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Polycyclic Aromatic Hydrocarbon-Metabolic Network in 1
Mycobacterium vanbaalenii PYR-1 2
3
Ohgew Kweon,1‡ Seong-Jae Kim,1‡ Ricky D. Holland,2 Hongyan Chen,3 Dae-Wi Kim,1 4
Yuan Gao,2 Li-Rong Yu,2 Songjoon Baek,4 Dong-Heon Baek,5 Hongsik Ahn,3 and Carl E 5
Cerniglia1* 6
7
Division of Microbiology, National Center for Toxicological Research/US FDA, 8
Jefferson, Arkansas 72079,1 Division of Systems Biology, National Center for 9
Toxicological Research/US FDA, Jefferson, Arkansas 72079,2 Department of Applied 10
Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794,3 11
Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, NIH, 12
Bethesda, Maryland 20814,4 and Department of Oral Microbiology and Immunology, 13
School of Dentistry, Dankook University, Chonan 330-714, Republic of Korea5 14
15
Running title: POLYCYCLIC AROMATIC HYDROCARBON-METABOLIC 16
NETWORK IN MYCOBACTERIUM VANBAALENII 17
*Corresponding author 18 Division of Microbiology 19 NCTR/US FDA 20 3900 NCTR Road 21 Jefferson, AR 72079 22 phone: 870-543-7341 23 fax: 870-543-7307 24 email: [email protected] 25 26
‡These authors contributed equally to this work 27
28
Copyright © 2011, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved.J. Bacteriol. doi:10.1128/JB.00215-11 JB Accepts, published online ahead of print on 1 July 2011
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ABSTRACT 1
This study investigated a metabolic network (MN) from Mycobacterium 2
vanbaalenii PYR-1 for polycyclic aromatic hydrocarbons (PAHs), from the perspective 3
of structure, behavior, and evolution, in which multilayer omics data are integrated. 4
Initially, we utilized a high throughput proteomic analysis to assess the protein 5
expression response of M. vanbaalenii PYR-1 to seven different aromatic compounds. A 6
total of 3,431 proteins (57.38% of the genome-predicted) were identified, which included 7
160 proteins that seemed to be involved in the degradation of aromatic hydrocarbons. 8
Based on the proteomics data and the previous metabolic, biochemical, physiological, 9
and genomics information, we reconstructed the experiment-based systems-level PAH-10
MN. The structure of PAH-MN, with 183 metabolic compounds and 224 chemical 11
reactions, has a typical scale-free nature. Behavior and evolution of the PAH-MN reveals 12
a hierarchical modularity with funnel effects in structure/function and intimate 13
association with evolutionary modules of the functional modules, which are the ring-14
cleavage process (RCP), side chain process (SCP), and central aromatic process (CAP). 15
The 189 commonly upregulated proteins in all aromatic hydrocarbon treatments provide 16
insights into the global adaptation to facilitate the PAH metabolism. All together, our 17
study provides the hierarchical viewpoint from genes/proteins/metabolites to the network 18
via functional modules of the PAH-MN, equipped with the engineering-driven 19
approaches of modularization and rationalization, which may expand our understanding 20
of the metabolic potential of M. vanbaalenii PYR-1 for bioremediation applications. 21
22
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INTRODUCTION 1
With the 2010 Deepwater Horizon BP oil spill in the Gulf of Mexico 2
(http://www.epa.gov/BPSpill), concerns have been raised on the impact of polycyclic 3
aromatic hydrocarbons (PAHs) on the environment. PAHs are a diverse class of organic 4
compounds with two or more fused benzene rings (4). These compounds are highly 5
hydrophobic and not easily bioavailable to microorganisms for degradation and they pose 6
a significant toxicological risk to human and environmental health (4). Microbial 7
activities represent one of the primary processes by which PAHs are eliminated from the 8
environment (4). 9
Mycobacterium vanbaalenii PYR-1, originally isolated from oil-contaminated 10
estuarine sediment, was the first bacterium reported to degrade high-molecular-weight 11
(HMW) PAHs with four or more fused benzene rings (10, 18). Since it has the ability to 12
mineralize or degrade various kinds of PAHs, such as phenanthrene, anthracene, 13
fluoranthene, pyrene, benzo[a]pyrene, benz[a]anthracene, and 7,12-14
dimethylbenz[a]anthracene (10, 11, 15-17, 24, 34-38), strain PYR-1 has been extensively 15
studied as a prototype organism to elucidate pathways (19) and has been used to 16
remediate PAH-contaminated soils (31). Recently, with the completion of the genome 17
sequence of M. vanbaalenii PYR-1, efforts to elucidate the molecular background for the 18
metabolism of PAHs have been initiated (21, 22, 26). 19
Recently, a global biodegradation network has been reconstructed from public 20
resources, independent of the microbial hosts (40). In this study, we conducted research 21
on the metabolism of PAHs by M. vanbaalenii PYR-1 from the standpoint of a metabolic 22
network (MN). We used high-throughput 1-dimensional (1D) gel electrophoresis coupled 23
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to nanoLC-MS/MS approaches to yield large-scale proteome data for the response of 1
strain PYR-1 to 7 aromatic hydrocarbons, phthalate, fluorene, acenaphthylene, 2
anthracene, phenanthrene, pyrene, and benzo[a]pyrene, which well-represent 3
characteristics of aromatic hydrocarbons degraded by strain PYR-1 in terms of their 4
structure and metabolism. The whole-cell proteome results were then combined with 5
previous metabolic, biochemical, physiological, and genomic information (20-22, 29) to 6
reconstruct an experimental evidence-driven PAH-MN for M. vanbaalenii PYR-1. We 7
analyzed the network from the viewpoints of structure, behavior, and evolution, which 8
provided a system-wide perspective on biodegradation of PAHs and insights on how M. 9
vanbaalenii PYR-1 satisfies the demands for unusual metabolic capability toward HMW 10
PAHs. 11
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MATERIALS AND METHODS 1
Bacterial strain, chemicals, and culture conditions. M. vanbaalenii PYR-1 2
(DSM 7251) was used in this study. Phthalate, fluorene, acenaphthylene, anthracene, 3
phenanthrene, pyrene, and benzo[a]pyrene were purchased from Sigma-Aldrich (St. 4
Louis, MO). A 100-ml of the initial culture was grown for a week in 7H9 Middlebrook 5
broth. The culture was washed, resuspended, and transferred into 250-ml flasks 6
containing 50 ml of phosphate-based minimal (PBM) medium (23) supplemented with 7
0.5% sorbitol and further incubated with shaking at 200 rpm for 5 days at 28oC. Aromatic 8
hydrocarbons were dissolved in dimethyl sulfoxide (DMSO) and added to the flasks 9
(final concentration 25 µM). The cells were induced two more times by repeated 10
additions of aromatic hydrocarbons at 24 h intervals. The control culture had the same 11
incubation conditions except that only DMSO was added. 12
SDS-PAGE and in-gel digestion. After 6 h of the third induction, the cells were 13
harvested by centrifugation. Protein extracts were prepared by homogenization using 14
glass bead as described previously (22). Twenty µg of each sample was resolved on a 1.5 15
mm thick, 4-12% Bis-Tris SDS gel (Invitrogen, Carlsbad, CA) and the separation was 16
duplicated. The SDS-PAGE gels were visualized by staining with SimplyBlue 17
(Invitrogen). Each lane was cut into 20 bands and the duplicate bands were then 18
combined. The bands were destained overnight in 25 mM NH4HCO3/50% acetonitrile. 19
After destaining, the gel bands were digested with 10 ng/µL of trypsin (Promega, 20
Madison, WI) overnight at 37°C and the peptides were extracted by 70% acetonitrile and 21
5% formic acid with sonication. The extracted peptides were lyophilized to dryness. 22
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Nanoflow LC-MS/MS analysis. The peptides extracted from each gel band were 1
analyzed by reversed-phase nanoflow LC-tandem mass spectrometry (RP nanoLC-2
MS/MS). RP nanoLC-MS/MS was performed using an Agilent 1200 nanoLC system 3
(Agilent Technologies) coupled online to a linear ion trap mass spectrometer (LTQ XL, 4
Thermo Electron). Reversed-phase separation was performed using a 75 µm i.d. × 360 5
µm o.d. × 9 cm long fused silica capillary column (Polymicro Technologies, Phoenix, 6
AZ) that was slurry-packed with Jupiter 5 µm, 300 Å pore size C18 silica-bonded 7
stationary phase (Phenomenex, Torrance, CA). Each sample was dissolved in 20 µL of 8
formic acid and 5 µL was injected onto the RP column. During sample loading, the 9
column was held for 30 min with 98% of solvent A (0.1% formic acid in water, v/v). 10
Peptides were eluted at a flow rate of 0.25 µL/min using a step linear gradient of 2%-42% 11
solvent B (0.1% formic acid in 100% acetonitrile, v/v) for 40 min and 42%-98% solvent 12
B for 10 min followed by 98% solvent B for 5 min. The mass spectrometer was operated 13
in a data dependent mode, in which each full MS scan was followed by 7 MS/MS scans, 14
where the 7 most abundant peptide molecular ions were dynamically selected from the 15
prior MS scan for collision-induced dissociation (CID) using a normalized collision 16
energy of 35%. 17
Protein identification and quantitation. The raw MS/MS data were searched 18
using SEQUEST, running under BioWorks (Rev. 3.3.1 SP1) (Thermo Electron, San Jose, 19
CA), against the M. vanbaalenii PYR-1 protein database with the addition of a PhtAc 20
protein sequence to identify peptides. Peptide mass tolerance of 2 Da and fragment ion 21
tolerance of 1 Da were allowed with tryptic specificity allowing two missed cleavages. 22
SEQUEST criteria were Xcorr ≥ 1.7 for [M+H]1+ ions, ≥ 2.5 for [M+2H]2+ ions, and ≥ 23
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3.2 for [M+3H]3+ ions and P ≤ 0.01 for identification of fully tryptic peptides. False 1
positive rate in peptide identification was evaluated to be less than 1% by SEQUEST 2
search of the database comprising forward and reversed protein sequences. The peak 3
areas of identified peptides were calculated using the BioWorks’ PepQuan module 4
(Thermo Electron, San Jose, CA), by which peak areas were integrated from their 5
extracted ion chromatograms (XIC) using a minimum intensity threshold of 50,000 6
counts, mass tolerance of 1.5 Da, and smoothing point of 5. Each peptide area was 7
normalized to the total area of all the peptides identified from the control sample or each 8
treatment and expressed as ppm. The areas of unique peptides from the same protein 9
were summed to represent the abundance of that protein. 10
Bioinformatic and statistical analysis. Some of the proteins identified in the 11
proteome were further determined with respect to function using a BlastP search. 12
Subcellular localizations of identified proteins were predicted using PSORTb ver. 2.0.4 13
(http://psort.org). The logP values for water solubility of the compounds were calculated 14
by MarvinSketch (version 4.1.8). Protein relative expression levels against control were 15
normalized by taking the logarithm base 2 form: 2
expression(treatment)log
expression(control)
. If the 16
expression in controls is not detectable but it is measurable in treatments, we arbitrarily 17
set the expression level to be 5; on the other hand, if the expression level in treatments is 18
not detectable, we set it to be -5. For the hierarchical clustering analysis with the average 19
linkage method, the dissimilarity measurement between proteins was calculated as (1 – r), 20
where r is the pairwise Pearson correlation of protein expression profiles. Afterwards, 21
the number of clusters c was determined by a combined assessment with external criteria 22
2R , pseudo F and pseudo 2T statistics, which are all based on between and within cluster 23
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sum of squares. 2 within between
total total
1SS SS
RSS SS
= − = is related to the proportion of the total 1
variation explained by clusters, where withinSS stands for within sum of square, betweenSS 2
stands for between sum of square, and totalSS stands for total sum of square. 3
between
total
/ ( 1)Pseudo F=
/ ( )
SS c
SS n c
−
− mimics the conventional F statistic, where n is the total 4
number of observations. Compared with the other two criteria, 5
within_t within_r within_s2
within_r within_s
( )( 2)Pseudo T = r s
SS SS SS n n
SS SS
− − + −
+ monitors every step of fusion 6
more directly, where r and s denote two merged old clusters while t represents the 7
merged cluster. The pseudo F and pseudo 2T statistics that performed the best out of 8
thirty methods for estimating the number of clusters in the simulation study by Cooper 9
and Milligan (5) and Milligan and Cooper (33) served as criteria for determining the 10
number of clusters. Based on the clustering analysis, the proteins identified as Mvan 11
numbers were classified by the COG functions. The methods were implemented in R 12
(version 2.4.1, downloadable from http://cran.r-project.org). 13
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RESULTS 1
Proteome analysis. We identified between 2119 and 2380 proteins, which added 2
up to 3431 proteins in total (57.38% of the 5,979 genome-predicted proteins) from both 3
control sample and aromatic hydrocarbon-treated samples treated with phthalate, fluorene, 4
acenaphthylene, anthracene, phenanthrene, pyrene, and benzo[a]pyrene (Table 1; Table 5
S1 in the supplemental material). Briefly, as shown in Figure 1A, 1927 proteins were 6
shared among the control and seven treatments, with 93 and 1411 proteins identified only 7
in control and PAH-treated samples, respectively. Comparative analysis of protein 8
expression profiles showed that 2,358 proteins were upregulated more than 2-fold in 9
abundance under at least a single set of growth conditions, and among them, 189 proteins 10
were commonly more than 2-fold abundant in all aromatic hydrocarbon treatments (Table 11
S2 in the supplemental material). An overall comparison of protein expression profiles by 12
cluster analysis is shown in Figure 1B. Figure 1C shows the groupings of these 3,431 13
proteins according to the COG categories (45) (Table 1). Generally most of the COGs 14
showed similar percentages of detection in the proteome compared to those of protein 15
coding genes predicted from the genome of M. vanbaalenii PYR-1. Identified proteins 16
were also COG-classified based on the culture conditions (Fig. S1 in the supplemental 17
material). 18
Out of ~200 genes involved in the degradation of aromatic hydrocarbons, which 19
were previously identified in the 6.4 Mb genome of strain PYR-1 (21), about 160 genes 20
in this proteome study were expressed as proteins. Among them, 10 ring-hydroxylating 21
oxygenases (RHOs), 20 cytochrome P450 monooxygenases (CYPs), and 10 other 22
monooxygenases were variably expressed, dependent on the structural differences of the 23
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aromatic substrates. These enzymes were thought to be mostly involved in the initial step 1
of aromatic hydrocarbon oxidation. M. vanbaalenii PYR-1 incubated with pyrene 2
upregulated at least six RHOs, including NidAB (Mvan_0488/0487) and PhtAaAb 3
(Mvan_0463/0464), whereas only one RHO, PhtAaAb, was upregulated when incubated 4
with phthalate. These observations clearly indicated that the higher the molecular weight 5
and size of the substrate, the more initial oxidation reactions are required and the more 6
RHOs, of three different types (1, 26), are expressed. Interestingly, the phthalate 7
dioxygenase, PhtAaAb, was identified in all aromatic hydrocarbon-induced conditions 8
except for benzo[a]pyrene. The electron transport chain (ETC), PhtAc and PhtAd 9
(Mvan_0467), was identified in all cultural conditions, including the control. 10
Although many of their physiological roles are not known, the involvement of 11
CYPs in the initial oxidation of PAHs has been demonstrated in M. vanbaalenii PYR-1 12
(12). Expression of 20 CYPs out of 50 gene copies found in the genome of strain PYR-1, 13
together with the expression of epoxide hydrolases (Mvan_0521/4998), which are 14
required to form trans-dihydrodiols (12), further supported the possibility of alternative 15
reactions for the oxidation of aromatic hydrocarbons. They include two CYPs, CYP151 16
(Mvan_1848) and CYP150 (Mvan_4141), whose functions in oxidizing PAHs were 17
experimentally proposed (2). Another CYP, Mvan_2007, was identified as upregulated in 18
all induction conditions; its function should be further characterized. Several types of 19
monooxygenases, including flavin-containing monooxygenases, were also detected and 20
could function in a wide variety of biological processes. 21
In contrast, a relatively limited number of cis-dihydrodiol dehydrogenases and 22
ring-cleavage dioxygenases were identified. Among five dihydrodiol dehydrogenase 23
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genes in the genome of M. vanbaalenii PYR-1, two, Mvan_0466/0544, were detected as 1
proteins. However, since the specific activity of PhtB (Mvan_0466) is for 3,4-dihydroxy-2
3,4-dihydrophthalate (44), the dehydrogenase, Mvan_0544, is believed to cover most 3
dihydrodiol dehydrogenation reactions in strain PYR-1. This protein was upregulated by 4
all aromatic hydrocarbons except for phthalate and benzo[a]pyrene. M. vanbaalenii PYR-5
1 is able to support both intra- and extradiol enzymatic ring cleavage of catechol 6
derivatives (22, 29). Of the four identified ring-cleavage dioxygenases, PhdI (Mvan_0468) 7
and PcaG (Mvan_0560/0561) were previously shown to be involved in the ring cleavage 8
of 1-hydroxy-2-naphthoate and protocatechuate, respectively (22). The other two ring-9
cleavage dioxygenases, Mvan_0470 and Mvan_0545 appear to catalyze the fission of 10
catechols. These observations suggest that the dihydrodiol dehydrogenases and ring-11
cleavage dioxygenases have extremely broad substrate specificities. Alterations in the 12
abundance of a number of other important PAH metabolic enzymes were also observed, 13
such as hydrolases, hydratase-aldolases, decarboxylases, alcohol (or aldehyde)-14
dehydrogenases, and enzymes involved in the β-ketoadipate pathway (Tables S1 and S3 15
in the supplemental material). 16
Structure of the PAH-MN. (i) Reconstruction of PAH-MN. A PAH-MN was 17
reconstructed based on the PAH metabolites of M. vanbaalenii PYR-1 (Fig. 2A and 2B; 18
Fig. S2 and Table S3 in the supplemental material). An initial set of 183 PAH metabolites 19
identified from the metabolism of 10 aromatic hydrocarbons, which are biphenyl, 20
naphthalene, acenaphthylene, acenaphthene, anthracene, phenanthrene, pyrene, 21
fluoranthene, benz[a]anthracene, and benzo[a]pyrene by strain PYR-1 (15, 22, 24, 29, 22
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34-37) was mapped to produce a rudimentary metabolite-centric network with 183 nodes 1
and 224 edges. 2
(ii) A metabolite-centric view. Many of the complex networks that occur in 3
nature share global structural features. Analysis of the PAH-MN suggests it has scale-free 4
properties. The log-log plots of probability distribution p(k) versus the interactions k 5
show a linear correlation, convincingly indicating that degree distribution follows a 6
power law, P(k) ≈ k-γ, with γin or out = 2.8, similar to that of many other metabolic networks 7
(Fig. 2C). Power-law connectivity of PAH-MN implies that a few nodes dominate the 8
overall connectivity. PAH-MN has a network diameter of 24, the largest distance 9
between two substrates, with the average shortest path length of 7.2, which is similar to 10
that of other metabolic networks (13, 40). As shown in Figure 2A and Figure S2A in the 11
supplemental material, PAH-MN had an apparent funnel-like structure, in which 12
peripheral pathways converge to a central aromatic pathway via a common intermediate, 13
phthalate. The pyrene- and fluoranthene-subnetworks are linked together via phthalate, 14
which also connected with the benz[a]anthracene and anthracene branches. Degree 15
analysis of all nodes of PAH-MN gave top rank to phthalate of 10 degree (Fig. S2B in the 16
supplemental material), with 2.4 degree average. Phthalate shows high connectivity of in-17
degree of 7 and out-degree of 3 and a low clustering coefficient, a typical property of hub 18
nodes in the network. Phthalate is a common metabolic intermediate during phenanthrene, 19
pyrene, and fluoranthene metabolism in strain PYR-1 (17, 22, 43, 44). 20
Behavior of the PAH-MN. (i) Functional modules. Cellular networks are 21
known to be organized into functionally separable modules (9, 41). To better understand 22
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the modularity and organization principle of PAH-MN (30), we identified functional 1
modules by rational decomposition based on the functions of network components (42). 2
The 224 reactions of the PAH-MN were grouped into three functional categories 3
based on the input/output compounds and the mode of reaction in the order of occurrence; 4
i) oxidoreduction of the aromatic substrates to produce catechols and ring-cleavage 5
products; ii) hydrolysis of the α-keto side chain of the ring-cleaved compounds to form a 6
biological precursor (pyruvate) and metabolites with an aldehyde group, which should be 7
removed through oxidation/decarboxylation in the following reaction; and iii) CoA 8
transfer and subsequent degradative thiolase reactions to form acetyl-CoA and succinyl-9
CoA. These categories were defined as three functional processes, ring cleavage process 10
(RCP), side chain process (SCP), and central aromatic process (CAP), respectively (Fig. 11
3 and Table 2). The repeating pattern of the chemical properties over the degradation 12
process further supports the functional modules (Fig. 4). It also indicates that the PAH-13
MN follows the common metabolic logic, with activation of the thermodynamically 14
stable benzene rings, ring cleavage, side-chain removal, and production of biological 15
metabolic precursors. 16
(ii) An enzyme-centric view. To obtain an enzymatic viewpoint based on the 17
functional modules, we correlated metabolites with enzymes via the chemical reactions 18
based on protein expression data. Encouragingly, the expression profiles of the PAH-19
MN-related enzymes are fully compatible with the PAH-MN. As shown in Figure 5, the 20
profiles of pyrene and phenanthrene, whose pathways mostly overlap, are clustered 21
together, while the profile of phthalate is broadly grouped with that of the control. 22
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Generally, functional distribution and expression of the enzymes are not 1
homogeneous in the PAH-MN. All of the enzymes responsible for RCP belong to the 2
group EC 1 (oxidoreductases), with the exception of epoxide hydrolase, which is in EC 3 3
(hydrolases), whereas SCP and CAP seem to consist of functionally diverse groups of 4
enzymes from EC 1 to EC 5 (Figs. 3 and 5). The β-ketoadipate:succinyl-CoA transferase 5
(EC 2.8.3.6) performs the penultimate step in CAP, the conversion of β-ketoadipate to β-6
ketoadipyl-CoA. Overall, the functional distributions and modularity of enzymes satisfied 7
a typical O2-dependent catabolic strategy. 8
A set of enzymes, including RHOs, CYPs, epoxide hydrolases, cis-dihydrodiol 9
dehydrogenases, and ring-cleavage dioxygenases, are responsible for RCP in M. 10
vanbaalenii PYR-1. Although the process is non-productive, preparing metabolites only 11
for entering SCP or CAP, it is a crucial process that determines substrate range and 12
pathways (28). As shown in Figures 2 and 5, the oxygenation steps in PAH-MN required 13
more enzymes to completely handle HMW PAHs, since the same suite of enzymes could 14
not serve the isofunctional reactions for substrates with different sizes and architecture, 15
with full activity. Actually, the initial enzymes, such as RHOs and CYPs for RCP, 16
showed relatively high genetic and functional redundancy and more complex regulation. 17
Interestingly, there is a clear correlation between the number of aromatic benzene rings 18
and the number of expressed RHOs, whereas CYPs showed no such relationship with the 19
substrates. This finding clearly shows that RHOs are the main enzymes for initial O2-20
activation and further points how M. vanbaalenii PYR-1 regulates RHO expression to 21
regiospecifically control the cis-dihydrodiol products with the best conversion rate for the 22
preferential metabolic route of each aromatic substrate. In detail, NidAB (Mvan_0488) is 23
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induced only by pyrene and phenanthrene. NidAB regiospecifically oxidizes pyrene to 1
only one metabolite, pyrene cis-4,5-dihydrodiol (28). Since the route through pyrene C-2
4,5 dioxygenation is a productive pathway, channeled into the TCA cycle, but the other 3
route, C-1,2 dioxygenation, forms O-methylated derivatives as dead-end products, the 4
expression and regiospecificity of NidAB with respect to pyrene degradation are of great 5
advantage to M. vanbaalenii PYR-1. On the other hand, cis-dihydrodiol dehydrogenases 6
and ring-cleavage dioxygenases, for the second and third steps of RCP, show low genetic 7
and functional redundancy, indicating their extremely broad substrate specificity. 8
Functionally diverse enzymes, with numerical abundance and relatively relaxed 9
substrate specificities, are involved in the SCP (Fig. 5; Table S4 in the supplemental 10
material). Copies of genes for the beginning steps of SCP, such as hydratase-aldolase and 11
hydrolase, are abundant and their level of expression is high, enabling the SCP to accept 12
an enormous range of ring-cleavage compounds. However, the decarboxylase 13
(Mvan_0543) is an exception to this observation. The genome of strain PYR-1 has only 14
one gene copy from which it can be inferred that this one enzyme functions for all of the 15
decarboxylation reactions in PAH-MN. All of the enzymes belonging to SCP, including 16
the decarboxylase, appeared to be expressed constitutively, which suggests that their 17
regulation is likely to be more relaxed than others, which belong to the RCP. 18
Phthalate is the main hub node, showing in-degree of 7 and out-degree of 3, 19
respectively. All 3 outgoing routes are channeled into protocatechuate, which has only 20
one out-degree, to β-carboxy-cis,cis-muconate. Therefore, the β-ketoadipate pathway, 21
which has been reported for HMW PAHs metabolism, inevitably functions for CAP (22, 22
29). Through the β-ketoadipate pathway, which consists of reactions of partial RCP and 23
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SCP, protocatechuate is converted to TCA cycle intermediates. The proteome shows a 1
coordinate expression of the CAP genes arranged as pcaHGBLIJ (21), enhancing the 2
linearity of the metabolic route. The paralogs of β-ketoadipyl CoA thiolase (pcaF), 3
catalyzing the last step of the pathway, transforming β-ketoadipyl CoA to succinyl-CoA 4
and acetyl-CoA, were also identified in the proteome (Fig. 5; Table S4 in the 5
supplemental material). 6
Overall, the enzyme-centric analysis demonstrates that PAH-MN is organized into 7
three functional modules in a hierarchical modularity. In the structure/function of the 8
functional modules, which operate on inputs that satisfy some constraints, RCP and SCP 9
show an apparent funnel shape, while CAP shows a cylinder shape with the same input 10
and output diameters. Therefore, the hierarchical functional modularity of PAH-MN is in 11
a funnel-shaped structure, consistent with its metabolite-centric scale-free characteristics. 12
As exemplified in pyrene metabolism, the coordination of the regulation and substrate 13
specificity and product regiospecificity of the components at the level of module and 14
network suggests that M. vanbaalenii PYR-1 has a channel management with apparent 15
preferred route(s) or pathway(s) for each substrate. However, enzyme-based analysis 16
showed that PAH-MN was probably less tolerant towards error(s) than expected from the 17
metabolite-centric topological robustness of the PAH-MN. 18
Global adaptive response of M. vanbaalenii PYR-1. We further analyzed 189 19
proteins whose expression was commonly upregulated by the presence of aromatic 20
hydrocarbons. Although it only represents 3.2% of the annotated PYR-1 genes (5979 21
ORFs) and 5.5% of the observed proteome, the agreement of upregulation among 7 22
independent measurements enhances the confidence in the expression changes of these 23
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proteins upon treatment of aromatic hydrocarbons, which significantly contributed to the 1
elucidation of the adaptation mechanisms in the context of PAH-MN. 2
A classification of the 189 proteins based on the annotated COGs is listed in 3
Table S2 in the supplemental material. Most of the COGs had similar percentage as 4
detected in the whole proteome (Fig. 1C), which shows treatment of aromatic 5
hydrocarbons influenced not only their metabolism but also global response of the 6
cellular process. Results of these 189 proteins location prediction also showed that that 7
the largest number of proteins belongs to the cytoplasm but that those located in the cell 8
membrane or cell wall were also found. Among 189 proteins, we associated 105 proteins 9
with the metabolism of aromatic hydrocarbons, which include 26 proteins related to 10
signal transduction/transcriptional regulation of COGs (Table S5 in the supplemental 11
material). For the remaining 84 proteins, 4 proteins were catabolic enzymes, which we 12
mentioned with other enzymes in the pathways of aromatic hydrocarbon degradation. 13
Functions of the other 80 proteins were not clear. 14
We identified upregulation of two ABC transporter-related proteins 15
(Mvan_2845/5577) and a membrane translocase-related protein (Mvan_6075), which 16
could affect the uptake of aromatic substrates or other substrates. Permease associated 17
proteins (Mvan_2277/2474) were also found when PYR-1 is cultivated with aromatic 18
hydrocarbons except for phthalate. Thus far no transport systems for entry of 19
hydrophobic substrates into cell have been identified. We also identified a considerable 20
number of proteins (15 proteins) commonly upregulated, which have predicted cellular 21
functions associated with cell wall/membrane organization and biosynthesis. Adaptation 22
mechanisms have been well observed with strains of environmental microorganisms in 23
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response to the poor aqueous solubility of aromatic hydrocarbon substrates, which 1
include PAH-bioavailability-enhancing mechanisms (14) such as modification of the cell 2
wall mycolic acid composition (48), production of biosurfactants (7), and alteration of 3
membrane fluidity and permeability by changing membrane lipid composition (47). 4
Defense or detoxification-related proteins have also been identified in the 5
proteome, which include several methyltransferases listed as related with cell wall 6
biogenesis and secondary metabolites. Toxicity of aromatic hydrocarbons is well 7
established as a result of the production of aldehyde- and hydroxyl-quinone intermediates 8
(24, 46). M. vanbaalenii has been shown to have strategies to neutralize the potential 9
toxic effect of catechols and o-quinones, which has been exemplified by catechol O-10
methyltransferase and o-quinone reductases (23, 25). We have identified a putative 11
GCN5-related N-acetyltransferase (Mvan_3906), which is known to modify toxic 12
phenolic compounds in Bradyrhizobium japonicum (39). 13
Based on these commonly upregulated proteins, we obtained a picture depicting 14
basic cellular response processes influenced by aromatic hydrocarbons (Fig. 6). Once 15
aromatic hydrocarbons enter into the cell, regulatory processes affect not only the PAH-16
MN but also other various cellular responses related to cell wall/envelope biosynthesis, 17
membrane transporters, production of secondary metabolites, signal 18
transduction/transcriptional regulation, defense or detoxification-related proteins. All 19
these responses are interconnected and affect each other being likely involved in the 20
enhancement of the efficiency of aromatic hydrocarbon degradation. 21
At least 33 proteins annotated as hypothetical or whose functions not in COGs 22
were found to be upregulated under aromatic hydrocarbon incubation states. Some of 23
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these proteins encoded by Mvan_1099, Mvan_3829, and Mvan_5946 genes increased 7-1
fold, 50-fold, and 19-fold, respectively, suggesting that they may have an important role 2
in the adaptive response to aromatic hydrocarbons. 3
Evolution of the PAH-MN. Phylogenetic network modules are evolutionarily 4
conserved functional units in the metabolic network (49). To trace a probable pathway for 5
the network evolution, we investigated the structures of evolutionary modules and their 6
relationships to functional modules, which would explain PAH-MN building principles. 7
As suggested by McLeod et al. (32), we analyzed the horizontal gene transfer (HGT)-8
acquired catabolic genomic islands (GIs) and the standard deviations (σ%G+C) to see their 9
contribution to the evolution of PAH metabolism and to compare the degree to which 10
recent HGT may have shaped a genome. We analyzed 23 aromatic hydrocarbon-11
degrading bacteria whose genomes are available, including 4 mycobacteria closely 12
related to M. vanbaalenii PYR-1 (Fig. 7A; Table S6 in the supplemental material). 13
Interestingly, all five PAH-degrading mycobacteria, isolated from geographically diverse 14
locations, shared the conserved ≈150 kb catabolic gene cluster with slightly different 15
genetic configurations with the value of σ%G+C ranging between 2.99 and 3.17, indicating 16
that they might occasionally have acquired the gene cluster in one relatively little recent 17
HGT event from a common ancestor rather than by vertical descent, promoting rapid 18
pathway evolution likely to confer immediate selective advantages to the recipients. 19
Further analysis of the conserved region suggests that the catabolic genes might 20
have been acquired from separate HGT events from neighbors with close physiological, 21
biochemical, and phylogenetic relationships. We found three GIs in the conserved region, 22
which is ≈57% (85 kb) of a ≈150 kb catabolic gene cluster, that showed high sequence 23
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similarity to the catabolic genes of nocardioform actinomycetes, such as Terrabacter and 1
Nocardioides (Fig. 7B and 7C). The first GI (GI-I) contains the genes functioning in RCP 2
and SCP. It includes the type V RHO system, phtAaAb (phthalate dioxygenase). The GI-3
II contains the type V RHO gene, nidAB, involved in RCP (26). The GI-III contains three 4
oxygenases and others functioning in SCP. Two of these oxygenases are type V and the 5
other belongs to type X (26). Interestingly, however, genes/operon for CAP were not 6
found in the catabolic regions of the GIs, strongly suggesting that the CAP operon was 7
not acquired through recent HGT at about the same time as the three GIs. Its acquisition 8
probably precedes the recruitment of the three GIs, which function mainly for the RCP 9
and SCP. Further supporting the hypothesis of separate HGT events is the existence of 10
plasmids harboring GI-II or GI-III in Mycobacterium spp. strains MCS and KMS. The 11
functional association of phylogenetic modules opens the possibility of hierarchical 12
modularity evolution (41), in which PAH-MN seems to have initially been elevated from 13
CAP to RCP via SCP. The CAP gene cluster is in a phylogenetically conserved operon 14
structure, suggesting an ancient origin, whereas the ones for the RCP, with an atypical 15
mosaic structure, appear to be young. All these observations together suggest that initially 16
a common ancestor progressively recruited the phylogenetic modules in a way that 17
functions coordinately, promoting rapid pathway evolution, which enables the 18
mycobacteria to overcome evolutionary pressure to enhance the interactions among the 19
functional modules. In spite of dynamic gene reorganization, the catabolic gene cluster 20
seemed to resist the gene-scrambling events. 21
Although such an evolutionary scenario has advantages, the demands for the 22
practical contribution of the phylogenetic modules to the metabolic capacity of the host 23
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require more sophisticated functional association at the level of proteins or modules. 1
Such functional association generally leads to a strong genomic association and 2
evolutionary cohesion, resulting in coexpression or genetic linkage (3). In this respect, of 3
particular interest are the RHO enzymes, due to their extremely low evolutionary 4
cohesiveness and significant numerical imbalance between oxygenases and ETC (21, 26). 5
In M. vanbaalenii PYR-1, twenty-one genes encoding oxygenase components were 6
identified, whereas only one copy of the type V ETC genes (phtAcAd) was found. 7
Interestingly, the genes are under relaxed regulation and PhtAcAd functions with diverse 8
oxygenase components (28). Moreover, as shown in Figure 8, among 21 oxygenases, the 9
five oxygenases expressed belong to types V and IV, all of which might be functionally 10
compatible with PhtAcAd. The recruitment of a [3Fe-4S]-type ferredoxin (28) would be 11
functionally and evolutionarily beneficial to M. vanbaalenii PYR-1. 12
Enzymes in the phylogenetic modules GI-I and GI-II cannot support all the 13
degradation steps from pyrene to protocatechuate (22), due to the absence of two 14
enzymes, decarboxylase (Mvan_0543) and dihydrodiol dehydrogenase (Mvan_0544). 15
The genes for the enzymes, which show very low genetic and functional redundancy, are 16
found on GI-III, indicating that all enzymes of the three GIs should be together for the 17
complete pyrene-subnetwork. In addition, the three GI-III RHO enzymes, nidA3B3, 18
Mvan_0539/0540, and Mvan_0546/0547, which might function in lateral or angular 19
dioxygenation of fluoranthene, 9-fluorenone, acenaphthylene, and naphthalene, are 20
essential for the fluoranthene subnetwork. The two oxygenases, nidA3B3 and 21
Mvan_0539/0540, are upregulated by fluoranthene but not by pyrene, and the enzyme 22
NidA3B3 dioxygenates fluoranthene regiospecifically to fluoranthene cis-2,3-dihydrodiol 23
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with the highest conversion rate (29). Therefore, interaction and complementation among 1
the three GIs are necessary for the complete pyrene- and fluoranthene-subnetwork. 2
Recruitment of metabolic capability towards pyrene and fluoranthene has probably co-3
occurred, indicating that the two HMW PAHs are the preferential choices of M. 4
vanbaalenii PYR-1. 5
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DISCUSSION 1
Since its isolation in 1986, M. vanbaalenii PYR-1 (10, 18) has been extensively 2
studied with respect to the bacterial metabolism of HMW PAHs (19, 27). Reconstruction 3
of PAH-MN was essentially an effort to organize the dispersed data into an integrated 4
resource for the multiscale mining of chemical and biological data. The proteome was 5
important not only for analyzing cell behavior during aromatic hydrocarbon metabolism, 6
but also for bridging the gap between the genome of strain PYR-1 and metabolites with a 7
holistic perspective. The conversion from gene redundancy (genomic view) to functional 8
redundancy (integrated view of proteome and genome) was crucial for a rational 9
decomposition into the functional modules and enzyme-centric interpretation of the PAH-10
MN. Experimentally founded systems-level interpretation of PAH-MN led to new and 11
deepened insight into M. vanbaalenii PYR-1’s metabolic activities for growth, despite the 12
extremely low bioavailable properties of HMW PAHs. 13
The scale-free architectural features of the PAH-MN are shared to a large degree 14
with other biological networks. Especially, the funnel-like topology of MN is intimately 15
related to its behavior and evolution, in which many peripheral pathways converge to a 16
widely conserved central aromatic pathway, the β-ketoadipate pathway. The CAP module 17
for the central aromatic pathway is more conserved in evolution and function, whereas 18
the SCP and RCP modules appeared later in evolution, being able to accomplish their 19
functions with relatively diverse specificity. The structure/functions of the modules, RCP 20
and SCP, are apparently funnel-shaped with a wide conical mouth and a narrow stem, 21
whereas CAP shows almost the same input and output diameters in its function (Fig. 9). 22
The CAP enzymes are relatively loosely regulated and functionally shared, while the 23
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function of RCP enzymes is substrate-dependent, and expression is tightly regulated (Fig. 1
7B and 7C). The enzymes for SCP are dispersed in modularity and redundant in function. 2
The integrated view of structure, behavior, and evolution of PAH-MN suggests how M. 3
vanbaalenii PYR-1 can benefit from the funnel effect (Fig. 9). The funnel effect and its 4
practical benefits can be summarized; i) enhancing input diversity with the controlled 5
production of limited outputs, which concentrates the flux of intermediates to central 6
metabolism; ii) increasing the connectivity between functional modules, which decreases 7
the epi-metabolome (6); iii) allowing more coordinated regulation; and iv) enhancing the 8
linearity of metabolic pathways, which reduces metabolite dissipation and ensures a more 9
efficient metabolic flow. Overall, such a channel management to maximize the benefits 10
of the funnel effect at the levels of functional module and network may expand and 11
optimize the catabolic potential of a ≈150 kb catabolic gene cluster of M. vanbaalenii 12
PYR-1 to exceptional metabolic diversity, including HMW PAHs with the accepted 13
efficiency. The link of new modules, such as a RCP module, to the existing modules in 14
PAH-MN has merit to expand economically the metabolic capability without affecting 15
the ability of other modules or even causing malfunction of the whole system. 16
In conclusion, this study introduces a channel management model of M. 17
vanbaalenii PYR-1 for global PAH metabolism, along with technical endeavors for 18
mounting and integrating omics data. The hierarchical viewpoint from 19
genes/proteins/metabolites to network via functional modules of PAH-MN, equipped 20
with the engineering-driven approaches of modularization and rationalization, may guide 21
how we use biodegradation knowledge to get traffic to practical applications via sound 22
descriptive and predictive modeling. 23
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1
ACKNOWLEDGMENTS 2
We thank John B. Sutherland and Steven L. Foley for critical review of the 3
manuscript, and Jeff A. Runnells for his assistance with graphics. This work was 4
supported in part by an appointment to the Postgraduate Research Fellowship Program 5
(D.K.) at the National Center for Toxicological Research, administered by the Oak Ridge 6
Institute for Science and Education through an interagency agreement between the U. S. 7
Department of Energy and the U. S. Food and Drug Administration. The views presented 8
in this article do not necessarily reflect those of the U.S. FDA. 9
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TABLE 1. Summary of identified proteins grouped by COGs. 1
COG functional category Predicted
from PYR-1 genome
% of genome
# of identified proteinsa
% of observed proteome
% identified in
proteome
# of >2-fold increase in any
treatmentb
% of >2-fold increase in any
treatment
# of >2-fold increase in all
treatmentsb
% >2-fold increase in all
treatments
J Translation, ribosomal structure, and biogenesis 173 2.89 134 3.91 77.46 72 3.05 9 4.76
A RNA processing and modification 1 0.02 1 0.03 100.00 1 0.04 1 0.53
K Transcription 514 8.60 280 8.16 54.47 219 9.29 20 10.58
L Replication, recombination, and repair 280 4.68 111 3.24 39.64 85 3.60 4 2.12
D Cell cycle control, cell division 32 0.54 24 0.70 75.00 14 0.59 0 0.00
V Defense mechanisms 54 0.90 20 0.58 37.04 14 0.59 0 0.00
T Signal transduction mechanisms 236 3.95 144 4.20 61.02 113 4.79 15 7.94
M Cell wall/membrane/envelope biogenesis 173 2.89 107 3.12 61.85 72 3.05 7 3.70
N Cell motility 9 0.15 1 0.03 11.11 0 0.00 0 0.00
U Intracellular trafficking, secretion,, and vesicular transport 41 0.69 22 0.64 53.66 11 0.47 1 0.53
O Posttranslational modification, protein turnover, chaperones 148 2.48 95 2.77 64.19 55 2.33 7 3.70
C Energy production and conversion 368 6.15 228 6.65 61.96 142 6.02 2 1.06
G Carbohydrate transport and metabolism 238 3.98 140 4.08 58.82 78 3.31 10 5.29
E Amino acid transport and metabolism 421 7.04 243 7.08 57.72 158 6.70 10 5.29
F Nucleotide transport and metabolism 97 1.62 70 2.04 72.16 45 1.91 5 2.65
H Coenzyme transport and metabolism 157 2.63 130 3.79 82.80 87 3.69 10 5.29
I Lipid transport and metabolism 499 8.35 304 8.86 60.92 206 8.74 8 4.23
P Inorganic ion transport and metabolism 311 5.20 133 3.88 42.77 96 4.07 5 2.65
Q Secondary metabolites biosynthesis, transport, and catabolism 527 8.81 245 7.14 46.49 164 6.96 13 6.88
R General function prediction only 841 14.07 401 11.69 47.68 289 12.26 22 11.64
S Function unknown 344 5.75 200 5.83 58.14 140 5.94 8 4.23 Not in COGs
Unclassified proteins 1506 25.19 751 21.89 49.87 546 23.16 49 25.93
Total 5979 3431 57.38 2358 189 a Proteins are identified by proteomic analysis in this study. 2
b M. vanbaalenii PYR-1 was treated with 7 aromatic hydrocarbons, which include phthalate, fluorene, acenaphthylene, anthracene, 3
phenanthrene, pyrene, and benzo[a]pyrene.4
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TABLE 2. Characteristics of functional processes for the degradation of HMW PAHs. 1 2
RCP SCP CAP
Input • (HMW) PAHs • Ring-cleavage compounds • Protocatechuate
Output • Ring-cleavage compounds
• Pyruvate • Metabolites acceptable to RCP
• Acetyl-CoA/succinyl-CoA
Metabolite-centric
• Relatively highly branched pathways
• High-degree metabolites • Gradual increase in MW
• Linear or less branched pathways
• Low-degree metabolites • Gradual decrease in MW
• Partial RCP and SCP • Linear pathway
Enzyme- centric
• O2-dependent • Non-productive metabolites • Tightly regulated • Diverse substrate specificity • Pathway-determining • Preparative for SCP • Mostly belong to EC 1
• Productive • Constitutively/loosely regulated • Generally broad substrate
specificities • Preparative for RCP or CAP • Belong to EC 1 - 5
• Productive • Constitutive expression • Entering TCA cycle • Belong to EC 1 - 5
3 4
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FIGURE LEGENDS 1
FIG. 1. Summary of proteins identified in this proteome study. (A) Venn diagram 2
showing numbers of proteins identified in control samples and aromatic hydrocarbon-3
treated samples treated with phthalate, fluorene, acenaphthylene, anthracene, 4
phenanthrene, pyrene, and benzo[a]pyrene. Bar graph shows comparative analysis of 5
protein expression profiles (explained in detail in text) (B) Heatmap of proteomic data 6
sets. Cluster analysis shows similarities of protein expression between samples. (C) Pie 7
chart showing observed proteins based on COG functions. COG category descriptions are 8
provided in Table S1 in the supplemental material. 9
10
FIG. 2. PAH-MN in M. vanbaalenii PYR-1 (183 nodes and 224 edges). (A) The 11
reconstructed PAH-MN of M. vanbaalenii PYR-1. Name of PAHs and their metabolites 12
are given in Table S3 in the supplemental material. (B) Scale-free PAH-MN showing the 13
highlighted larger hubs in size. Color and size of the nodes indicate benzene ring number 14
and connectivity, respectively. For example, the main hub node, phthalate, which shows 15
high connectivity of in-degree of 7 and out-degree of 3 was shown to be the biggest 16
yellow node in size and color. (C) Log-log plots showing the number of compounds 17
versus connectivity. Connections involved either as a substrate or as a product are 18
counted. k, connectivity; p(k), number of compounds. Cytoscape 19
(http://www.cytoscape.org/) was used for network analysis and visualization. 20
21
FIG. 3. (A) and (B) General scheme of PAH metabolism in M. vanbaalenii PYR-1. 22
Hexagon, circle, and rhombus indicate starting PAH, intermediates, and ring-cleavage 23
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metabolites, respectively. In RCP, aromatic hydrocarbons are monooxygenated with the 1
aid of epoxide hydrolase or dioxygenated to dihydrodiols, which are ring-cleaved via the 2
corresponding catechols. The output of the RCP, ring-cleavage compounds (rhombus), 3
then go through SCP, which converts them to metabolic intermediates (circle or hexagon), 4
which are ready to enter another round of RCP or CAP, and produce active biological 5
precursors. SCP shows various steps from 1 to more than 5 in the functional order of i) 6
hydroxylation of side chains of the ring-cleavage compounds, ii) oxidation of the 7
hydroxylation-generated aldehyde group to a carboxylic acid, and iii) decarboxylation of 8
the carboxyl group from the aromatic nucleus. The functional linearity for rearrangement 9
of metabolites to enter another round of RCP or CAP results in the relatively low in- and 10
out-degree of the metabolic compounds. Because more benzene rings may still remain, 11
RCP and SCP reiterate until the pathways produce an intermediate to enter CAP. The 12
number of repetitions depends on the number of benzene rings of the HMW PAHs. 13
Diverse PAHs are transformed into the common metabolic intermediate, protocatechuate, 14
which is then funneled into CAP. The CAP consists of a series of reactions for the 15
conversion of protocatechuate to small aliphatic compounds, which can directly enter 16
central metabolism. In M. vanbaalenii PYR-1, the β-ketoadipate pathway functions for 17
completion of the pathway into the TCA cycle intermediates. Please refer articles by Kim 18
et al. (21) and Kweon et al. (29) for more information about on genes and enzymes in 19
detail. 20
21
FIG. 4. Relationship between nodes (chemical compounds) and their molecular weights 22
(Mr) and water solubility (logP) on the network. Modularity does not always guarantee 23
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clear-cut subnetworks linked in well-defined ways, but there is a high degree of overlap 1
and crosstalk between modules (8). As degradation proceeds, metabolites repeatedly 2
move up and down in molecular weight and hydrophobicity, which is in accordance with 3
the functional modules in the degradation process. This repeating pattern of the chemical 4
properties over the degradation process strongly supports the concept of functional 5
modules in the PAH-MN of M. vanbaalenii PYR-1. Hexagon, circle, and rhombus 6
indicate starting PAH, intermediates, and ring-cleavage metabolites, respectively. Color 7
denotes number of aromatic rings: red, 4; blue, 3; green, 2; yellow, 1; while, 0. Numbers 8
with M represent PAHs and their metabolites, which can be found in Figure 2 and Table 9
S4 in the supplemental material. The logP values for water solubility of the compounds 10
were calculated by MarvinSketch Version 4.1.8. 11
12
13
FIG. 5. Heatmap of the proteins involved in the metabolism of aromatic hydrocarbons in 14
M. vanbaalenii PYR-1. Profiles of protein expression showed close relationship with 15
functional modules. We correlated three functional modules with the concept of 16
peripheral pathways and central pathways. Clearly, the heatmap shows that the regulation 17
of enzyme expression involved in RCP is much tighter that those of SCP and CAP, which 18
additionally supports the idea of a three functional module system. 19
20
FIG. 6. Predicted cellular processes influenced by growth of M. vanbaalenii PYR-1 with 21
aromatic hydrocarbons based on the commonly upregulated proteins. 22
23
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FIG. 7. Evolution of PAH-MN. (A) Genome size vs. σ%G+C for 24 replicons from 23 1
aromatic hydrocarbon degrading bacteria 2
(http://www.pathogenomics.sfu.ca/islandpath/update/IPindex.pl). The M. vanbaalenii 3
PYR-1 replicon and those of the four PAH-degrading mycobacteria shows relatively low 4
σ%G+C values, predicting that they have undergone relatively little HGT. Notably, the 5
σ%G+C of M. vanbaalenii PYR-1 is similar to that of the Gram-positive PCB-degrader, 6
Rhodococcus jostii RHA1, but smaller than that of another PCB-degrader, Burkholderia 7
xenovorans LB400. The 23 bacteria are listed in Table S6 in the supplemental material. 8
(B) and (C) The relationships between phylogenetic and functional modules and the 9
growth of PAH-MN. The genomic island prediction was analyzed using IslandViewer 10
(http://www.pathogenomics.sfu.ca/islandviewer/query.php). 11
12
FIG. 8. Classification of 21 RHOs of M. vanbaalenii PYR-1 and their protein expression 13
patterns under PAH treatments. The expressed 10 RHOs were classified as type IV 14
(Mvan_4415), type V (Mvan_0463/0488/0525/0546), and type X 15
(Mvan_0492/0539/0908/2869/4190) based on Kweon’s classification (1, 26). 16
17
FIG. 9. The funnel effect in the structure/function of the functional modules, RCP, SCP, 18
and CAP in the PAH-MN. 19
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Identified proteinsin total (3,431)
>2-fold increasein any treatment (2,358)
>2-fold increase
in all treatment (189)
J
A
KL
D
VTM
N
U
OC
G
E
F
HIP
Q
RSNoCOGs
FIG. 1.
10
B
Benzo[a
]pyre
ne
Acenap
hth
yle
ne
-10
C
Pyre
ne
Phen
anth
ren
e
Flu
ore
ne
Anth
racen
e
Phth
ala
te
Control
Aromatichydrocarbon-treated
93 1,927 1,411
A
TotalProtein
3,4312,358
>2 fold in alltreatments
>2 fold in anytreatment
189
947947
1,927 93
# of proteins
1,411
189
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151
O
ScoA
O
ScoAHOOC
OH
OH
OO
OH
O
OHO
O
O
OH
97
2937266
OHOH
O
O
HO
OH
O
O
O
HO
HO
O
O
HOO
3250
OH
OHOH
OH
HO
HO HO OH
HO OH
O
OCH3
H3C
HO
HO
OHOH
O
O
CH3
CH3
OH
OH
3 23
OH
2 27 47
4 5
1
48 493024 28
OO
CH3
CH3
25
O
HOHH
31O
HO
H
OH
HO
HO
H
O
H
CH
38
39
178
OH
OHO
OOH
OH
OHO
OHOH
HO OO
HO
OH
5152 403341
OH
1110
OHOH
53HO O
35 HO 4243
HO OH
OHOH
55
O
12
OO
54HO OH
44
O O45 O O 34
OH
OH
5657
OOO
OH
OH
O
OH
O
OH
OH
OHO
OH
36O
OH
HO
O
OOH
OH
O
OH
O
15
14 13
18
O
OH
OHO
O
19 O O O
46
102
OHHO
113
123
103 HOOH
104HO
OH
OHHO
114
OHOH
OHO
CH3
OHOH
115
109 O
O
106
110 112
O O116
OO
CH3
CH3
O
O
OH
OH
120
82
O O
CH3 CH3
119HO
O
OHO
105
111
117
116 O
O
CH3
H3C
HO
O
OO
H
OHOH
O
O
108
107 O
OH OH
O
O
OHO
121 123OH
OH
OOCH3
CH3
128O O
148 HO OH
HO
127OH
OH HO OH
HO
147 143 136HO OHOH
OH
142
125
126
141124O
OH
OHO
OH
OH
O
OHOH
O
O
HO
146 HO OH
130
129
135 OOH
OH
131
OH
OHO
O132
149HO
OH
OHOO 137
OHO
OHO
OHOOH
O
133144150
OH
OHO
OH
OHOO 138
OH
O
H
OH
O
H
O
134
OHO
OH
O
22
169
OH
OH
O
O
OOH
HOO
HO
OH
O
O
HOO
OH
OH
O
OO
OH20 1621
OHOOH
O
OHOH
OHOOH
O
OHOH
170
O
OH O
O
OHOH
OH
O145
OH
O
OH
139
OH
OH140
174OHO
OHO OH
O
OH
OHO
OH
172
173
171
175
OHO
OHOH
OHO
OOH
182
O O
O OH
HO
O
O OHO
O
183
176
O OHO
O 177
O
OH
O OOH178
O
OH
O
OH
ScoA179
OH
O
CH3
O
O
CH3
CH3
OH
OH
85OH
OH
86 O
OH
CH384
83
89OH
OH
81
88
OHOH
OH99
87OH
OHOH
OH
76
77
OHO
OHOH
101
OH
OH
O
O
90
92
100
80
OH
O
OHO
OH OH
O
HOO
OHO
O
OH
O
OH
H
91
OHO
O
OOH OH
O
O
OH
OH H
O79
78
O O
93
OOH
OO
CH3
73 72
OOH
OOH
71
OH
OH
OH
OH
62
O
61
60
OH
OH
59
58
OO
OH
OH66
65
OH
OH
O
OH
CH3
OH
O
CH3
O
O
CH3
CH
OH
OHO
67
154
94
63
68
74
70
75
69
64
OOHOH
OH
O
HO
OOHOH
OH
O
HO O
OOHOH
OH
O
97
96
98
OO
OOHOH
OOHOH
HOOH
OOHOH
HO
OH
95
HO
HO
O
HO
HO
H
OOHO
OH
156
157HO
OHO
O
HO
O
OHO
160
161 162
O
O
H3C
H3C
OH3C
HO
167
168
HO
OH
O
HOOH
HO
OH
OHOH
OH
OH
OHHO
OHHO
163
164
165
166 159 153
158152
180181
TCAcycle
A
O
155
O
FIG. 2.
γ=2.0
B
0.0
0.5
1.0
1.5
2.0
2.5
0.0 0.5 1.0
Log K
Log p
(K)
Incoming Outgoing All
γ=2.8γ=2.8
C# of aromatic rings
5 4 3 2 1 0
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FIG. 3.
Ring-cleavage dioxygenase
B
RHOO P-450
OH
OH
Dehydrogenase
Decarboxylase
OH
CHO
COOH
CHO
COOH
COOH
Cn
OH
COOH
RCP
SCP
Epoxide hydrolase
Dihydrodiol dehydrogenase
OHOH
OH
OH
OH
HOOCOH
COOHCOOH
COOH
Hydratase-aldolase
Aldolase
Hydrolase
A
Cn
RC
P
SC
P
TCACycle
CA
P
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Metabolite and Functional module
M r
RCP SCP RCP SCP-RCP
0
100
200
300
0
2
4
6
8
10
M58 M59 M60 M71 M81 M77 M78 M79 M80 M100M101 M22 M171M175M72 M87 M172
logP
RCP-SCPSCP SCP
M173
OHOH
OHOH
OOH
OOH
OOH OH
OH
OHO
O
OH
OHOH
OH H
O
OH OH
O
HOOOH
O
O
OH
O
OH
H
OH
O
OH
O
OHOOH
O
OHOH
OHOOH
O
OHOH
OHO
OHOH
O
OH O
OOH
OH
FIG. 4.
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FIG. 5.
RHO
P450
Mono-oxygenaseDihydrodiol
dehydrogenase
Ring-cleavage dioxygenase
Epoxide hydrolase
Catechol O-methyl-
transferase
Hydrolase
Alcohol dehydrogenase
Decarboxylase
Aldehyde dehydrogenase
PcaHGBLIJ
PcaF
RCP
SCP
CAP
Peripheral
Pathway
Central
AromaticPathway
Hydratase-aldolase
Benzo[a
]pyre
ne
Acenap
hth
yle
ne
Phth
ala
te
Anth
racen
e
Flu
ore
ne
Pyre
ne
Phen
anth
ren
e
-7 7
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PAH-MNRCP
Regulatory systems
Cell wall/membrane biogenesis
Secondary metabolites/polyketide synthesis
Lipid/amino acid metabolism
Defense mechanism
Energy/carbon metabolism
Other cellular responses
SCP
CAP
TCAcycle
FIG. 6.
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1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
0 1 2 3 4 5 6σ%G+C
Genom
e s
ize (
Mb) RHA1
LB400 Chromosome 1
PYR-1KMSJLSPYR-GCK
MCS
A
C
Fluoranthene-subnetworkTCACycle
Pyre
ne-s
ubn
etw
ork
B
Pyr
ene-s
ubnetw
ork
RC
P
RC
P
SC
P
RCP
RCP
RCP
TCACycle
GI-I GI-II GI-IIIPhylogenetic modules
Functional modules
PA
H-M
N G
row
th
Fluoranthene-subnetwork
RCP SCP RCP RCP RCP RCPSCP
CAP
CAP
FIG. 7.
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FIG. 8.
Mvan_0463
Mvan_0488
Mvan_0525
Mvan_0546
Mvan_4415
Mvan_0533
Mvan_0539
Mvan_0492
Mvan_4184
Mvan_4190
Mvan_4213
Mvan_4319
Mvan_0908
Mvan_4962
Mvan_4910
Mvan_1001
Mvan_2012
Mvan_3784
Mvan_2869
Mvan_5211
Mvan_5225
Phth
ala
te
Flu
ore
ne
Acenaph
thyle
ne
Anth
race
ne
Phenan
thre
ne
Pyr
ene
Benzo[a
]pyre
ne
Type X
Type I
Type II
Type III
Type IV
Type V
-5 5
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FIG. 9.
SCP
CAP
SCP
RCP
RCP
SCP--RCP
Acetyl-CoA, Succinyl-CoA
Pyruvate
OHHOOC
OH COOHCOOH
OHO
OHOH
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