Mycoplasma genitalium: A comparative genomics study of metabolic pathways for the identification of drug and vaccine targets

Infection, Genetics and Evolution 12 (2012) 53–62

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Mycoplasma genitalium: A comparative genomics study of metabolic pathways for the identification of drug and vaccine targets Azeem Mehmood Butt a, Shifa Tahir b,1, Izza Nasrullah c, Muhammad Idrees a, Jun Lu d,⇑, Yigang Tong e,⇑ a

Division of Molecular Virology, National Centre of Excellence in Molecular Biology (CEMB), University of the Punjab, Lahore 53700, Pakistan National Center for Bioinformatics, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan c Department of Biochemistry, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan d Tumor Biotherapy Ward, Beijing YouAn Hospital, Capital Medical University, FengTai District, Beijing 100069, People’s Republic of China e State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, People’s Republic of China b

a r t i c l e

i n f o

Article history: Received 13 June 2011 Received in revised form 7 October 2011 Accepted 10 October 2011 Available online 25 October 2011 Keywords: Mycoplasma genitalium Computational comparative genomics Subtractive genomics Metabolic pathways Drug targets Antibiotics

a b s t r a c t Increasing emergence of antibiotic-resistant pathogenic microorganisms is one of the biggest challenges for biomedical research and drug development. Traditional drug discovery methods are time-consuming, expensive and often yield few drug targets. In contrast, advances in complete genome sequencing, bioinformatics and cheminformatics represent an attractive alternative approach to identify drug targets worthy of experimental follow-up. Mycoplasma genitalium is a human parasitic pathogen that is associated with several sexually transmitted diseases. Recently, emergence of treatment-resistant isolates has been reported, which raises serious concern and a need for identification of additional drug targets. In the present study, a systematic workflow consisting of comparative genomics, metabolic pathways analysis and additional drug prioritizing parameters was defined for the identification of novel drug and vaccine targets that are essential for M. genitalium, but absent in its human host. In silico analyses and manual mining identified 79 proteins of M. genitalium, which showed no similarity to human proteins. Among these, 67 proteins were identified as non-homologous essential proteins that could serve as potential drug and vaccine targets. Subcellular localization, molecular weight, and three-dimensional structural characteristics that could facilitate filtering of attractive drug targets were also calculated for the non-homologous essential proteins. Enzymes from thiamine biosynthesis, protein biosynthesis, and folate biosynthesis were identified as attractive candidates for drug development. Furthermore, druggability of each of the identified drug targets was also evaluated by the DrugBank database. Results from this study could facilitate selection of M. genitalium proteins for entry into drug design and vaccine production pipelines. Ó 2011 Elsevier B.V. All rights reserved.

1. Introduction Mycoplasma species are the smallest free-living organisms. These organisms are unique among prokaryotes in that they lack a cell wall, a feature that is largely responsible for their biological properties, such as lack of reaction to Gram stain and susceptibility to many commonly prescribed antimicrobial agents, including b-lactams (Razin et al., 1998). Mycoplasma genitalium has a genome size of only 580,070 base pairs (bp), and is recognized as an important microbe because it has the smallest genome among any known free-living microorganism (Jensen, 2006). Hence, it has

⇑ Corresponding authors. Tel.: +86 10 6329 1028 (J. Lu), tel.: +86 10 6386 9835 (Y. Tong). E-mail addresses: [email protected] (J. Lu), [email protected] (Y. Tong). 1 Co-first author. 1567-1348/$ - see front matter Ó 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.meegid.2011.10.017

been used as a model for the study of the minimum set of genes necessary to sustain life (Gibson et al., 2008; Glass et al., 2006). Apart from this, M. genitalium is also a disease-causing pathogen. It was first isolated from humans in 1981 (Tully et al., 1981). It is a common cause of acute and chronic non-gonococcal urethritis (NGU), primarily in patients without Chlamydia trachomatis infection (Jensen, 2004). Studies in non-human primates have clearly demonstrated the pathogenicity of M. genitalium in male and female animals. M. genitalium can be isolated from infected animals and transferred to uninfected animals and cause disease. In addition, in vitro studies have demonstrated the potential for M. genitalium to attach to genital tract epithelial cells using a surface adhesin protein and then to enter the cells, leading to upregulation of cytokine genes with an associated inflammatory response (Zhang et al., 2000). M. genitalium can also attach to spermatozoa, which gives them a potential mechanism to spread to the female upper genital tract (Svenstrup et al., 2003). In light of these studies, M. genitalium has been suggested as a cause of NGU in men, and is

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A.M. Butt et al. / Infection, Genetics and Evolution 12 (2012) 53–62

associated with genital tract inflammatory diseases in women, including endometritis (Cohen et al., 2002), cervicitis (Manhart et al., 2003), pelvic inflammatory disease, and tubal factor infertility (Anagrius et al., 2005). Bacterial infections are a major cause of human morbidity and mortality worldwide. Despite global efforts to combat disease, there are still many bacterial pathogens for which no effective treatment is available, and the emergence of multi-drug resistant (MDR) bacterial strains is also a serious concern. Several recent studies have pointed out the emergence of treatment-resistant M. genitalium isolates, which is alarming (Bradshaw et al., 2006; Falk et al., 2003; Jensen et al., 2008). Before the discovery of M. genitalium associated NGU, tetracyclines such as doxycycline were the antibiotics commonly used for the treatment of NGU. However, they have failed to produce the desired results against M. genitalium. Instead, frequent persistence of this bacterium has been observed in the urethra of men treated with tetracyclines (Falk et al., 2003), and it has become clear that tetracycline treatment is responsible for some, if not all, cases of persistent or chronic NGU. More recently, another drug, azithromycin, was tested for treatment of M. genitalium. It is more active in vitro than the tetracyclines, has superior mucosal cell penetration, and all isolates are highly susceptible. Treatment trials have indicated that a 5-day course of azithromycin can eradicate the bacterium from 95% of patients (Hamasuna et al., 2005). This has led to extensive use of azithromycin against M. genitalium, and recent studies have shown the emergence of azithromycin-resistant isolates of M. genitalium. It has been suggested that resistance is due to mutations in region V of the 23S rRNA gene (Bradshaw et al., 2006; Jensen et al., 2008). This strongly indicates that there is continuing need to search for additional drug targets in the bacterial genome, which should offer less resistance in the long term. In addition, a controlled combination of more than one drug should be used therefore targeting multiple targets at a time for better treatment outcomes. During the past several years, the possibilities of selecting targets using computational approaches with integrated ‘‘omics’’ data such as genomics, proteomics, and metabolomics have been increasing continuously. Amongst these, two in silico methods, comparative genomics and subtractive genomics have been being widely used for the prediction and identification of potential drug targets in numerous pathogenic bacteria and fungi (Abadio et al., 2011; Amineni et al., 2010a; Perumal et al., 2007; Sakharkar et al., 2004). In principle, these approaches rely on searching for those genes/proteins that are absent in the host but present in the pathogen. Furthermore, these non-host homologs are essential for the survival of the pathogen, and serve as a critical component in vital physicochemical and metabolic pathways. In this way, a designed drug or a lead compound specific to such targets will only effect on the pathogen and not any aspect of the host biology. The availability of complete genome sequences of several pathogenic microorganisms has been of great assistance in this area. Combination of genome information with bioinformatics methods aims to reduce the problem of searching for potential drug targets from a large list to selecting from a chosen few. It is known that the most common mechanism of action of antibiotics is to act as inhibitors of targeted bacterial enzymes. Therefore, theoretically, all enzymes specific to bacterial systems can be considered as potential drug targets (Galperin and Koonin, 1999). In the present study, we performed an in silico metabolic pathways analysis of M. genitalium G-37 isolate and its human host. It is expected that the identified potential drug and vaccine targets will not only expand our understanding of the molecular mechanisms of M. genitalium pathogenesis but also facilitate the production of novel therapeutic agents.

2. Materials and methods 2.1. Identification of host and pathogen metabolic pathways Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database was used as a source of metabolic pathways information (Kanehisa et al., 2006, 2010). A list of metabolic pathways and identification numbers of the human host and the pathogen M. genitalium G-37 was extracted from the KEGG database and saved locally. A manual comparison was conducted and pathways that did not appear in the host but were present in the pathogen, according to KEGG database annotations were selected as pathways that were unique to M. genitalium, whereas the remaining pathways were listed as common pathways (Table 1). Proteins from common and unique pathways were identified and the respective amino acid sequences were obtained from the SwissProt database (Boeckmann et al., 2003). 2.2. Screening of non-homologous and essential proteins First, two-step comparisons were performed between host and pathogen proteomes for the identification of non-homologous proteins of M. genitalium. At first, only proteins from pathogen-specific pathways were subjected to BLASTP analysis (Altschul et al., 1997). Second, proteins from common pathways were also compared by BLASTP analysis. In each scenario, searching was restricted to proteins from humans only through an option available under BLASTP parameters. Hits were filtered on the basis of expectation value (evalue) inclusion threshold being set to 0.005, and a minimum bit score of 100. Proteins, that did not have hits below the e-value inclusion threshold of 0.005, were picked out as non-homologous proteins. After all the non-homologous proteins were identified, they were further filtered out on the basis of essentiality, that is, proteins that are essential for M. genitalium. Essential proteins of a cellular organism are necessary to live and replicate, and are therefore attractive targets for antimicrobial treatments. Information about essential proteins of M. genitalium was retrieved from the global transposon mutagenesis study (Glass et al., 2006) and from the Database of Essential Genes (DEG) (Zhang et al., 2004). E-value cut-off of 1010 and a minimum bit score of 100 were used for screening essential proteins of M. genitalium when using DEG microbial BLASTP. 2.3. Drug targets prioritization Several molecular and structural criteria (Aguero et al., 2008) that could help in prioritizing suitable drug targets were also evaluated for each of the potential drug targets. This involved calculation of molecular weight (MW) using computational tools and drug targets associated literature available at Swiss-Prot database. Transmembrane predictions were made by TMHMM server (Krogh et al., 2001), and we searched for the presence of solved 3D structures via Protein Data Bank (PDB) (Bernstein et al., 1977) and ModBase (Pieper et al., 2011). Druggability is another important target prioritization criterion, which is defined as the likelihood of being able to modulate the activity of the protein target with a small-molecule drug (Cheng et al., 2007; Keller et al., 2006). The druggability potential of each of the identified drug targets was calculated by mining DrugBank contents. The DrugBank database is a unique bioinformatics and cheminformatics resource that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information. The database contains 6796 drug entries including

A.M. Butt et al. / Infection, Genetics and Evolution 12 (2012) 53–62 Table 1 Unique and common metabolic pathways of M. genitalium with reference to humans. No.

Unique pathways

Pathways ID

Total proteins

Bacterial secretion system Phosphotransferase system

03070 02060

8 6

No.

Common pathways

01 02

ABC transporters Amino sugar and nucleotide sugar metabolism Aminoacyl-tRNA biosynthesis Base excision repair Citrate cycle (TCA cycle) Cysteine and methionine metabolism DNA replication Folate biosynthesis Fructose and mannose metabolism Galactose metabolism Glutathione metabolism Glycerolipid metabolism Glycerophospholipid metabolism Glycine, serine and threonine metabolism Glycolysis/gluconeogenesis Homologous recombination Lipoic acid metabolism Mismatch repair Nicotinate and nicotinamide metabolism Nucleotide excision repair One carbon pool by folate Oxidative phosphorylation Pantothenate and CoA biosynthesis Pentose and glucuronate interconversions Pentose phosphate pathway Propanoate metabolism Protein export Purine metabolism Pyrimidine metabolism Pyruvate metabolism propanoate metabolism Riboflavin metabolism Ribosome RNA degradation RNA polymerase Selenoamino acid metabolism Starch and sucrose metabolism Sulfur relay system Taurine and hypotaurine metabolism Thiamine metabolism Valine, leucine and isoleucine biosynthesis Valine, leucine and isoleucine degradation

Pathways ID 02010 00052

Total proteins 17 3

00970 03410 00020 00270 03030 00790 00640 00190 00480 00561 00564 00260 00010 03440 00785 03430 00760 03420 00670 00620 00770 00040 00030 00520 03060 00230 00240 00500

42 5 3 2 11 1 3 10 1 2 6 2 5 6 1 4 4 6 7 8 2 2 9 5 10 18 16 3

00740 03010 03018 03020 00450 00051 04122 00430 00730 00290 00280

2 54 5 4 4 8 2 2 2 4 1

1 2

03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

1437 FDA-approved small molecule drugs, 134 FDA-approved biotech (protein/peptide) drugs, 83 nutraceuticals and 5174 experimental drugs. Additionally, 4285 non-redundant protein (i.e. drug target/enzyme/transporter/carrier) sequences are linked to these drug entries (Knox et al., 2011). BLASTP with default parameters was used to align the potential drug targets from M. genitalium against the list of protein targets of compounds found within DrugBank. The selection criteria for filtering BLAST results were as described previously (Holman et al., 2009), that is, alignments with e-values less significant than 1  1025 were removed. 2.4. Subcellular localization prediction Prediction of biological significance and subcellular localization of the potential drug targets was carried out by CELLO v.2.5 (Yu et al., 2006) which is a multi-class support vector machine classification system. Results obtained were also cross-checked with subcellular localization predictions obtained from PSORTb v3.02 (Yu et al., 2010).

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3. Results and discussion 3.1. Identification of metabolic pathways and non-homologous proteins Here we report first computational comparative and subtractive genomics analysis of different metabolic pathways from M. genitalium, for the identification of potential drug and vaccine targets. A systematic workflow was defined that involved several bioinformatics tools, databases and drug target prioritization parameters (Fig. 1), with the goal of obtaining information about proteins that are involved in various metabolic pathways of M. genitalium, but absent in its host, therefore avoiding any potential side effects. Enzymes and non-enzymes both were considered for the identification of potential drug and vaccine targets. The initial information of M. genitalium metabolic pathways was taken from the KEGG database. At present, KEGG contains information about 43 different metabolic pathways of M. genitalium G-37 isolate that was isolated from a man with NGU (Fraser et al., 1995). Following our workflow, 4 metabolic pathways, namely, two-component system, methane metabolism, phosphotransferase system (PTS), and bacterial secretion system were identified as unique pathways of M. genitalium, whereas, 41 metabolic pathways were identified as common between M. genitalium and humans (Table 1). The genes assigned in methane metabolism and two-component system also fell into other pathways, and it is unlikely that M. genitalium employs these two pathways (Razin et al., 1998). Therefore, in this context, we did not consider the methane metabolism and two-component system as unique pathways of M. genitalium. In the next step, protein sequences from common and unique pathways were obtained and compared using NCBI BLASTP against the human proteome, for the identification of non-homologous proteins. This search resulted in identification of 79 proteins that showed ‘‘no hits’’ against the human proteome. Among these, 8 proteins were from unique and 71 from common pathways (Table S1). Initially, this information can be used for selection of drug targets; however, to minimize the time required for drug testing and development, it would be preferable to filter out these drug targets by further selection parameters.

3.2. Identification of non-homologous essential proteins Unique pathways are those that are specific to the pathogen but absent in its host. Proteins in these pathways can also be considered as unique to the pathogen and might serve as potential drug and vaccine targets. In addition, several unique or pathogen specific proteins are also known to be present in common pathways of the pathogen and host, as identified during our analysis (Table 2) and in several previous studies (Amineni et al., 2010b; Barh and Kumar, 2009; Chong et al., 2006). Similarly, we also identified that a single unique protein can also take part in multiple pathways. Proteins that are involved in more than one pathway could be more effective drug targets when, additionally, they are nonhomologous proteins. However, being unique or non-human and involved in metabolic pathways are not the sole criteria for selecting favorable drug targets. Identification of proteins that regulate key factors, such as nutrient uptake, virulence and pathogenicity, is of great importance for disruption of pathogen functions and existence. Such proteins are termed as essential for the pathogen. Again, not all essential proteins are non-homologous in nature. Therefore, pathogen proteins that fulfill the criteria of being unique and essential at the same time represent more attractive drug targets. As mentioned in Section 2.2, global transposon mutagenesis of M. genitalium reported 382 protein-coding essential genes (Glass et al., 2006). However, it is not yet known how many of these gene

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Fig. 1. Schematic representation of steps involved in computational comparative genomics-based target identification in bacterial pathogens. Identified targets can be used to develop drugs or vaccines, depending on their subcellular localization and drug target prioritization parameters.

products are non-homologous to human proteins, or to which metabolic pathways they are mapped. We therefore compared the 79 non-homologous proteins of M. genitalium to the list of 382 essential proteins. This comparison was performed by using microbial BLASTP from DEG and by manual curation. We identified 67 essential proteins of M. genitalium, which were also non-homologous to human proteins. Among these, 6 essential proteins mapped to unique pathways and 61 to common pathways (Table 2). All these non-homologous essential proteins represent an attractive dataset that could be exploited for future drug design and vaccine production against M. genitalium. 3.3. Subcellular localization and drug target prioritization Previous studies using computational comparative/subtractive genomics have focused mainly on determining whether a non-human homolog is also an essential protein (Amineni et al., 2010b; Chhabra et al., 2010; Chong et al., 2006). Although this is an important and major criterion that we also considered during our study, several additional factors also determine the suitability of a drug and vaccine target. The major factors are: subcellular localization of a target; accessibility value of a target protein; preferred to be of low MW (<100 kDa); whether a potential drug is a transmem-

brane protein; druggability; and availability of 3D structural information (Aguero et al., 2008; Caffrey et al., 2009; Crowther et al., 2010). Incorporation of such additional details can greatly improve the screening of potential drug and vaccine targets. Therefore, after the non-homologous essential proteins of M. genitalium were identified, they were further characterized using the above-mentioned parameters. It has been suggested that smaller proteins are more likely to be soluble and easier to purify, and membrane proteins are avoided as drug targets because these can be difficult to purify (Duffield et al., 2010). The MW for each potential drug target was calculated using online tools and confirmed with the available literature. Most of the identified non-homologous essential proteins had MW less than or in the range of 100–110 kDa, which indicates that these target proteins can be experimentally studied for drug development (Table 2). Subcellular localization is a key functional attribute of a protein. Cellular functions are often localized in specific compartments; therefore, predicting the subcellular localization of unknown proteins could be used to obtain useful information about their functions, and to select proteins for further study. Moreover, studying the subcellular localization of proteins is also helpful in understanding disease mechanisms and developing novel drugs (Wang

Table 2 Non-homologous essential proteins of M. genitalium identified from unique and common metabolic pathways with reference to humans as potential drug and vaccine targets. The subcellular localizations are based on the consensus results through predictions made by the CELLO and PSORTb UniProt ID

Length

Non-homologs targets (enzymes and non-enzymes)

Associated metabolic pathways

Subcellular localization

MW (kDa)

TMD

PDB

ModBase models

1 2 3 4 5 6 7

P35888 P47301 P47416 P47702 P47318 P47668 P47315

437 123 475 385 806 572 908

aa aa aa aa aa aa aa

Chromosomal replication initiation protein (dna A) Preprotein translocase subunit (SecE) Preprotein translocase subunit (SecY) Inner membrane protein translocase component (YidC) Preprotein translocase subunit (SecA) Phosphoenolpyruvate-protein phosphotransferase (EC:2.7.3.9) (ptsI) PTS system, glucose-specific II ABC component (EC:2.7.1.69) (ptsG)

Cytoplasmic Membrane Membrane Membrane Cytoplasmic Cytoplasmic Membrane

50.77 14.48 51.86 44.23 91.59 64.21 98.42

No 1 10 6 No No 10

Yes No No No No No No

No No Yes No No Yes No

8 9 10 11 12 13 14 15

P47291 P47290 P47289 Q49410 P47323 P47324 P47427 P47337

483 284 285 368 407 376 420 160

aa aa aa aa aa aa aa aa

Spermidine/putrescine ABC transporter Spermidine/putrescine ABC transporter (potC) Spermidine/putrescine ABC transporter (potB) Phosphonate ABC transporter Oligopeptide ABC transporter, permease protein (OppB) Oligopeptide ABC transporter, permease protein (OppC) Metal ion ABC transporter, permease protein Single-strand binding protein family (ssb)

Membrane Membrane Membrane Membrane Membrane Membrane Membrane Cytoplasmic

54.86 31.68 32.11 42.26 45.48 41.30 47.58 17.96

1 6 6 0 6 6 6 0

No No No Yes No No No No

No No No No No No No No

16

Q49406

291 aa

50 –30 Exonuclease

Cytoplasmic

33.27

0

No

No

17

P47247

380 aa

DNA polymerase III, beta subunit (EC:2.7.7.7) (dnaN)

Cytoplasmic

42.39

0

No

No

18 19

P47477 P47277

291 aa 1451 aa

Apurinic endonuclease (APN1) DNA polymerase III PolC (EC:2.7.7.7) (polC)

Cytoplasmic Cytoplasmic

32.40 167.5

0 0

No No

No Yes

20

P47253

254 aa

DNA polymerase III subunit delta (EC:2.7.7.7)

Cytoplasmic

29.39

0

No

No

21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

P47340 P47385 P47612 P47396 P47399 P47402 P47405 P47408 P47397 P47412 P47682 P47339 P47338 P47336 P36255

468 569 385 106 106 144 200 108 257 184 119 150 105 208 122

Replicative DNA helicase (dnaB) Metallo-beta-lactamase superfamily protein Thiamine biosynthesis protein (ThiI) 30S Ribosomal protein S10 (rpsJ, nusE) 50S Ribosomal protein L23 (rplW) 50S Ribosomal protein L22 (rplv) 50S Ribosomal protein L29 (rpmc) 50S ribosomal protein L24 (rplx) 50S Ribosomal protein L3 (rplC) 50S Ribosomal protein L6 (rplf) 50S Ribosomal protein L19 (rplS) 50S Ribosomal protein L9 (RplI) 30S Ribosomal protein S18 (rpsR) 30S Ribosomal protein S6 (rpsF) 50S Ribosomal protein L7/L12 (rplL)

DNA replication Bacterial secretion systema Bacterial secretion systema Bacterial secretion systema Bacterial secretion systema Phosphotransferase systema Glycolysis/gluconeogenesis Starch and sucrose metabolism Amino sugar and nucleotide sugar Phosphotransferase systema ABC transporters ABC transporters ABC transporters ABC transporters ABC transporters ABC transporters ABC transporters DNA replication Mismatch repair Homologous recombination Purine metabolism Pyrimidine metabolism DNA replication Base excision repair Nucleotide excision repair Homologous recombination Purine metabolism Pyrimidine metabolism DNA replication Mismatch repair Homologous recombination Base excision repair Purine metabolism Pyrimidine metabolism DNA replication Mismatch repair Homologous recombination Purine metabolism Pyrimidine metabolism DNA replication Mismatch repair Homologous recombination DNA replication RNA degradation Thiamine metabolism Ribosome Ribosome Ribosome Ribosome Ribosome Ribosome Ribosome Ribosome Ribosome Ribosome Ribosome Ribosome

Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasm Membrane Cytoplasmic Cytoplasm Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic

53.89 64.21 44.19 11.97 11.89 16.15 23.29 12.15 28.51 20.54 13.86 17.39 12.47 24.28 13.06

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

No No No No No No No No No No No No No No No

No No No No No No No No No No No No Yes Yes Yes

aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa

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No.

58

Table 2 (continued) UniProt ID

Length

Non-homologs targets (enzymes and non-enzymes)

Associated metabolic pathways

Subcellular localization

MW (kDa)

TMD

PDB

ModBase models

36 37 38 39 40 41 42

P36263 P47328 P47553 P47424 P47411 P47413 P47269

162 226 205 123 141 115 288

50S Ribosomal protein L10 (rplJ) 50S Ribosomal protein L1 (rplA) 30S Ribosomal protein S4 (rpsD) 50S Ribosomal protein L17 (rplQ) 30S Ribosomal protein S8 (rpsH) 50S Ribosomal protein L18 (rplR) Fructose-1,6-bisphosphate aldolase, class II (EC:4.1.2.13)

Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic

18.26 24.69 23.95 14.11 15.81 12.93 31.31

0 0 0 0 0 0 0

No No No No No No No

No Yes Yes Yes Yes Yes Yes

43

P47669

507 aa

Phosphoglyceromutase (EC:5.4.2.1) (pgm)

Cytoplasmic

56.87

0

No

Yes

44 45 46

P47636 P47271 P47541

152 aa 298 aa 320 aa

Ribose-5-phosphate isomerase B (EC:5.3.1.6) Glycosyl transferase, group 2 family protein Phosphotransacetylase (EC:2.3.1.8) (eutD)

Cytoplasmic Cytoplasmic Cytoplasmic

16.9 35.05 35.47

0 2 0

No No No

Yes Yes Yes

47

P47599

393 aa

Acetate kinase (EC:2.7.2.1) (acka)

Cytoplasmic

44.35

0

No

Yes

48 49 50 51 52 53

Q49408 P47593 P47644 P47645 P47643 P47489

478 184 102 292 208 239

NADH oxidase (EC:1.6.99.3) (Nox) Inorganic pyrophosphatase (EC:3.6.1.1) (ppa) F0F1 ATP synthase subunit C (EC:3.6.3.14) (atpE) F0F1 ATP synthase subunit A (EC:3.6.3.14) (atpB) F0F1 ATP synthase subunit B (EC:3.6.3.14) (atpF) Putative glycerol-3-phosphate acyltransferase (PlsY)

Cytoplasmic Cytoplasmic Membrane Membrane Membrane Membrane

53.18 21.65 10.6 33.18 24.61 27.49

0 0 2 7 2 6

No No No No No No

Yes Yes Yes Yes No No

54

P47268

145 aa

DNA-directed RNA polymerase subunit delta (EC:2.7.7.6) (rpoE)

Cytoplasmic

17.03

0

No

No

55

P47471

340 aa

Cytoplasmic

39.16

0

No

Yes

56

P47473

721 aa

Ribonucleotide-diphosphate reductase subunit beta (EC:1.17.4.1) (nrdF) Ribonucleotide-diphosphate reductase subunit alpha (EC:1.17.4.1)

Cytoplasmic

82.26

0

No

Yes

57 58 59 60 61 62

P47572 P47672 P47252 P47374 P47623 P47482

217 243 210 259 248 350

aa aa aa aa aa aa

Ribosome Ribosome Ribosome Ribosome Ribosome Ribosome Glycolysis/gluconeogenesis Pentose phosphate pathway Pentose phosphate pathway Fructose and mannose metabolism Glycolysis/gluconeogenesis Biosynthesis of secondary metabolites Microbial metabolism in diverse environments Pentose phosphate pathway Fructose and mannose metabolism Taurine and hypotaurine metabolism Pyruvate metabolism Propanoate metabolism Taurine and hypotaurine metabolism Pyruvate metabolism Propanoate metabolism Oxidative phosphorylation Oxidative phosphorylation Oxidative phosphorylation Oxidative phosphorylation Oxidative phosphorylation Glycerolipid metabolism Glycerophospholipid metabolism Purine metabolism Pyrimidine metabolism RNA polymerase Purine metabolism Pyrimidine metabolism Purine metabolism Pyrimidine metabolism Pyrimidine metabolism Pyrimidine metabolism Pyrimidine metabolism Nicotinate and nicotinamide metabolism Nicotinate and nicotinamide metabolism Nicotinate and nicotinamide Metabolism

Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic

24.75 26.74 24.25 29.2 28.19 33.56

0 0 0 0 0 0

No No No No No No

Yes Yes Yes No Yes Yes

63 64 65 66 67

875324 P47470 P47534 P47618 P47525

114 160 900 537 483

aa aa aa aa aa

Pantothenate and CoA biosynthesis Folate biosynthesis one carbon pool folate Aminoacyl-tRNA biosynthesis Aminoacyl-tRNA biosynthesis Aminoacyl-tRNA biosynthesis

Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic Cytoplasmic

13.16 18.97 104.3 62.42 55.88

0 0 0 0 0

No No No No No

Yes Yes Yes Yes Yes

aa aa aa aa aa aa aa

aa aa aa aa aa aa

Cytidylate kinase (EC:2.7.4.14) (cmk) Uridylate kinase (pyrH) Thymidylate kinase (EC:2.7.4.9) (tmk) Inorganic polyphosphate/ATP-NAD kinase (EC:2.7.1.23) (ppnK) NH(3)-dependent NAD+ synthetase, putative Putative nicotinate-nucleotide adenylyltransferase (EC:2.7.7.18) (nadD) 40 -Phosphopantetheinyl transferase (EC:2.7.8.7) (acpS) Dihydrofolate reductase (EC:1.5.1.3) (dhfR) Alanyl-tRNA synthetase (EC:6.1.1.7) (alas) Arginyl-tRNA synthetase (EC:6.1.1.19) (argS) Prolyl-tRNA synthetase (EC:6.1.1.15) (pros)

TMD: transmembrane domain; PDB: protein data bank; MW: molecular weight. a Represents targets from unique pathways of M. genitalium.

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et al., 2005). All bacterial proteins are synthesized in the cytoplasm, and most remain there to carry out their unique functions. Other proteins, however, contain export signals that direct them to other cellular locations. In Gram-positive bacteria, these include the cytoplasmic membrane, cell wall and extracellular space, and in Gram-negative bacteria, they include the cytoplasmic membrane, the periplasm, the outer membrane and the extracellular space. In most cases, the whole protein is located in a single compartment; however, proteins can also span multiple localization sites (Gardy and Brinkman, 2006). The subcellular localization of 67 non-homologous essential proteins of M. genitalium was evaluated by the CELLO server and further crosschecked by PSORTb. Consideration of all non-homologous essential proteins as potential drug targets, 52 proteins predicted to be cytoplasmic and 16 as membrane proteins. Transmembrane predictions were made by using TMHMM. Out of 67 non-homologous essential proteins, 16 proteins predicted to have transmembranes. The TMHMM results were in agreement with the subcellular localization predications, where all the transmembrane proteins were found to be membrane bound (Table 2). Bacterial cell surface and secreted proteins are of interest for their potential as vaccine candidates or as diagnostic targets (Thompson et al., 2008). These 16 membrane proteins of M. genitalium have the potential to act as common vaccine candidates, and might be active against bacterial species in which these non-homologous essential proteins are evolutionarily conserved.

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3.5. Therapeutic targets from unique pathways PTS and bacterial secretion system are the two metabolic pathways that were identified as unique to M. genitalium. These pathways are critical for growth and survival of the organism in extreme conditions. Therefore, proteins from the PTS and bacterial secretion system pathways are already known to act as drug targets in different bacteria. (Chhabra et al., 2010; Gotoh et al., 2010). Our results are also in parallel to previous studies such as two membrane proteins, preprotein translocase subunits (SecE and SecY) which were identified from the bacterial secretion system of M. genitalium have also been proposed as potential drug and vaccine targets for other bacterial species including Neisseria gonorrhoeae (Barh and Kumar, 2009), Burkholderia pseudomallei (Chong et al., 2006) and Staphylococcus aureus (Morya et al., 2010). The PTS is a major mechanism used by bacteria for uptake of carbohydrates, particularly hexoses, hexitol, and disaccharides, where the source of energy is from PEP and involves enzymes of the plasma membrane and cytoplasm. Two enzymes, phosphoenolpyruvate–protein phosphotransferase (ptsI) and glucosespecific II ABC component (ptsG) of M. genitalium were identified as drug targets (Table 2). Drug potential of these two enzymes and PTS is also evident from previous studies (Galperin and Koonin, 1999; Morya et al., 2010) and we propose that these enzymes can be targeted for drug development against M. genitalium. 3.6. Therapeutic targets from common pathways

3.4. Druggability of therapeutic targets Druggability of each of the non-homologous essential proteins of M. genitalium was identified by sequence similarity to the targets of small-molecule drugs by utilizing the DrugBank database. A local copy of the DrugBank database was downloaded and a BLASTP search was performed to align the non-homologous essential proteins to the list of drug-targeted proteins from DrugBank. This led to the identification of 31 M. genitalium proteins that were highly similar to the binding partners of FDA-approved drugs, experimental small-molecule compounds, or nutraceutical compounds which supports the potential of comparative genomics in drug discovery. Among these 31 M. genitalium proteins, 2 were from unique pathways and 29 from common pathways (Table 3). This comparison with drug-targeted proteins additionally produced a list of approved drug and drug-like compounds that bind to proteins with similar sequences to those of M. genitalium. Although protein sequence similarity does not guarantee identical structures or binding pockets, it seems reasonable that careful filtering of this set could reveal a panel of potential binding compounds primed for optimization and derivatization using traditional medicinal chemistry (Holman et al., 2009). This opens the interesting possibility of applying bioinformatics analysis to bypass a portion of the tedious de novo drug development pipeline. Finally, we searched the non-homologous essential proteins of M. genitalium for the presence of 3D structures and/or 3D structures complexed with a ligand, inhibitor or drug. Threedimensional structural information for each of the non-homologous essential proteins was retrieved from PDB and ModBase. Out of 67 non-homologous essential proteins of M. genitalium, chromosomal replication initiation protein (dna A) from the DNA replication and phosphate ABC transporter substratebinding protein (P37) from ABC transporters system were the only two with experimentally determined 3D structures. However, 33 out of 67 proteins were found to have 3D models in ModBase (Table 2). Such structural information could enhance the druggability value by facilitating a structure-based drug design, including homology modeling, docking, virtual screening or pharmacophore-based screening.

In addition to drug targets from pathogen specific pathways, several drug and vaccine targets were also identified from metabolic pathways that are common to both M. genitalium and humans. Several of these targets were found to be involved in multiple pathways and targeting these proteins will lead to development of more potent antibiotics against M. genitalium (Table 2). One such potential drug target that we identified is thiamin phosphate kinase (thiL) that belongs to thiamine biosynthesis pathway (Table 2). Thiamin (vitamin B1) is indispensable for the activity of the carbohydrate and branched-chain amino acid metabolic enzymes in its active form thiamin diphosphate (ThDP). Therefore, it is an essential cofactor for all organisms (Begley et al., 1999; Settembre et al., 2003). Synthesis of prokaryotic ThDP is a two-step process that involves the formation of thiazole moiety (4-methyl5-b-hydroxyethyl thiazole phosphate or THZ-P) and pyrimidine moiety (4-amino-5-hydroxymethyl-2-methyl-pyrimidine pyrophosphate or HMP-PP) separately (Begley et al., 1999). During the first step, THZ-P is derived from an oxidative condensation of tyrosine, cysteine, and 1-deoxy-D-xylulose 5-phosphate (DXP) via seven different genes including thiL. In the second step, THZ-P and HMP-PP are coupled to form thiamin monophosphate (ThMP) that is mediated by thiamin phosphate synthase (ThiE); while thiL catalyzes a final phosphorylation step to yield ThDP, the active form of thiamin (Du et al., 2011). Involvement of thiL in these two steps clearly demonstrates its importance and significance as a potential drug target. In addition, thiL and other genes from the thiamin metabolism pathways of Plasmodium falciparum (Muller et al., 2010) and Mycoplasma tuberculosis (Sassetti et al., 2003) have also been identified as essential for survival and as potential novel drug targets. ThiL of M. genitalium is a low molecular weight cytoplasmic enzyme. Therefore, designing inhibitors against thiL to block the biosynthesis of thiamin represents an attractive strategy which will damage the growth and survival of M. genitalium. As it is also non-homologous to humans, targeting this enzyme will hypothetically have no adverse effects on humans. We are currently performing in depth computational molecular modeling and screening of inhibitors of the thiL as novel drug target against M. genitalium.

Uniprot ID

M. genitalium proteins

Associated metabolic pathways

DrugBank ID

DrugBank targets

DrugBank organisms

1

P47318

Preprotein translocase subunit

DB03431

Preprotein translocase subunit

B. subtillus

2

P35888

DB03431

Chromosomal replication initiator protein

A. aeolicus

3

P47668

Phosphotransferase systemb

DB08357

P47291 P47247

DB03566 DB06998

6

P47385

Metallo-beta-lactamase superfamily protein

ABC transporters Purine metabolism Pyrimidine metabolism DNA replication Mismatch repair Homologous recombination DNA Replication RNA degradation

Phosphoenolpyruvate-protein phosphotransferase Spermidine/putrescine-binding protein DNA polymerase III subunit beta

Acinetobacter sp

4 5

Chromosomal replication initiation protein DnaA Phosphoenolpyruvate-protein phosphotransferase Spermidine/putrescine ABC transporter DNA polymerase III, beta subunit

Bacterial secretion systemb Protein export DNA replication

Metallo-beta-lactamase L1

F. gormanii

7 8 9 10 11 12 13 14 15 16

P47396a P47402a P47405 P47408 P47397a P47412 P47682 P47338 P47336 P36263

30S 50S 50S 50S 50S 50S 50S 30S 30S 50S

Ribosome Ribosome Ribosome Ribosome Ribosome Ribosome Ribosome Ribosome Ribosome Ribosome

30S 50S 50S 50S 50S 50S 50S 50S 30S 50S

E. coli D. radiodurans D. radiodurans D. radiodurans S. pyogenes serotype M1 D. radiodurans D. radiodurans D. radiodurans T. thermophilus S. flexneri

17 18

P47328 P47553a

50S Ribosomal protein L1 30S Ribosomal protein S4

Ribosome Ribosome

19 20 21

P47424 P47411 P47269

50S Ribosomal protein L17 30S Ribosomal protein S8 Fructose-1,6-bisphosphate aldolase, class II

22

P47636

Ribose-5-phosphate isomerase B

23 24 25 26 27

P47271 P47593 P47644 P47572 P47252

Glycosyl transferase, group 2 family protein Inorganic pyrophosphatase F0F1 ATP synthase subunit C cytidylate kinase Thymidylate kinase

Ribosome Ribosome Glycolysis/gluconeogenesis Pentose phosphate pathway Fructose and mannose metabolism Pentose phosphate pathway Biosynthesis of secondary metabolites Fructose and mannose metabolism Oxidative phosphorylation Oxidative phosphorylation Pyrimidine metabolism Pyrimidine metabolism

28

P47374

Inorganic polyphosphate/ATP-NAD kinase

29

P47482

Nicotinate-nucleotide adenylyltransferase

30 31

875324 P47470

40 -Phosphopantetheinyl transferase Dihydrofolate reductase

Ribosomal Ribosomal Ribosomal Ribosomal Ribosomal Ribosomal Ribosomal Ribosomal Ribosomal Ribosomal

protein protein protein protein protein protein protein protein protein protein

S10 L22 L29 L24 L3 L6 L19 S18 S6 L10

Represents approved drug targets. Represents targets from unique pathways of M. genitalium.

Nicotinate and nicotinamide Metabolism Nicotinate and nicotinamide Metabolism Pantothenate and CoA biosynthesis Folate biosynthesis

DB02032; B02365; DB04740; B07939; DB08069; B08199; DB08702 DB00698 DB00199; B00207; DB01369 DB08769 DB08769 DB01256 DB08769 DB08769 DB08769 DB08185 DB00778; B01190; DB01211; B01369; DB01627 DB03684 DB00254; B00256; DB00453; B00595; DB00618; DB01017 DB08769 DB08185 DB00131; B02778; DB02848; B04175; DB04493; B07270; DB07312; B07321; DB08484 DB03108; B04496

Ribosomal Ribosomal Ribosomal Ribosomal Ribosomal Ribosomal Ribosomal Ribosomal Ribosomal Ribosomal

protein protein protein protein protein protein protein protein protein protein

S10 L22 L29 L24 L3 L6 L19 L18 S6 L10

E. coli E. coli

50S Ribosomal protein L1 30S Ribosomal protein S4

T. thermophilus E. coli

50S Ribosomal protein L17 30S Ribosomal protein S8 Fructose-1,6-bisphosphatase 1

D. radiodurans T. thermophilus H. sapiens

Ribose-5-phosphate isomerase B

M. tuberculosis

Glycosyl transferase Inorganic pyrophosphatase ATP synthase C chain, sodium ion specific Cytidylate kinase Thymidylate kinase

DB02065; B02976 DB02212 DB03143 DB02456; B02883; DB03403; B04555 DB01643; B02452; B03280; DB03666; B03846; DB04160; B04184; DB04485 DB01907

Inorganic polyphosphate/ATP-NAD kinase

N. meningitidis E. coli B. pseudomallei P. modestum E. coli M. tuberculosisS. flexneri M. tuberculosis

DB04099

Nicotinate-nucleotide adenylyltransferase

B. subtillus

40 -Phosphopantetheinyl transferase Dihydrofolate reductase

B. subtillus E. coli

DB01992 DB00157; B00205; DB00642; B01093; DB02104; B02427; DB02919; B03060; DB03461; B03695; DB07140; B07141; DB08234; B08406;

B00440; B00563; DB01131; B01157; DB02559; B02583; DB03125; B03351; DB03886; B03987; DB07142; B07144; DB08448; B08642

A.M. Butt et al. / Infection, Genetics and Evolution 12 (2012) 53–62

a b

No

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Table 3 Non-homologous essential proteins of M. genitalium similar to binding partners of FDA approved drugs, experimental small molecule compounds, or nutraceutical compounds as inferred from DrugBank database using BLASTP.

A.M. Butt et al. / Infection, Genetics and Evolution 12 (2012) 53–62

In this study, we identified three Aminoacyl-tRNA synthetases (AaRS) namely Alanyl-tRNA synthetase, Arginyl-tRNA synthetase, and Prolyl-tRNA synthetase as antibacterial drug targets against M. genitalium. These enzymes showed no homology to human proteins and were also identified as essential for M. genitalium protein biosynthesis pathway (Table 2). During the past few years, AaRS enzymes have received much attention for antibacterial drug discovery due to their crucial role in protein biosynthesis (Hurdle et al., 2005). In addition, there are several features which makes them an attractive targets for drug discovery, such as; (i) presence of considerable evolutionary divergences between prokaryotic and eukaryotic enzymes thus making them ideal candidates whose inhibition will not likely affect human enzymes (Ibba and Soll, 2000; Raczniak et al., 2001). (ii) The respective synthetases are highly conserved across different bacterial pathogens, which make possible the discovery of broad-spectrum antibiotics (Ibba and Soll, 2000; Raczniak et al., 2001). (iii) The full complement of 20 synthetases is found in most bacterial pathogens and may represent 20 independent antibacterial targets (Ibba and Soll, 2000; Raczniak et al., 2001). (iv) These enzymes are soluble, stable, and easy to purify in large quantities from recombinant expression systems and can be assayed by one or more conventional methods which are amenable to high-throughput screening (Gallant et al., 2000; Macarron et al., 2000). (v) X-ray crystal structures for most of the synthetases have been solved and provide a platform for rational drug design (Ibba and Soll, 2000; Pohlmann and Brotz-Oesterhelt, 2004). It has been observed that the inhibition of these enzymes leads to disruption of protein biosynthesis, which in turn results in the attenuation of bacterial growth under both in vitro and infectious conditions (Tao et al., 2000). We, therefore, propose that inhibition of alanyl-tRNA synthetase, arginyl-tRNA synthetase, and prolyl-tRNA synthetase can lead to disruption of M. genitalium protein synthesis and associated infections with no side effects on its human host. Studies are currently underway in our lab to define three dimensional structures of these enzymes and virtual screening of potent inhibitors which will greatly aid in development of novel inhibitors against M. genitalium. Folate biosynthesis is another important biochemical pathway that is well known for its therapeutic potential and has been exploited since 1940 using inhibitors of dihydropteroate synthase (DHPS) and dihydrofolate reductase (DHFR). Enzymes of this pathway have been under consideration for the treatment of infections caused by different bacteria such as Plasmodium falciparum (Hyde, 2005), Pneumocystis carinii (Smith et al., 2002), etc. DHFR is a ubiquitous enzyme that has wide-scale application as a drug target. DHFR is responsible for the reduction of dihydrofolate to tetrahydrofolate, an important co-factor in the biosynthesis of thymine. It has been observed that the inhibition of DHFR leads to cell death through lack of thymine as the cell has no alternative (Zuccotto et al., 1998). As folate biosynthesis is a common pathway, there is possibility of its enzymes to show homology between host and pathogen. However, based on our comparative analysis, we suggest that DHFR of M. genitalium (Table 2) holds strong therapeutic potential because it turned out to be non-homologous and essential for bacterial survival. In addition to above mentioned drug targets, several other novel targets were also identified from common pathways of M. genitalium based on our comparative genomics approach. These potential drug targets are listed in Table 2 along with their drug target prioritization parameters. Computational genomic approaches have already facilitated the search for potential drug targets against many pathogens. The present study has thus led to the identification of several proteins that can be targeted for effective drug design and vaccine development against M. genitalium. As several of the identified drug targets have been reported to play a role in bacterial pathogenicity, essen-

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tial nutrients uptake, etc., therefore, a systematic approach to develop drugs against these targets can be adapted for treating M. genitalium infections. We are currently investigating potential of identified drug targets at both computational and experimental levels. In addition, several other bacterial species are also under investigation for the identification of drug and vaccine targets using comparative genomics approach. It is expected that the drugs developed against identified targets will be specific to the pathogen, and should have less toxicity for the host. Conflicts of Interest None declared. Acknowledgements This work was supported by the Hi-Tech Research and Development (863) Program of China (No. 2009AA02Z111), the National Natural Science Foundation of China (No. 30872223) and by the Beijing Natural Science Foundation (7092044-J.L) and ‘‘215’’ highlevel health technology Project (2011-J.L). We are thankful to Mr. Muhammad Saad Noon (Research Assistant, University of Maryland, USA) for his help with literature collection. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.meegid.2011.10.017. References Abadio, A.K., Kioshima, E.S., Teixeira, M.M., Martins, N.F., Maigret, B., Felipe, M.S., 2011. Comparative genomics allowed the identification of drug targets against human fungal pathogens. BMC Genomics 12, 75. Aguero, F., Al-Lazikani, B., Aslett, M., Berriman, M., Buckner, F.S., Campbell, R.K., Carmona, S., Carruthers, I.M., Chan, A.W., Chen, F., Crowther, G.J., Doyle, M.A., Hertz-Fowler, C., Hopkins, A.L., McAllister, G., Nwaka, S., Overington, J.P., Pain, A., Paolini, G.V., Pieper, U., Ralph, S.A., Riechers, A., Roos, D.S., Sali, A., Shanmugam, D., Suzuki, T., Van Voorhis, W.C., Verlinde, C.L., 2008. Genomicscale prioritization of drug targets: the TDR targets database. Nat. Rev. Drug. Discov. 7, 900–907. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J., 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402. Amineni, U., Pradhan, D., Marisetty, H., 2010a. In silico identification of common putative drug targets in Leptospira interrogans
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