-species method
does not require any similarity between the E. coli and
R. palustris regulatory networks. However, it is instructive to
consider how the transcriptional regulation of these pathways
differs in these two species and why regulons, perhaps unique
to the ␣-proteobacteria, were not predicted de novo by phylo-
genetic footprinting and clustering.
In E. coli, the metA,metC,metE,metF, and metJ genes are
regulated by the TF MetJ. Orthologs of the methionine syn-
thesis genes (metA,metC,metE, and metF) are present in
R. palustris, but a metJ ortholog is not present. In addition, no
common motif is found upstream of the four biosynthesis
genes, as would be expected if a single TF regulated them. The
metE gene, however, has eight predicted sites that are con-
served across four of the species in our study. Similarly, metF
has seven distinct sites predicted that are conserved across
seven or eight species and that cover 142 bp of the 410 bp in
the R. palustris metF intergenic region. These data indicate
extensive regions of conservation in the upstream regions of
these genes, beyond that which might be expected for straight-
forward TF-mediated regulation. We found that seven of the
eight sites upstream of metE in R. palustris overlap a predicted
riboswitch for cobalamine (Rfam) (19). A riboswitch has not
been predicted upstream of metF; however, given the limited
data available for the prediction of riboswitches, this is perhaps
not surprising. These data indicate that the R. palustris methi-
onine biosynthesis pathway may be at least partially controlled
7450 CONLAN ET AL. APPL.ENVIRON.MICROBIOL.

by riboswitches, as it is in Bacillus subtilis (11, 53), highlighting
the need to extend the phylogenetic-footprinting technique to
more efficiently detect riboswitches.
In E. coli, aromatic amino acid biosynthesis is coordinated
by TrpR and TyrR. The trpLEDCBA operon encodes proteins
involved in tryptophan biosynthesis and is regulated by the TF
TrpR and by attenuation. The orthologous genes in R. palustris
are separated into three operons: trpG (a trpE ortholog),
rpa2889-trpDC-maoC-rpa2893, and trpFBA. Few motifs were
found cross-species for these operons, and no motifs were
found when the three upstream regions from R. palustris alone
were analyzed (data not shown). Similarly, genes encoding
the proteins of the tyrosine biosynthesis pathway had few mo-
tifs predicted cross-species. The absence of TrpR and TyrR
orthologs, as well as the lack of unique motif predictions in the
intergenic regions controlling the tryptophan and tyrosine bio-
synthesis pathways, suggests that the regulation of these path-
ways has diverged from the E. coli paradigm and may not
depend on a classical TF-mediated mechanism.
Conclusions. The emphasis of this work was on improving
our understanding of R. palustris biology. Specifically, we
sought to describe the regulatory network of this metabolically
versatile species and to make functional predictions that will be
of use to the R. palustris research community. This paper pro-
vides a description of the most striking patterns that emerged
from our genome scale study. From a molecular-biology per-
spective, many of the predictions made in this study lead di-
rectly to hypotheses which can be tested experimentally. For
example, interruption of rpa0828 and rpa4102, the putative
ohrR orthologs, is predicted to result in constitutive expression
of the organic hydroperoxide resistance genes. Interruption of
rpa1026, the ccrM ortholog, is likely to have strong effects on
cell cycle control and could even be a lethal mutation, as it is
in C. crescentus. Our results suggest that the inverted repeat
described in this study serves a regulatory role that may involve
a stem-loop structure; the first step in investigating this hypoth-
esis is to determine whether the repeat is transcribed, perhaps
using nucleic acid hybridization. From a computational-biology
perspective, this work demonstrates the applicability of our
methods to nonmodel organisms; it illustrates the importance
of not only developing computational methods, but also apply-
ing them to pertinent bacterial species.
ACKNOWLEDGMENTS
We thank the Computational Molecular Biology and Statistics Core
Facility at the Wadsworth Center, Lee Newberg for assistance with
species selection, William Thompson for assistance with the Gibbs
sampler, Michael Palumbo for assistance with the BMC software, and
Caroline Harwood for comments on the manuscript. We also thank
The Institute for Genome Research and Joint Genome Institute for
making sequence data available before completion.
This research was supported by Department of Energy grants DE-
FG02-01ER63204 and DE-FG02-04ER63942 to C.L. and L.A.M. and
NIH grant R01HG01257 to C.L.
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