Mapping the Protein Interaction Network in Methicillin-Resistant

ARTICLE pubs.acs.org/jpr

Mapping the Protein Interaction Network in Methicillin-Resistant Staphylococcus aureus Artem Cherkasov,*,†,‡,§ Michael Hsing,†,‡,|| Roya Zoraghi,‡ Leonard J. Foster,^,# Raymond H. See,‡,z Nikolay Stoynov,^ Jihong Jiang,‡ Sukhbir Kaur,‡ Tian Lian,‡ Linda Jackson,‡ Huansheng Gong,‡ Rick Swayze,‡ Emily Amandoron,‡ Farhad Hormozdiari,§ Phuong Dao,§ Cenk Sahinalp,§ Osvaldo Santos-Filho,‡ Peter Axerio-Cilies,‡ Kendall Byler,‡ William R. McMaster,‡,O Robert C. Brunham,‡,z B. Brett Finlay,#,O and Neil E. Reiner*,†,‡ ‡

Division of Infectious Diseases, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada Centre for High-Throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada # Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, British Columbia, Canada O Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada z Centre for Disease Control, University of British Columbia, Vancouver, British Columbia, Canada § Department of Computer Sciences, Simon Fraser University, Vancouver, British Columbia, Canada Bioinformatics Graduate Program, University of British Columbia, Vancouver, British Columbia, Canada. † These authors contributed equally to the writing of this manuscript.

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^

bS Supporting Information ABSTRACT: Mortality attributable to infection with methicillin-resistant Staphylococcus aureus (MRSA) has now overtaken the death rate for AIDS in the United States, and advances in research are urgently needed to address this challenge. We report the results of the systematic identification of protein-protein interactions for the hospital-acquired strain MRSA-252. Using a high-throughput pull-down strategy combined with quantitative proteomics to distinguish specific from nonspecific interactors, we identified 13 219 interactions involving 608 MRSA proteins. Consecutive analyses revealed that this protein interaction network (PIN) exhibits scale-free organization with the characteristic presence of highly connected hub proteins. When clinical and experimental antimicrobial targets were queried in the network, they were generally found to occupy peripheral positions in the PIN with relatively few interacting partners. In contrast, the hub proteins identified in this MRSA PIN that are essential for network integrity and stability have largely been overlooked as drug targets. Thus, this empirical MRSA-252 PIN provides a rich source for identifying critical proteins essential for network stability, many of which can be considered as prospective antimicrobial drug targets. KEYWORDS: protein interaction network, MRSA, drug targets, hub proteins, antimicrobials

’ INTRODUCTION Progress in genomics and the accumulation of proteomics data have accelerated investigation of cellular processes and revealed the underlying complexity of protein-protein interactions. Recent reports demonstrate that for diverse cell types, protein interactions are organized in the form of scale-free networks that govern the operation of panoply of physical and biological systems, including many microbial cells. To date, such scale-free protein interaction networks (PIN) have been described in Saccharomyces cerevisiae,1 gastrointestinal pathogen Helicobacter pylori,2 Treponema pallidum—the causative agent of syphilis,3 Escherichia coli,4 hepatitis C,5 and vaccinia viruses6 as well as multicellular r 2010 American Chemical Society

organisms including Caenorhabditis elegans,7 Drosophila melanogaster,8,9 and Homo sapiens.10,11 Early studies on yeast protein interaction networks (PINs) have revealed correlations between the level of physical connectivity of proteins and their impact on maintaining cellular functions and survival of the organism.12 Highly connected proteins (or referred as “hub”) have been implicated in cellular essentiality13 and have important roles in closely connected subnetworks corresponding to specific biological processes.14 Received: September 7, 2010 Published: December 17, 2010 1139

dx.doi.org/10.1021/pr100918u | J. Proteome Res. 2011, 10, 1139–1150

Journal of Proteome Research Recent network analyses further supported the ideas that different network centrality measures such as the ones based on shortest paths or localized interaction modules are potential indicators for proteins with cellular essentiality.15 Upon the basis of these findings, it is reasonable to propose that removal of a hub protein from the network is more likely to lead to lethality than removal of a less connected protein that has a peripheral network position. Hence, defining the architectural properties of a PIN for a microbial pathogen not only provides novel biological insights including potential functions for previously uncharacterized proteins but also offers a path toward identifying high quality drug targets based on the identification of hub proteins essential to network integrity. In addition to their correlations to essentiality, another attractive feature of targeting hub proteins is that in comparison to conventional targets—they may be less likely to develop drug resistance given their central network positions making them less tolerant of mutations.16 The accumulated knowledge about the unique architecture of PINs from previous studies provided a rationale for investigating whether protein-protein interaction data could be used as a basis to identify novel, high quality drug targets in the ubiquitous pathogen methicillin-resistant Staphylococcus aureus (MRSA). This organism of major medical importance emerged in the 1980s and has since become endemic in many hospitals, leading to the increasing use of vancomycin, which has been considered a last line of defense. In 2002, the first case of a S. aureus isolate that was totally resistant to vancomycin was documented in the United States,17 raising concern about vancomycin’s longevity. Similarly in Canada, the prevalence of MRSA infections has been increasing at an alarming rate.18 Thus, a deeper understanding of MRSA biology through protein-protein interactions and the identification of novel drug targets in the MRSA PIN with reduced potential to develop resistance is a high priority.

’ RESULTS AND DISCUSSION MRSA PIN Summary

Initially, 406 MRSA proteins were cloned, expressed as glutathione S-transferase (GST)-fusions and used as baits in affinity pull-down experiments, with each pull-down having an internal, isotopically labeled negative control. The resulting protein complexes were then characterized by quantitative mass spectrometry (LTQ-FT), leading to the identification of 13 807 bait-to-prey pull-down interactions or cocomplex memberships (Table 1 of the Supporting Information). Although these bait-toprey interactions might be direct or indirect, for the purpose of subsequent bioinformatic and statistical analyses, we assumed a `possible’ physical interaction between a bait protein and each of its preys. As a result, 13,219 pairwise interactions among 608 MRSA252 proteins were reconstructed into an MRSA PIN as illustrated in Figure 1. (See Experimental Section for details, and the list of 608 MRSA interacting proteins is included in Table 2 of the Supporting Information.) Compared to a total of 2656 proteins annotated in the RefSeq database19 at the time of the study, 608 interacting proteins identified from the PIN correspond to 22.89% coverage of the MRSA252 proteome. Bait Selection Strategy

For the MRSA-252 protein pull-down experiments, we used a bait selection strategy consisting of two rounds to maximize the total number of protein-protein interactions detected. The firstround baits (corresponding to one-third of the total number of baits tested) were selected using hub prediction tools developed

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Figure 1. 2D representation of the developed MRSA PIN by inferring physical and pairwise interactions between protein baits and each of their preys. Hub proteins are shown in yellow and nonhubs in blue. Established antimicrobial drug targets are shown in red if they were classified as nonhubs and in purple if they were categorized as hubs.

in our previous studies20,21 consisting of bio- and cheminformatics approaches that can predict proteins acting as highly connected nodes in their corresponding PINs. These approaches rely on sequence- and structure-derived information were applied to the MRSA proteome to identify potential high probability hubs to be used as baits in the initial round of pull-downs. In the second round of pull-downs, baits were selected from the list of the most abundant preys identified in the first round of pull-downs. This second round bait selection strategy is sometimes referred to as the “name your friend” method. It has been commonly applied to maximize network coverage and the efficacy of vaccine campaigns against various infectious diseases,22,23 as well as in some studies concerned with protein-protein interactions.4 We also used a number of known bacterial drug targets as second-round baits. More details can be found in the Experimental Section. Thus, our bait selection strategy has a bias toward identifying interactions involved with highly interacting proteins in the MRSA252 PIN. This bias was intentional for two purposes: (1) to maximize the number of protein interactions determined from the pull-down experiments and (2) to facilitate the discovery and prioritization of antibacterial drug targets. Accuracy of Bait-Prey Interactions Detected

It has been documented in numerous studies that high throughput PIN mapping experiments are prone to false positive predictions,24,25 so here we have used an internal control for nonspecific binding. For each pull-down, the amount of each interactor bound to the GST-fused bait protein was quantified relative to the amount bound to GST alone; if an interactor was present in the bait pull-down at least twice the level that it was present in the GST pull-down then it was considered a bonafide interactor since it fits the biochemical definition of specific. Thus far, all other large-scale, affinity purification-mass spectrometry 1140

dx.doi.org/10.1021/pr100918u |J. Proteome Res. 2011, 10, 1139–1150

Journal of Proteome Research

a

6446 S. cerevisiae [Gavin]

nr = non-redundant. b Percentages were calculated with respect to the subject species.

0 (0.00%)

16 (6.45%) 263 (1.24%) 1189 (5.60%) 32.88 16.68 21246 (10.66) 2549 (1.28) 239 (12%)

8 (6.25%)

no data

1993

2 (0.05%) 71 (1.95%) 21.61 10.10 3640 (3.67) 721 (0.73) no data

27 (21.95%)

T. pallidum [Titz]

991

123 (1.06%)

456 (8.82%) 1900 (36.75%)

988 (8.55%) 23.77

30.94 8.34

17.76 11557 (7.82)

5170 (7.98) 1241 (1.92)

1301 (0.88) no data

118 (18.2%) 648

C. jejuni [Parrish]

1477

1000

no data

E. coli [Butland]

n/a

1141 (13.23%)b

26 (11.93%)

n/a

259 (3.00%)

n/a

12.88

45.03 43.48

7.04 8625 (3.23)

13219 (32.56) 608 (1.5)

2448 (0.92) 330 (12.3%)

11 (2.7%) 406

E. coli [Arifuzzaman]

2667

449

4339

MRSA 252

to the MRSA

hubs compared

compared to the MRSA to the MRSA (hubs only) (per bait), nra data set

attempted

completed

no interactions

(per bait), nra

degree

(%) of conserved conservation of

of proteins compared baits with

interactions

average network

average degree

conservation of pairs 1141

baits

To examine organization of the empirically derived MRSA PIN we compared it with similar data sets recently reported for Escherichia coli,4,30 Campylobacter jejuni,31 Treponema pallidum,3 and Saccharomyces cerevisiae.1 We were particularly interested in comparing the numbers of protein interactions established per bait in these other organisms to assess the efficiency of the bait selection strategy we used. The corresponding parameters were calculated for the above data sets and are presented in Table 1 along with relevant properties such as average number of interacting partners established per bait and average degree of a network node (average number of connections). These numbers indicate that our hub-centric bait selection strategy understandably allowed recovery of the largest number of interactions and interactors per bait. The largest average degree of the MRSA network also reflects its expected enrichment with hub proteins.

baits

Table 1. Bait Coverage Summary and Conserved Interactions for MRSA and Other PIN Data Sets

PIN Coverage

interactors

approaches to define PINs have simply use a qualitative subtractive approach to remove those proteins found in a control purification from those found in the specific purification. As we26-28 and others29 have shown with smaller scale experiments, the quantitative approach to determine specific interactions is far more sensitive and specific than the qualitative approach. No two methods for determining protein interactions are ever in complete agreement but in order to assess whether some of the interactions identified by our high-throughput approach could be confirmed by a different approach, we used a coimmunoprecipitation (Co-IP) strategy specifically to compare findings with those of the GST pull-down experiments for MRSA pyruvate kinase. By GST pull-down, pyruvate kinase was found to be one of the most highly connected hubs in the MRSA PIN with 243 interacting partners. When pyruvate kinase was subjected to CoIP, a total of 83 MRSA proteins interacting with pyruvate kinase were identified with 56 of these in common with those of the GST pull-down method. Thus, for this one protein, our highthroughput pull-down approach had a sensitivity of 67.5%, with a false negative rate of 32.5%. Notably, this value is much lower than the false negative rates previously reported for other PINs: 76% for Caenorhabditis elegans, 77% for Treponema pallidum, 85% for Saccharomyces cerevisiae, and 90% for Drosophila melanogaster3,25 but this may not apply for our entire data set. Conversely, using high-throughput pull-downs, we identified 160 pyruvate kinase-interacting proteins that were not found by co-IP. While it is impossible to predict an accurate false positive rate due to the absence of negative protein interaction data, because of the quantitative nature of the pull-downs it is highly likely that all of these are specific interactors. What these data do not tell us, however, is whether these interactions play a functional role in the cell. In addition to the Co-IP experiment on pyruvate kinase, we analyzed the observed pairs of protein baits and preys in the MRSA252 PIN for the presence of any reciprocal interactions (i.e., protein A pulls down protein B, and protein B pulls down protein A). Among 11,241 interacting protein pairs, both of which have been used as baits in the GST pull-down experiments, 588 (or 5%) of them have detected reciprocal interactions. This number characterizes the detected MRSA interactions as memberships, rather than binary interactions. That is to say, the observed edges in the experimental PIN indicate that two proteins correspond to the same protein complex, rather than necessarily interacting directly.

pairs of “interacting” proteins

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Figure 2. Distribution of the numbers of protein interactions in the MRSA PIN (A panel) and a logarithmic transformation plot for the distribution of protein interactions (B panel).

Interestingly, the adopted hub-centric PIN mapping strategy resulted in the lowest number of baits that did not return any interacting pray proteins. As the data in Table 1 indicate, only 11 such noninteracting proteins were found in the MRSA PIN (see Figure 1) which corresponds to only 3% of baits used, compared to 12-18% reported in other studies.1,5,30 The different network statistics among the PIN systems may be attributable to experimental methods used and also our bias in bait selection toward highly interacting proteins through the use of predicted hubs as pull-down baits. In this regard, we recently published several bio- and cheminformatics approaches20,21,32 that allow predicting such hypothetical hubs that can be used for designing similar PIN mapping experiments. Overlap with Other Experimental PINs

Another important consideration for the MRSA PIN was the extent to which the interactions observed were found to be conserved in PINs reported for other microbes. Thus, for each pair of interacting proteins that has been reported for any particular organism, we searched for a pair of highly similar proteins (using as similarity criteria: alignment E-value e 10-5, sequence similarity g 35% and alignment coverage g 65%) to ask whether they also interacted in the MRSA PIN. The third last column of Table 1 corresponds to the degree of conservation of protein pairs interacting in MRSA and also present in Escherichia coli, Campylobacter jejuni, Treponema pallidum and Saccharomyces cerevisiae. As can be seen, this number varied from 2 to 37%, with E. coli having the highest and T. pallidum the least number of conserved protein pairs. The numbers were even lower when it came to pairs of proteins that were not only conserved but also interacted in the subject species (when queried against the MRSA PIN). Those values ranged from 0.05% for T. pallidum to 8.82% for E. coli. Low degrees of overlap between PINs established for different organisms has been reported before31 and may be attributable to different experimental methods used, bait selection bias, and to varying degrees of incompleteness for the different PINs. On the other hand, this finding provides indirect evidence that “wiring” of the various networks studied may, in fact, be fundamentally different in different organisms (i.e., conserved hubs interact with different partners in different PINs).

Scale-Free Characteristics of the MRSA PIN

Recent advances in network theory have demonstrated the importance of uneven distributions of interactions occurring in social, physical and biological systems. It has been found that seemingly unrelated systems such as economic, professional, social and biological networks all exhibit a power-law distribution (eq 1): ð1Þ PðkÞ k-γ where P(k) is the probability of a selected protein with exactly k links (degree), and γ is the value of the exponent.33,34 The heterogeneous architecture of scale-free networks imparts a robustness and error-tolerance from random perturbation and is often viewed as a possible common blueprint for naturally occurring large-scale networks.33 The relevance of scale free networks has been demonstrated in previous protein network surveys,33 where it was found that the distribution of network degrees (aND) (numbers of connections) for the nodes in a PIN also follows an asymptotic power law (eq 1). This finding laid the foundation for characterizing the evolution of the protein universe in terms of a growing scale-free system in which individual proteins are represented as nodes of a propagating network.35-39 To evaluate the topology of the MRSA PIN we carried out a frequency analysis of network degrees. Figure 2A shows the protein interaction distribution plot and that following logarithmic transformation it demonstrates linearity (Figure 2B). The results show that the power distribution of protein degrees in the MRSA PIN fit well onto the logarithmic linear dependence with r2 = 0.68 indicating the scale-free character of the network. Characteristic Features of Hub Proteins

As discussed above, one of the most prominent features of scale-free PINs is the presence of a relatively small subset of highly connected hub proteins that play a major role in network integrity and stability. Thus, identification of characteristic features and properties of hubs in the scale-free MRSA PIN was of particular interest. Based on our experimental network of 608 proteins, we arbitrarily defined a list of 60 hubs (see Table 2 in the Supporting Information) that corresponded to the top 10% of interactors [a parameter developed in previous reports.20,21,32 The MRSA hub subset consisted mostly of 1142



1143

The three categories (hub, nonhub, drug target) are presented in this table exclusively. For example, if a protein is both a hub and a target, it is classified only to the “drug target” category. p-values from two sample t tests. a

b

0.01 0.00 0.03 0.00 1.25 ( 0.15 3.12 ( 0.81 1.66 ( 0.10 6.55 ( 1.05

1.98 ( 0.34

0.53 0.08 0.02 0.15 38.08 ( 3.69 34.42 ( 4.32 41.69 ( 0.98 46.26 ( 2.98

36.61 ( 2.80

0.44 0.11 0.02 0.01 84.29 ( 3.97 88.57 ( 3.02 86.00 ( 2.67

0.02 0.00 0.00 0.00 608.45 ( 198.32 1487.27 ( 361.60

62.33 ( 1.55

Protein-protein interactions are of major importance in both protein function and in the structural organization of the cell. The large-scale protein-protein interactions we report here for MRSA should serve as an important basis for investigating S. aureus biology, including predicting potential functions for previously uncharacterized proteins. Moreover, and no less important, the PIN architecture itself should assist in the identification of novel drug targets that are essential and critical for cell viability and growth. This would be a truly novel approach since known antimicrobial targets that have been selected and used in the clinic thus far were not chosen based upon their hierarchy and importance in protein interactomes. To evaluate the network locations and potential importance of current antibacterial targets we searched the literature and databases (including reviews by43-48 and identified 94 bacterial

75.29 ( 3.87

Mapping Known Antibacterial Targets to the MRSA PIN

959.98 ( 192.81

Table 3. Differential Characteristics Averaged for MRSA Hubs, Nonhubs and Antimicrobial Targets

p-values (hubs vs nonhubs)b

ribosomal proteins, various kinases and enzymes involved in carbohydrate metabolism. Several hypothetical MRSA proteins were also identified as highly interacting hubs. We also compared the subset of MRSA hub proteins with less highly connected proteins in terms of their essentiality, cellular abundance and various physicochemical characteristics. For example, we had previously demonstrated that several sequencederived physicochemical properties of proteins such as Hydrophobicity, Surface Area, and Fraction of Flexible Coil Fragments may exhibit different patterns for hubs and nonhubs.32 The corresponding average values for these parameters computed for MRSA hubs and nonhubs are shown in Table 2 and indicate that MRSA hubs were generally smaller, more hydrophilic and contained a higher fraction of flexible coil residues when compared with nonhubs. For some MRSA “hub” proteins, there were no entries in the Database of Essential Genes.40 In order to address the essentiality of some of these proteins, we proceeded to knock down or knockout the target of interest using antisense RNA or TargeTron technologies, respectively.41 The corresponding mean values featured in Table 2 demonstrate that the percentage of confirmed essential proteins was much higher among hub proteins (77%) when compared to nonhub proteins (49%). The data in Table 2 also illustrate that highly connected MRSA proteins tended to have a higher relative cellular abundance which is consistent with previous observations that hubs tend to be essential, are involved in house-keeping functions, and are usually expressed at higher levels.42 As we reported previously, differences such as these in physicochemical and biological properties of hub and nonhubs can be used to develop predictive bioinformatics approaches that can optimize PIN mapping strategies.20,32

307.22 ( 34.86

0.010

p-values are from two sample t-tests.

5429.74 ( 740.77

0.042

0.570

0.00

14519.700

0.583

0.00

12458.070

Fraction of flexible coil residues

0.00

Estimated surface area

42 39.50 ( 6.39

0.026

28 69.36 ( 12.97

0.000

-0.309

70 51.44 ( 6.63

1.619

-0.331

486 28.40 ( 1.50

6.603

Average hydrophobicity

52 173.73 ( 4.88

Relative protein abundance

Number of proteins Average network degree Average network betweenness Average conservation across 20 bacterial pathogens (%) Average sequence similarity to human proteins (%) Relative protein abundance

n/a

drug targets (all)

n/a

48.54%

nonhubs

548

76.67%

hubsa

60

Frequency of essential proteins in the group

p-values (hubs vs approved targets)

Number of proteins

p-values (hubs vs experimental. targets)

t test p-valuea

drug targets (experimental)

a

nonhubs

drug targets (clinical)

hubs

p-values (approved vs experimental targets)

Table 2. Differential Characteristics Averaged for MRSA Hubs and Nonhubs

0.03

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Figure 3. Average number of protein interactions among established drug targets, and hubs and nonhubs in the MRSA PIN (A panel). Network Betweenness (NB) values for established drug targets, and hubs and nonhubs in the MRSA PIN (B panel).

proteins that were previously reported as either experimental or established antimicrobial drug targets (Table 3 of the Supporting Information). Seventy of these were also found to be present in the MRSA PIN based on their sequence similarity (Table 2 in the Supporting Information). Among the 70 drug targets, 28 have FDA-approved cognate antibiotics targeting them, and the remaining 42 targets are still either theoretical or experimental. Six of the former group of drug targets were classified as hub proteins in the MRSA252 PIN (50S ribosomal protein L4 [YP_041689.1], 50S ribosomal protein L10 [YP_039993.1], 30S ribosomal protein S4 [YP_041184.1], 50S ribosomal protein L22 [YP_041685.1], 30S ribosomal protein S9 [YP_041655.1], translation elongation factor G [YP_040001.1]). Surprisingly, only two of the experimental targets—UDP-N-acetylmura-moylalanyl-D-glutamate-2,6-dia minopimelate ligase (YP_040406.1) and cell division protein FtsZ (YP_040573.1)—met the criterion for MRSA PIN hubs based upon their level of connectivity (169 protein interactions for YP_040406.1 and 137 for YP_040573.1). To illustrate this point graphically, in Figure 1 we have color-coded these 70 antimicrobial targets and this shows clearly that the majority of the target proteins were positioned in peripheral parts of the interactome. On Figure 1 hub proteins are shown in yellow and nonhubs in blue. Established antimicrobial drug targets are shown in red if they were classified as nonhubs and in purple if they were categorized as hubs. As it can be clearly seen, the majority of the established targets are positioned outside the hub-core of the protein network. From an absolute numeric standpoint indicating the experimentally estimated average number of protein interactions (average Network Degree, aND), it is clear that conventional antimicrobial targets (aND = 51) were closer to nonhubs (aND = 28) than they were to actual hubs (aND = 174) (see Table 3). These numbers further quantify the observation that for the most part, the current experimental antimicrobial targets do not occupy central positions in the protein interactome of MRSA. A priori, based upon the scale-free network paradigm,33,34 this observation would have suggested that these targets would have limited utility. Nevertheless, considering that these relatively poorly connected proteins can serve as effective drug targets, it is reasonable to predict that highly connected MRSA PIN hubs with essential functions likely represent an excellent opportunity for improved bacterial target selection. To illustrate further that MRSA hub proteins identified have higher hierarchical positions in the interactome when compared with existing antibacterial targets, we calculated the corresponding

Network Betweenness (NB) values for all 608 MRSA proteins identified in the PIN. This network property NB of a protein in a PIN has been shown to correlate with the protein’s importance for network stability.49 Specifically, for any protein i its NB value is defined as the fraction of all shortest paths that pass through the protein i (2). X Njk ðiÞ ð2Þ NBi ¼ Njk i6¼ j6¼ k ∈ G where Njk is the number of shortest paths between j and k in a network graph (G), and Njk(i) is the number of shortest paths between j and k that pass through a protein i. A higher NB value of a protein indicates it is located in a central position of the network where many shortest paths between other nodes pass through. Thus, the estimated NB parameters (Table 2 of the Supporting Information) can reflect the impact of each protein to the network efficiency for relaying cellular signals, and removal of proteins with higher NB values may have a greater likelihood of causing a destabilizing effect. Not surprisingly, the average NB values computed for 52 MRSA hubs and 486 nonhubs (excluding the antimicrobial targets) differed significantly (5430 versus 307). Importantly, the average NB value of 960 estimated for known antimicrobial targets not only groups them with less connected proteins but also clearly characterizes them as less efficient network nodes (see Table 3 and Figure 3 for more details). Given the importance of highly connected hub proteins to network stability and cell viability, the question arises as to why these MRSA hubs have not yet emerged more frequently as antimicrobial targets. This is very likely explained by conventional target selection approaches that are heavily biased toward essential pathogen proteins that possess broad conservation across among different bacteria and that exhibit low cellular abundance and do not have a close human ortholog. To explore this issue further, we analyzed the 70 known antimicrobial targets along with the 52 hubs and 486 nonhubs in the MRSA PIN for parameters of conservation among pathogenic bacteria, their relative cellular abundance and similarity to human sequences. To assess the degree of cross-species conservation of the MRSA proteins we selected 20 representative pathogenic bacteria (8 g-positive and 12 g-negative species) for analysis. We carried out sequence similarity searches across 20 bacterial proteomes for each MRSA protein and determined the number of species that possess at least one homologue to the query protein. In parallel, we also analyzed similarities to human 1144



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Figure 4. Separation of established drug targets and hubs and nonhubs in the MRSA PIN in the three-dimensional space of protein abundance, protein conservation and similarity to human proteins. (A) Top view of the space and (B) side view. The areas of 3D space occupied by conventional drug targets are shown within red ovals and the space occupied by MRSA hubs is shown within the yellow ovals.

proteins by comparing 608 MRSA sequences against the human proteome (and estimated the highest sequence similarity values). These parameters (Table 3) along with the estimated values of cellular abundance for each MRSA protein were then analyzed by the two-tailed t test for the three groups (hubs, nonhubs and targets). The estimated p-values are shown in Table 3 along with the corresponding group means for conservation, human similarity and relative abundance numbers. These estimates indicate that known antimicrobial drug targets can be distinguished from MRSA hubs in that they exhibit the highest conservation among bacteria, lowest cellular abundance and lowest similarity to human proteins among the sequences examined. When we plotted the 608 MRSA proteins identified in the PIN according to

conservation among bacteria, similarity to human proteins and abundance (Figure 4) we found that known antimicrobial targets form two distinct groups that are clearly segregated from MRSA hubs. This separation is consistent with the notion that current antimicrobial targets have likely been chosen using criteria of essentiality, cellular abundance, and conservation, and not based on their network characteristics. It is reasonable to propose that higher cellular abundance of some MRSA hubs and/or their higher similarity to human proteins could have contributed to the fact that only very few hubs have been targeted for drug development thus far. In general, these results demonstrate that conventional drug targets tend to group with nonhub proteins, rather than highly 1145



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Table 4. List of Hub Proteins as Potential Drug Targets for MRSA252

protein ID

protein name

has human homologue

essentiality (confirmed essential = 2,

number of interactions

(30% similarity cutoff), yes = 1, no = 0

inferred essential = 1, nonessential = 0, NA = result not available)

YP_040645.1

uridylate kinase

269

0

YP_041684.1

30S ribosomal protein S3

199

0

1

YP_041002.1

elongation factor P

197

0

1

YP_041688.1

50S ribosomal protein L23

188

0

1

YP_041115.1


184

0

1

YP_039957.1 YP_040884.1

S4 domain containing protein DNA-binding protein HU

182 176

0 0

1 2

YP_041675.1


176

0

1

YP_039994.1

50S ribosomal protein L7/L12

172

0

1

YP_040627.1

tRNA (guanine-7-)-methyltransferase

160

0

2

YP_040123.1

LysR family regulatory protein

160

0

NA

YP_041175.1

universal stress protein

159

0

NA

YP_041626.1

alkaline shock protein 23

154

0

NA

YP_040805.1 YP_040469.1

hypothetical protein SAR1403 hypothetical protein SAR1055

140 140

0 0

NA NA

YP_040450.1

hypothetical protein SAR1035

139

0

NA

YP_041723.1

ferrichrome-binding lipoprotein precursor

133

0

NA

YP_040662.1

hypothetical protein SAR1251

128

0

1

connected hubs when judged by their network degree, betweenness values and physical-chemical characteristics. On another hand, we also observed that actual drug targets in use in the clinic do possess higher network connectivity values (Table 3) and more frequently correspond to hub proteins (6 out of 28 are hubs or 21%), when compared to experimental or theoretical antimicrobial targets (2 out of 42 are hubs or 5%). Despite the fact that actual drug targets with clinical relevance have not been deliberately chosen based on their hierarchy of network connectivity, their tendency to segregate with hub proteins may be more than a random event. On the basis of what we learned from the network characteristics of known drug targets in MRSA252, it is reasonable to suggest that antimicrobial drug development can be improved by targeting highly interacting proteins. In this regard, we selected as an example one of the most highly connected proteins - pyruvate kinase (PK) as a candidate drug target. Pyruvate kinase was chosen because this protein represents an unexploited and attractive target for the development of novel classes of antibacterials. Importantly, the essentiality of pyruvate kinase has been established by both of our knockout (Targetron) and antisence RNA experiments, thus, confirming our drug target selection process. In addition, its essential and highly conserved nature of this protein among other bacteria makes this protein amenable to broad-spectrum inhibitor development. Although pyruvate kinase is also present in humans, our structural modeling revealed a unique potential binding site that could be exploited for selective targeting of the pathogen protein. Thus, using an in-house virtual screening platform, several compounds were identified that selectively inhibited S. aureus pyruvate kinase enzymatic activity as well as MRSA252 cell growth, with little or no effects on human PK enzymatic activity or human cell viability (manuscript in preparation). The >1000-fold selectivity of these compounds for the pathogen PK over the human PK illustrates there is the potential to exploit PK as a novel antimicrobial drug target.

1

The above example of pyruvate kinase illustrates the novelty and feasibility of our drug target identification strategy. Among the identified MRSA hubs (Supplementary Table 2, Supporting Information) a number of proteins such as ribosomal components, bacterial elongation factors (e.g., Translation Elongation Factor G), uridylate kinase,50 tRNA (guanine-7-)-methyltransferase,51 and glycerol kinase52 have been previously proposed as attractive drug targets. In some instances, small molecule inhibitors have been developed for hub proteins such as peptide deformylase53 and cell division protein, FtsZ.54 These reports provide additional validation for our targeting strategy for the identification of potentially novel drug targets and corresponding novel antibiotics. Table 4 lists a number of MRSA hub proteins that do not have human homologue(s) and have not yet been rigorously investigated for antimicrobial development. The essentiality for some of the selected targets has either been confirmed by our experiments in MRSA252 (such as “DNAbinding protein HU’” and “tRNA (guanine-7-)-methyltransferase”) or can be inferred from other bacterial species. Thus, these proteins represent potentially interesting targets for further investigation and validation, including the ones that do not yet have functional and/or essentiality annotations.

’ CONCLUSIONS In conclusion, we have reported 13 219 high-confidence, experimentally determined interactions involving 608 MRSA proteins and analyzed these using conventional bioinformatics and statistical tools. We found a low degree of similarity between the MRSA interaction network and previously reported data for Saccharomyces cerevisiae, Helicobacter pylori, Caenorhabditis elegans, and Escherichia coli species. The low concordance between the PINs of even closely related bacteria makes it clear that it may not be feasible to infer protein-protein interactions from the existing experimental data. Furthermore, identification of the scale-free protein-protein interaction network for MRSA allowed 1146


Journal of Proteome Research assessment of the network roles of conventional antimicrobial targets indicating their predominant nonhub character. This observation characterizes them as relatively low efficiency targets which are more likely to develop resistance. Based upon the demonstrated scale-free character of bacterial PINs along with the recent developments in network theory, a strong case can be made that conventional antibiotic target selection criteria should be adapted and expanded to consider the network properties of the protein interactome. Thus, many MRSA hubs could be considered as potential antimicrobial targets. In particular, the generated MRSA PIN data indicate that there are certain proteins that act as network hubs and yet still fulfill all usual target selection criteria, that is, they are essential for bacterial survival, have no paralogs in the MRSA genome, demonstrate low cellular abundance, and do not have close human analogues (Table 2 of the Supporting Information). Such highly connected bacterial hubs clearly represent new attractive drug development opportunities, as the discovery of specific inhibitors has the potential to expand our arsenal of antibiotics that may help address the problem of increasing antibiotic resistance.

’ EXPERIMENTAL SECTION Bait Selection Strategy

A total of 406 baits were cloned into the bacterial pGEX vector and recombinant proteins were expressed and purified for affinity pull-down experiments using standard approaches as described in detail below. We used two rounds of bait selection in order to construct the MRSA PIN. In the first round, one-third (133) of all baits were selected based on their predicted hub-like properties. This was done to maximize potential coverage of the MRSA network. Protein hub predictions were performed based on two different methods. In the first method a hub predictor was built by utilizing a machine-learning method of boosting trees to classify hub proteins based on their Gene Ontology (GO) annotations.13 In the second method,32 a QSAR-related approach was used to classify and predict hubs based on their physiochemical properties such as hydrophobicity, surface area, molecular weight, electronegativity, polarizability, surface charge, among others. In the second round of experiments, 273 baits were selected based on the scale-free network-derived strategy “name your friend” which identified the most common preys arising from the first round of pull-downs, and used these as second round baits (potential hubs). Some of second-round baits were also chosen based on their homology to known drug targets, and some were selected based on the prediction that they were likely to be hubs as previously reported.13 The sequences for all the 2656 proteins in MRSA252, used for the hub prediction and the other downstream sequence-based analyses, were obtained from the RefSeq database.19 Protein Interaction Network Analysis and Characterization of MRSA Proteins

The experimentally identified interactions between the bait and prey protein were managed in a MySQL database and visualized in a two-dimensional network graph using Cytoscape.55 The protein interaction distribution was plotted and logarithmically transformed to estimate its fitness with the power law function as defined in eq 1. The determined MRSA protein interactions were analyzed and compared with other published PIN data sets including Escherichia coli,4,30 Campylobacter jejuni,31 Treponema pallidum3

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and Saccharomyces cerevisiae.1 The interaction data sets were obtained from IntAct56 and stored in a local MySQL database. For each pair of interacting proteins that has been reported for the above organisms, we searched for a corresponding pair of highly similar proteins (using similarity criteria: BLAST alignment E-value e10-5, sequence similarity g35% and alignment coverage g65%) and then examined if their interaction was conserved in the MRSA PIN. To further characterize interacting proteins in the MRSA PIN, a number of protein properties were calculated for hubs (the top 10% of interactors), nonhubs (the other 90% of interactors) and known drug targets. The “network degree” of a given protein is the number of other proteins that have been pulled down together in the baitprey experiment. The “network betweenness” was calculated based on the eq 2. To obtain abundance estimates for various MRSA proteins, an MRSA whole-cell lysates was separated into 24 fractions by off-gel isoelectric focusing and analyzed using LC-MS/MS as described.59 The analysis was repeated in triplicate and absolute protein expression (APEX) indices were calculated as described.59 Protein essentiality was analyzed by inducible antisense RNA or gene knockout (TargeTron, Sigma) experiments for a selected number of proteins in MRSA252, while essentiality for the other proteins was inferred based on their sequence similarity (BLAST E-value e10-5, sequence similarity g35% and alignment coverage g65%) to proteins known to be essential in Staphylococcus aureus N315, Streptococcus pneumoniae, or Bacillus subtilis, as reported in Database of Essential Genes.40 In addition to the protein essentiality, we estimated the overall degree of sequence conservation by comparing each protein sequence in MRSA252 to a set of 20 bacterial proteomes (8 g-positive and 12 g-negative bacterial species) by BLAST. For any individual protein, we counted the total number of species that had at least one homologue (similarity criteria: blast E-value e10-5, sequence similarity g35% and alignment coverage g65%). MRSA252 protein sequences were also compared to the human proteome to determine whether a human homologue existed. Other properties including hydrophobicity, surface area, and frequency of flexible residues were calculated using SABLE 2.0,57 PARASURF 06,58 and several other QASAR-based approaches described in detail previously.32 A two-tailed t test was performed to compare the differences of the above protein properties for the three groups: hubs, nonhubs and targets. Statistics for mean values and t-tests comparing hubs, nonhubs and drug targets were carried out using STATISTICA 8.0 package.60 Expression and Purification of GST Bait Fusion Proteins

Genomic DNA was extracted from genome sequenced MRSA strain Sanger 252 (NRS71) obtained from NARSA (Network on Antimicrobial Resistance in S. aureus) using Dneasy Tissue Kit (Qiagen). ORFs of 449 MRSA baits were amplified by PCR using genomic DNA as template and inserted in frame at the 30 end of GST in the expression vector pGEX-6P3 (GE Healthcare). Primer sequences are available upon request. DNA segments corresponding to GST alone and GST fusion-protein for 406 MRSA baits were cloned and transformed into Escherichia coli BL-21 (DE3) (Invitrogen). For each transformation, a single fresh colony was grown overnight in L-broth (LB) containing 100 μg/mL ampicillin. This was then diluted 1/100 in 2YT broth containing 100 μg/mL ampicillin and grown at 37 C to reach an OD600 1147


Journal of Proteome Research of 0.4-0.6. The expression of the GST and GST fusion MRSA baits were induced by adding of 0.1 mM IPTG for 3 h at 20 C. Bacteria were harvested and washed in ice cold PBS, lysed in lysis buffer [20 mM Hepes pH 7.6, 100 mM KCl, 0.2 mM EDTA, 100 μg/mL lysozyme, 2 mM DTT, 20% (v/v) glycerol and 0.5% (v/v) Nonidet-40] containing EDTA-Free protease inhibitor (Complete; Roche Molecular Biochemicals). After sonication on ice the sample was then centrifuged at 20 000 g at 4 C for 20 min. Cleared lysate was incubated in the presence of 2 mM ATP and 10 mM MgCl2 for 5 min at room temperature to reduce nonspecific binding of heat shock proteins. GST protein and GST-fusion baits were purified from bacterial lysates with glutathione-Sepharose 4B beads (GE Healthcare). Beads were washed three times with 10 volumes of PBS and once with lysis buffer lacking lysozyme and DTT. Purity and physical integrity of proteins were confirmed by SDS-PAGE and Coomassie Blue staining. The amount of GST or GST-fusion proteins bound to beads was estimated by quantification of SDSPAGE gels using known amounts of BSA as standards. Preparation of MRSA Whole-Cell Extract for GST Pull-Down

MRSA strain 252 (NRS71) was cultured overnight (OD600 of 1.0) and then freshly, inoculated (OD600 of 0.1) and grown until midexponential phase (OD600 of 0.45) in brain heart infusion (BHI) medium (BD). The cells were washed with PBS and lysed for 30 min at 37 C in MRSA lysis buffer (20 mM TrisHCl pH7.5, 150 mM NaCl, 100 μg/mL lysozyme, 100 μg/mL lysostaphin, 0.04% Triton X-100, 16 μg/mL DNaseI, 1.6 mM MgCl, 0.5% NP-40, 1 mM DTT and Complete protease inhibitor). Cell debris was pelleted by centrifugation at 25 000 g for 20 min. The pellet was discarded and soluble protein extracts were filtered through a 0.8 μm filter. GST Pull-Downs of MRSA Protein Complexes

MRSA strain NRS71 soluble protein extract was prepared from cells grown in brain heart infusion (BHI) medium to midexponential phase (OD600 of 0.45) as described above. GST protein alone was used as negative control for each pull down experiment. Equal amounts (40 pmol) of freshly prepared GST protein or GST-fusion baits bound to 50 μL glutathioneSepharose 4B beads were incubated for 2 h at 4 C with 4 mL of MRSA (15 mg/mL) soluble protein extracts with continuous end-overend mixing. The beads were washed four times with 20 volumes of MRSA lysis buffer lacking lysozyme, lysostaphin DNase, MgCl2 and Triton X-100. Protein complexes released from beads after overnight cleavage of GST by 8 units of PreScission protease (GE Healthcare) were precipitated and digested in solution as described61 for LC-MS/MS. At least four pull down experiments were repeated for each of the 406 baits. The measured bait-to-pray ratios are presented in the Supplementary file for each bait. Identification of Interacting Proteins by Mass Spectrometry

Prior to proteomic analysis, digested peptides were labeled with C1H2O (for pull downs done with GST alone) or C2H2O (for pull downs done with GST-fusion proteins) in the presence of cyanoborohydride to reductively dimethylate62 primary amines and to allow later distinction of nonspecific (i.e., bound to GST alone and GST-fusion) from specific (i.e., bound only to GSTfusion) interactions. Derivatized samples were mixed and analyzed using an LTQ-FT (ThermoFisher Scientific, Bremen, Germany) as described previously.63 Fragment spectra were searched against the MRSA protein database using Mascot64

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with the following criteria: trypsin cleavage rules with up to 1 missed cleavage, ESI-TRAP fragmentation characteristics, 10 ppm parent mass accurate, 0.6 Da fragment mass accuracy, fixed modification: cysteine carbamidomethylation, variable modifications: Dimethyl (N-term), Dimethyl (K), Dimethyl:2H(4) N-term, Dimethyl:2H(4) K, methionine oxidation.63 Proteins were considered potential interactors if two or more peptides meeting the above criteria and having IonsScores g20 were sequenced in an experiment. Quantitative ratios were extracted using MSQuant65 and expressed, for a given protein, as the average ratio of all peptides detected for that protein. Within a given protein the average relative standard deviation (coefficient of variation, CV) of ratios was approximately 23%. Only proteins observed in at least two of the four biological replicates with a GST-fusion/GST alone ratio of g2 were considered as specific preys, even though a particular protein might have been seen with a ratio e2 in some replicates. Relevant details can be found in Table 4 in the Supporting Information. Co-immunoprecipitation of Protein Complexes Using F(ab0 )2 Fragments of Anti-MRSA Pyruvate Kinase (PK) Antibody

Polyclonal anti-MRSA PK antibody was produced by immunization of rabbits with purified recombinant MRSA His6-PK (Pacific Immunology Crop, CA) according to standard protocols. F(ab0 )2 fragments of purified IgG from preimmune and immune (anti-MRSA PK) sera were prepared by pepsin treatment (Pierce) and immobilized to NHS-activated Sepharose 4 fast flow (GE Healthcare) according to the manufacturer’s protocol. PK interactors were coimmunoprecipitated from 30 mg of precleared MRSA252 lysate for 2 h at 4 C using 12 μg of each immobilized preimmune (control) or immune (anti-MRSA PK) F(ab0 )2 fragments. Protein complexes released from beads after washing were precipitated and solubilized in preparation for insolution tryptic digestion. Reductive dimethylation labeling and quantitative mass spectrometry as described above were used to identify MRSA PK interacting proteins brought down by co-IP.

’ ASSOCIATED CONTENT

bS

Supporting Information Supplementary Table 1. A list of bait-to-prey interactions in MRSA252. Supplementary Table 2. A summary of interacting MRSA proteins. Supplementary Table 3. A list of antimicrobial drug targets identified in the MRSA PIN. Supplementary Table 4. MASS Spec detected peptides for the interacting proteins. Supplementary Table 5. Co-IP identified interacting partners for pyruvate kinase protein. Supplementary File. The measured bait-to-pray ratios for the studied bait proteins. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Dr. Cherkasov ([email protected]) or Dr. Reiner (ethan@ interchange.ubc.ca) UBC Division of Infectious Diseases, 2733, Heather St, Vancouver, BC V5Z3J5, Canada (tel. AC 604.876.5555 x 69628, fax 604.875.5654; tel. NR, 604-875-4011).

’ ACKNOWLEDGMENT This work was supported by funding from Genome Canada and Genome British Columbia, Vancouver General Hospital & 1148


Journal of Proteome Research University of British Columbia Hospital Foundation, and the SARS Accelerated Vaccine Initiative through the PRoteomics for Emerging PAthogen REsponse (PREPARE) Project. Computer equipment for PREPARE Project’s Computational Genomics research were also supported by in-kind contribution from IBM Healthcare and Life Sciences and laboratory space by the Vancouver Coastal Health Research Institute.

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