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Journal of Chemical Biology logoLink to Journal of Chemical Biology
. 2010 May 14;3(4):165–173. doi: 10.1007/s12154-010-0039-1

In silico identification of common putative drug targets in Leptospira interrogans

U Amineni 1,, D Pradhan 1, H Marisetty 2
PMCID: PMC2957888  PMID: 21572503

Abstract

Infectious diseases are the leading causes of death worldwide. Hence, there is a need to develop new antimicrobial agents. Traditional method of drug discovery is time consuming and yields a few drug targets with little intracellular information for guiding target selection. Thus, focus in drug development has been shifted to computational comparative genomics for identifying novel drug targets. Leptospirosis is a worldwide zoonosis of global concern caused by Leptospira interrogans. Availability of L. interrogans serovars and human genome sequences facilitated to search for novel drug targets using bioinformatics tools. The genome sequence of L. interrogans serovar Copenhageni has 5,124 genes while that of serovar Lai has 4,727 genes. Through subtractive genomic approach 218 genes in serovar Copenhageni and 158 genes in serovar Lai have been identified as putative drug targets. Comparative genomic approach had revealed that 88 drug targets were common to both the serovars. Pathway analysis using the Kyoto Encyclopaedia of Genes and Genomes revealed that 66 targets are enzymes and 22 are non-enzymes. Sixty two common drug targets were predicted to be localized in cytoplasm and 16 were surface proteins. The identified potential drug targets form a platform for further investigation in discovery of novel therapeutic compounds against Leptospira.

Keywords: Leptospirosis, Subtractive genomics approach, Novel drug targets, KEGG

Introduction

The widespread emergence of bacterial resistance to existing antibiotics is a global health threat and has emphasized the need to develop new antibacterial agents directed towards novel targets [1]. Leptospirosis is a globally widespread infectious zoonosis with more than 500,000 severe cases annually in the world [2]. Human leptospirosis is caused mainly by spirochete pathogen Leptospira interrogans [3]. People exposed to recreational activities, farming, and post-flood conditions are at major risk of getting infected through contaminated water, rodents, or pet animals [4, 5]. High prevalence of leptospiral antibodies was observed in rural areas with sanitation workers and sugarcane workers being the most frequent victims [6, 7]. Leptospirosis occurs as either anicteric leptospirosis syndromes in 85-90% of cases or icteric leptospirosis in 5-10% of cases. In anicteric leptospirosis, there are two stages, viz., septicemia stage and the immune stage. Symptoms of infection include fever, chills, headache, and severe myalgia. In 5-15% cases, multiple organ damage is reported and the mortality rate has been shown to be 5-40% [8, 9]. As number of animals act as hosts, the acquiring of infection can have considerable economic implications in developing countries like India.

Prevention of infection by controlling environmental factors is difficult to practice in developing countries. Drugs like oxytetracycline, doxycycline and penicillin have been used in management of infection. A review of clinical trials revealed inconclusive evidence on the benefit or safety of antibiotics for leptospirosis [10]. More than 200 diverse pathogenic Leptospira serovars remain as a challenge to develop an effective and safe leptospirosis vaccine [11]. Vaccines for serovars like Hardjo, Pomona, Canicola, Grippotyphosa, and Icterohaemorrhagiae have been developed and showed disadvantages like suboptimal protection, requirement of booster doses, and the need of the vaccine for local serovars [5]. Available leptospiral vaccines were relatively unsuccessful in preventing leptospirosis [11], thus underscores the need to search for a new drug target for development of effective drug against the pathogen. Subtractive genomic approach locates essential genes of the pathogen and the non-human homologous proteins which help in preventing the unexpected cross reactivity in the host. The putative common drug targets of L. interrogans serovars Copenhageni and Lai were reported in the present study through subtractive genomic approach [12] and metabolic pathway analysis [13]. The drug targets elucidating unique physiology of the versatile spirochetal pathogen were documented. It is anticipated that the identified common drug targets will illuminate understanding of the molecular mechanisms of leptospiral pathogenesis and facilitate the identification of novel drug candidates.

Materials and methods

Identification of putative common drug targets

Complete genome sequences of both the serovars of L. interrogans were retrieved from the National Centre for Biotechnology Information (ftp://ftp.ncbi.nlm.nih.gov/genome/bacteria) [14, 15]. The Database of Essential Genes (DEG) was accessed at http://tubic.tju.edu.cn/deg/ [16]. The DEG search engine parameters were set to an expectation value (E value) cut-off of 10−10 and minimum bit score cut off of 100 for screening essential genes from both Leptospira serovar genomes. These genes were searched [17] against human proteome in the National Center for Biotechnology Information (NCBI) server. The homologs were excluded and the list of non-human homologs (drug targets) was compiled. The protein products of final list of genes were obtained from Uniprot (http://www.uniprot.org/).

Metabolic pathway analysis

The involvements of drug targets in metabolic pathways were analyzed at the Kyoto Encyclopedia of Genes and Genome (KEGG) [18]. Comparative analysis of the metabolic pathways of the host and pathogen was performed to trace out drug targets involved in pathogen specific metabolic pathways.

Subcellular localization prediction

Computational prediction of the subcellular localization of proteins is a valuable tool for genome analysis and annotation in bacterial pathogens, since the prediction of proteins on the cell surface is of particular interest due to the potential of such proteins to be primary drug or vaccine targets. Proteome analyst specialized subcellular localization server v2.5 [19] was used to predict the surface membrane proteins which could be highly useful as probable vaccine targets. The predicted membrane proteins were further analyzed in psortb v3.0 [20] to confirm if the drug targets are identified as membrane proteins irrespective of the subcellular localization prediction methods.

Results and discussion

Infectious diseases are the second leading cause of death worldwide [21]. Though there is an increasing demand for new antimicrobial agents, their development is hampered due to requirement of huge investment, less market, short-term usage in same patient, and high level of competition with newly developed agents [22]. Developments in Bioinformatics have brought the algorithms, tools, and facilitated the automation of microbial genome sequencing, development of integrated databases over the internet, comparison of genomes, identification of gene product function, and paved the way for development of anti-microbial agents, vaccines, and rational drug design [23].

The L. interrogans genome consists of a 4.33-Mb chromosome I and a 350-kb chromosome II. The genome is highly conserved between the serovars Copenhageni and Lai, exhibiting 95% identity at the nucleotide level. The genome sequence of Copenhageni has 4,669 and 455 genes on chromosome I and chromosome II while that of Lai has 4,360 and 367 genes, respectively [14, 15]. In silico drug target identification mainly relies on the principle “a good drug target is a gene essential for bacterial survival yet cannot be found in host” [24]. The principle was well implemented using DEG and the NCBI non-redundant database to develop subtractive genomic approach, which had been widely used for fast screening of potential drug targets from the sequenced genomic information of emerging infectious pathogens [12]. Metabolic pathway analysis is another approach for drug target identification which focuses on enzymes of pathways unique and vital to bacteria [13]. Subtractive genomic approach was practiced to find L. interrogans common drug target followed by assessment of these targets for its vitality in pathogens’ metabolic pathway.

Sakharkar et al. [25] identified 306 essential genes in Pseudomonas aeruginosa; Dutta et al. [26] reported 178 essential genes in Helicobacter pylori, and Chan-Eng Chong et al. [27] found 312 essential genes in Burkholderia pseudomallei by using the same approach. In the present study, screening of Copenhageni whole genome has resulted 576 essential genes in chromosome I and 32 essential genes in chromosome II, while Lai has 403 and 39 essential genes in chromosome I and II, respectively. About 218 essential genes of Copenhageni and 158 essential genes of Lai were identified as human non-homologs. Each gene product can be considered as drug target to the respective pathogen serovars. Eighty eight drug targets were found common to both the serovars (Tables 1 and 2). Sixty-six common drug targets were enzymes and 22 were non-enzymes. This result also revealed the presence of 11 and 10 putative uncharacterized proteins in Copenhageni and Lai, respectively, as potential drug targets but none of them were common to both (Table 1).

Table 1.

Results of the computational analyses of Leptospira interrogans serovars Copenhageni and Lai genome

Genes/proteins Copenhageni Lai Common drug targets
Number of genes 5,124 4,727 -
Predicted essential genes product 608 442 -
Predicted non-human homologs (putative drug targets) 218 158 88
Putative uncharacterized drug targets 11 10 NIL
Predicted common drug targets - - 88
Number of enzymes as common drug target - - 66
Common drug target enzymes present in pathways of both host and pathogen - - 46
Common drug target enzymes specific to unique pathways of pathogen - - 20
Non-enzymes - - 22
Cytoplasmic proteins - - 72
Membrane protein (potential vaccine candidate) - - 16

Table 2.

Common drug targets of Leptospira interrogans serovars Copenhageni and Lai

Uniprot ID Gene product name (Copenhageni) Gene name Uniprot ID Gene product name (Lai)
Enzymes
Lipopolysaccharide biosynthesis
1 Q72P96 Lipid-a-disaccharide synthase protein lpxB Q8F752 Lipid-A-disaccharide synthase
2 Q72RV5 UDP-3-O-[3-hydroxymyristoyl] N-acetylglucosamine deacetylase lpxC Q8F3U4 UDP-3-O-[3-hydroxymyristoyl] N-acetylglucosamine deacetylase
3 Q72LT1 UDP-3-O-[3-hydroxymyristoyl] glucosamine N-acyltransferase lpxD Q8F8P2 UDP-3-O-[3-hydroxymyristoyl] glucosamine N-acyltransferase
4 P61656 2-dehydro-3-deoxyphosphooctonate aldolase kdsA Q8F3J4 2-dehydro-3-deoxyphosphooctonate aldolase
5 Q72QF5a 3-deoxy-manno-octulosonate cytidylyltransferase kdsB1 Q8F5P2a 3-deoxy-manno-octulosonate cytidylyltransferase
6 Q72RC1a Phosphoheptose isomerase gmhA Q8F4E9a Phosphoheptose isomerase
7 Q72Q34 3-deoxy-d-manno-octulosonic acid transferase kdtA Q8F636 3-deoxy-d-manno-octulosonic-acid transferase
8 Q72S48 ADP-heptose synthase rfaE2 Q8F3J1 ADP-heptose synthase
Peptidoglycan biosynthesis
9 Q72MD7 Udp-n-acetylglucosamine 1-carboxyvinyltransferase murA Q8EYY3 Udp-n-acetylglucosamine 1-carboxyvinyltransferase
10 Q72R85 UDP-N-acetylmuramate-alanine ligase murC Q8F4J0 UDP-N-acetylmuramate-alanine ligase
11 Q72NP4a UDP-N-acetylmuramoylalanine-d-glutamate ligase murD Q8F7V4a UDP-N-acetylmuramoylalanine-d-glutamate ligase
12 Q72R81 UDP-N-acetylmuramoyl-L-alanyl-d-glutamate-2,6-diaminopimelate ligase murE Q8F4J4 UDP-N-acetylmuramoyl-L-alanyl-d-glutamate-2,6-diaminopimelate ligase
13 Q72Q23 UDP-N-acetylmuramoyl-tripeptide-d-alanyl-d-alanine ligase murF Q8F648 UDP-N-acetylmuramoyl-tripeptide-d-alanyl-d-alanine ligase
14 Q72R84b UDP-N-acetylglucosamine-N-acetylmuramyl-(pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase murG Q8F4J1b UDP-N-acetylglucosamine-N-acetylmuramyl-(pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase
15 Q72R93 d-alanine-d-alanine ligase ddl Q8F4I2 d-alanine-d-alanine ligase
DNA Replication
16 Q72VS4 DNA polymerase III alpha subunit dnaE Q8F9D7 DNA polymerase III, subunit alpha
17 Q72RP3 DNA primase dnaG Q8F416 DNA primase
18 Q72WD5 DNA polymerase III beta subunit dnaN Q8FA33 DNA polymerase III beta subunit
19 Q72WD1 DNA gyrase subunit A gyrA Q8CM44 DNA gyrase subunit A
20 Q72S35 Primosomal protein N' priA Q8F3K4 Primosomal protein N'
21 Q72WD4 DNA replication and repair protein recF recF Q8FA32 DNA replication and repair protein recF
Histidine metabolism
22 P61660 Imidazoleglycerol-phosphate dehydratase hisB Q8F9R6 Imidazoleglycerol-phosphate dehydratase
23 Q72PG3 Histidinol-phosphate aminotransferase hisC Q8F6W9 Histidinol-phosphate aminotransferase
24 P62382 ATP phosphoribosyltransferase hisG Q8F6P1 ATP phosphoribosyltransferase
25 P61780 Imidazole glycerol phosphate synthase subunit hisH hisH P59118 Imidazole glycerol phosphate synthase subunit hisH
Phenylalanine, tyrosine, and tryptophan biosynthesis
26 Q72W01a Chorismate synthase aroC Q8F9N4a Chorismate synthase
27 Q72NP6 Indole-3-glycerol phosphate synthase trpC Q8F7V2 Indole-3-glycerol phosphate synthase
28 Q72PD0 Anthranilate phosphoribosyltransferase trpD Q8F708 Anthranilate phosphoribosyltransferase
29 Q72RH2 N-(5'-phosphoribosyl)anthranilate isomerase trpF Q8F495 N-(5'-phosphoribosyl)anthranilate isomerase
Protein export, Bacterial secretion system
30 Q72PD3b SecD secD Q8F706b Preprotein translocase subunit SecD
31 Q72PD4c SecF SecF Q8F705c Preprotein translocase subunit SecF
32 Q72NI1b Preprotein translocase secY subunit secY Q9XD16b Preprotein translocase secY subunit
ABC transporters
33 Q72PE3b Sulfate ABC transport system permease protein cysT Q8F6Z3b Sulfate transport system permease protein cysT
34 Q72PE4b Sulfate ABC transport system permease protein cysW Q8F6Z2b Sulfate transport system permease protein cysW
Sulfur metabolism
35 Q72W82 Serine acetyltransferase cysE Q8F9X5 Serine acetyltransferase
36 Q72R95 Homoserine O-acetyltransferase metX Q8F4I0 Homoserine O-acetyltransferase
Lysine degradation
37 Q72SS2a l-lysine 2,3-aminomutase Q8EXB5 l-lysine 2,3-aminomutase
38 Q72UX3b Penicillin-binding protein1 pbp1 Q8F6T0b Penicillin-binding protein 1A
RNA Polymerase
39 Q72NI8 DNA-directed RNA polymerase subunit alpha rpoA Q9XD09 DNA-directed RNA polymerase subunit alpha
40 Q72S41 RNA polymerase sigma-54 factor rpoN Q8F3J8 Transcription initiation factor sigma 54
Enzymes of other pathways
41 Q72W86 7,8-dihydropteroate synthase protein (Folate biosynthesis) folP Q8F9Y0 Dihydropteroate synthase
42 Q72W63 Diaminopimelate epimerase (lysine biosynthesis) dapF Q8F9V5 Diaminopimelate epimerase
43 Q72VI0 Ribose 5-phosphate isomerase B (pentose phosphate pathway) rpiB Q8F928 Ribose 5-phosphate isomerase B
44 Q72VB8 2,3-bisphosphoglycerate-independent hosphoglycerate mutase (glycolysis/gluconeogenesis) gpmI P59173 Probable 2,3-bisphosphoglycerate-independent phosphoglycerate mutase
45 P61724 6,7-dimethyl-8-ribityllumazine synthase (riboflavin metabolism) ribH Q8F8T9 6,7-dimethyl-8-ribityllumazine synthase
46 Q72S57 4-hydroxy-3-methylbut-2-enyl diphosphate reductase (terpenoid backbone biosynthesis) ispH Q8F3I3 4-hydroxy-3-methylbut-2-enyl diphosphate reductase
47 Q72RV6a Phosphotransferase system enzyme I (phosphotransferase system (PTS)) ptsI Q8F3U3 Phosphoenolpyruvate-protein phosphotransferase
48 Q72RJ9 N-acetyl-gamma-glutamyl-phosphate reductase (arginine and proline metabolism) argC P59307 N-acetyl-gamma-glutamyl-phosphate reductase
49 Q72QN9 Aspartate 1-decarboxylase (beta-Alanine metabolism @@@Pantothenate and CoA biosynthesis) Q8CVE9 Aspartate 1-decarboxylase
50 Q72Q13b Phosphatidylserine synthase (Glycerophospholipid metabolism) pssA Q8F660b Phosphatidylglycerophosphate synthase
51 Q72PZ2a Type III beta-ketoacyl synthase-like protein (Fatty acid biosynthesis) fabH Q8F686a 3-oxoacyl-[acyl-carrier-protein] synthase III
52 Q72NU6 Methylglyoxal synthase (pyruvate metabolism) mgsA Q8F7N5 Methylglyoxal synthase
53 Q72NA2 Mannose-6-phosphate isomerase (amino sugar and nucleotide sugar metabolism) manA Q8F8A2 Mannose-6-phosphate isomerase
54 Q72M22 Uroporphyrinogen-III C-methyltransferase (porphyrin and chlorophyll metabolism) cobA Q8EXQ3 Cob(I)alamin adenosyltransferase
55 Q72Q21 Phosphoribosylaminoimidazole carboxylase ATPase subunit (purine metabolism) purK Q8F650 Phosphoribosylaminoimidazole carboxylase ATPase subunit
56 Q72R77 Chemotaxis protein methyltransferase (two-component system, Bacterial chemotaxis) cheR Q8F4J9 Methylase of chemotaxis methyl-accepting proteins
57 Q72RD4 Glutamate-cysteine ligase (Glutathione metabolism) gshA Q8F4D5 Glutamate-cysteine ligase
58 Q72W44 Leucyl/phenylalanyl-tRNA-protein transferase aat Q8F9T2 Leucyl/phenylalanyl-tRNA-protein transferase
59 Q72VM0b Prenyltransferase Q8F977b UbiA prenyltransferase family protein
60 Q72QC5 Perosamine synthetase Q8F5S2 Perosamine synthetase
61 Q72RB6 NADPH-dependent 7-cyano-7-deazaguanine reductase queF Q8F4F6 NADPH-dependent 7-cyano-7-deazaguanine reductase
62 Q72RG8 Biotin synthase bioB Q8F498 Biotin synthase
63 Q72RL4 FKBP-type peptidyl-prolyl cis-trans isomerase slyD Q8F453 FKBP-type peptidyl-prolyl cis-trans isomerase
64 Q72S40 HPr kinase/phosphorylase hprK Q8F3J9 HPr kinase/phosphorylase
65 Q72SA5 Tyrosine recombinase xerD xerD Q7ZAM7 Tyrosine recombinase xerD
66 P61611 LexA repressor lexA Q8F663 LexA repressor
Non-enzymes
Ribosome proteins
1 Q72NH6 50 S ribosomal protein L6 rplF Q9XD21 50 S ribosomal protein L6
2 Q72NH7a 50 S ribosomal proteinL18 rplR Q9XD20a 50 S ribosomal protein L18
3 Q72S27 50 S ribosomal proteinL19 rplS Q8F3L4 50 S ribosomal protein L19
Cell Division proteins
4 Q72N67 FtsA ftsA Q8F8E6 Cell division protein ftsA
5 Q72R83b Cell division protein ftsW Q8F4J2c Cell division protein FtsW
6 Q72N68b Cell division protein ftsZ ftsZ Q8F8E5b Cell division protein ftsZ
Vitamin B6 metabolism proteins
7 Q72NL4a Pyridoxal phosphate biosynthesis protein pdxA Q8F7Z2a Pyridoxal phosphate biosynthetic protein pdxA
8 Q3V896 Pyridoxal phosphate biosynthetic protein pdxJ Q8F5Z9 Pyridoxal phosphate biosynthetic protein pdxJ
Translation intitiation factors
9 P61690a Translation initiation factor IF-1 infAa Q9XD14 Translation initiation factor IF-1
10 Q72PK8 Translation initiation factor IF-3 infC Q8F6Q9 Translation initiation factor IF-3
Other proteins
11 Q72RW5e Outer membrane protein Q8F3T2e Predicted outer membrane protein
12 Q72RN3 Magnesium transporter mgtE Q8F430 Magnesium transporter
13 Q72RI9 UvrABC system protein C/ Excinuclease ABC subunit C uvrC Q8F479 UvrABC system protein C
14 Q72R15c Heavy metal efflux pump czcA Q8F5X3b Cation efflux system membrane protein czcA
15 Q72QP4a Cytoplasmic membrane protein mviN Q8F5E7a Virulence factor mviN
16 Q72QQ3b Sensor protein Q8F425b Q8EXH1 Sensor protein
17 Q72Q47b Na+/H + antiporter Q8F621c Na+/H + antiporter
18 Q72PM6 Fatty acid/phospholipid synthesis protein plsX plsX Q8F6N9 Fatty acid/phospholipid synthesis protein plsX
19 Q72N21b Acriflavine resistance Q8F3G5b Acriflavine resistance protein-like protein
20 Q72LR5 ParB parB Q8EY30 ParB protein
21 Q72RK4 TldD tldD Q8F463 Q8F462 TldD protein
22 Q72WD6 Chromosomal replication initiator protein dnaA dnaA Q8FA34 Chromosomal replication initiator protein dnaA

The subcellular localization of drug targets were identified from the consensus results through predictions made by PA-SUB and psortb

aDrug targets with no hits in human

bPredicted inner membrane proteins

cPredicted inner membrane proteins with no hits in human

dPredicted outer membrane protein

ePredicted outer membrane protein with no hits in human

Comparative analysis of the metabolic pathways of the host and pathogen by using the KEGG pathway database had revealed that 20 pathways are unique to both pathogenic Leptospira serovars (Table 3). The results of pathogen specific pathways are in agreement with the findings by Anisetty et al. [13] and Barh and Kumar [28] in Mycobacterium tuberculosis and Neisseria gonorrhoeae, respectively. Seven unique pathways such as d-alanine metabolism, lipopolysaccharide biosynthesis, peptidoglycan biosynthesis, two-component system, bacterial chemotaxis, phosphotransferase system, and bacterial secretion system were observed to have 20 common drug targets (Tables 1, 2, and 3). The remaining 13 pathogen-specific pathways, namely, geraniol degradation, gamma-hexachlorocyclohexane degradation, novobiocin biosynthesis, streptomycin biosynthesis, polyketide sugar unit biosynthesis, 1- and 2-methylnaphthalene degradation, 1,2-dichloroethane degradation, benzoate degradation via CoA ligation, 3-chloroacrylic acid degradation, styrene degradation, C5-branched dibasic acid metabolism, caprolactam degradation, and flagellar assembly did not show involvement of any leptospiral putative drug target. Barh and Kumar [28] also reported four pathogen specific pathways namely; C5-branched dibasic acid metabolism, streptomycin biosynthesis, polyketide sugar unit biosynthesis and novobiocin biosynthesis did not involve any drug targets in N. gonorrhoeae. Thus, out of 66 common enzymes identified as drug target, 46 enzymes were from pathways common to host and pathogen. But the enzymes were non-homologous to human.

Table 3.

Unique metabolic pathway of Leptospira interrogans with respect to human

Sl. no. Pathway ID (Copenhageni) Pathway ID (Lai) Pathway name No. of common targets
1 lil00281 lic00281 Geraniol degradation -
2 lil00361 lic00361 gamma-Hexachlorocyclohexane degradation -
3 lil00401 lic00401 Novobiocin biosynthesis -
4 lil00473 lic00473 d-alanine metabolism 1a
5 lil00521 lic00521 Streptomycin biosynthesis -
6 lil00523 lic00523 Polyketide sugar unit biosynthesis -
7 lil00540 lic00540 Lipopolysaccharide biosynthesis 8
8 lil00550 lic00550 Peptidoglycan biosynthesis 7
9 lil00624 lic00624 1- and 2-Methylnaphthalene degradation -
10 lil00631 lic00631 1,2-Dichloroethane degradation -
11 lil00632 lic00632 Benzoate degradation via CoA ligation -
12 lil00641 lic00641 3-Chloroacrylic acid degradation -
13 lil00643 lic00643 Styrene degradation -
14 lil00660 lic00660 C5-Branched dibasic acid metabolism -
15 lil00930 lic00930 Caprolactam degradation -
16 lil02020 lic02020 Two-component system 1b
17 lil02030 lic02030 Bacterial chemotaxis 1b
18 lil02040 lic02040 Flagellar assembly -
19 lil02060 lic02060 Phosphotransferase system (PTS) 1
20 lil03070 lic03070 Bacterial secretion system 3

Thus, overall 20 enzymes reported here as drug target are from pathways unique to bacteria

aThe target (ddl) is one of the seven drug targets reported from peptidoglycan biosynthesis pathway

bThe same drug target (cheR) participates in Two-component system and Bacterial chemotaxis pathway

Considering all 88 common drug targets reported in the present study, 72 targets are cytoplasmic and 16 are membrane proteins (Tables 1 and 2). The 16 membrane proteins have the potential to act as common efficacious vaccine candidate against different strains of Leptospira. However, the membrane proteins from protein export, bacterial secretion system (SecD, SecF, and secY), ABC transporters (CysT, CysW), cell division proteins (FtsW, FtsZ), heavy metal efflux pump (CzcA), sensor protein, and Na+/H + antiporter would be highly useful based on the avalable literature evidences [2528]. We also proved CzcA as a T cell-driven subunit vaccine cadidate through computational pan-genome reverse vaccinology approach [29].

As has been mentioned, 20 enzymes are identified from the seven pathogen specific pathways. This small group of drug targets would have high impact as drug targets, as absence of these pathways in host eradicate any potential risk factors exerted by the drugs targeting these pathways. Lipopolysaccharides (LPS) and peptidoglycans biosynthesis pathways are two major pathogen-specific pathways. LPS and peptidoglycans, the main constituents of the outer cell wall of Gram-negative bacteria, play an important role in pathogenesis and antibiotic sensitivity. Eight enzymes of lipopolysaccharide pathway (LpxB, LpxC, LpxD, KdsA, KdsB1, GmhA, KdtA, and RfaE2) and seven enzymes of peptidoglycan biosynthesis pathway (MurA, MurC, MurD, MurE, MurF, MurG, and Ddl) were identified as potential common drug target in the present study. Both the pathways being unique to Gram-negative bacteria, designing potential inhibitors targeting the common drug targets of the pathways would be highly useful for controlling diseases rendered by Gram-negative bacterial pathogens by making it susceptible to osmotic lysis. The efficiency of the drug targets from the two pathways were well evident from previous studies reporting enzymes of these two pathways as drug target in various bacterial pathogens to identify novel inhibitors [3035]. Thus, the unique leptospiral drug targets identified from these two pathways would be highly useful for further potential inhibitor design against leptospirosis.

Many of the common drug target code for the basic survival mechanisms of the bacterium. The list of potential drug targets encoded in microbial genomes includes genes involved in translation, transcription, DNA replication, repair, outer membrane proteins, permeases, enzymes of intermediary metabolism, host interaction factors, and many more. Four genes aroC, trpC, trpD, and trpF reported as drug targets found to be essential components of phenylalanine, tyrosine, and tryptophan biosynthesis pathway. Therefore, targeting of these enzymes may disrupt pathways essential for Leptospira survival and virulence and therefore might be a potential antibacterial therapeutic strategy.

Ddl that participates in d-alanine metabolism is essential for the precursor of peptidoglycan backbone and metabolic pathway thus could be considered as good drug target. Phosphoenolpyruvate-protein phosphotransferase is found to be an ideal target to block the phosphotransferase system and is unique to the pathogen. Type III secretion system is a contact-dependent pathway that plays a role in host-pathogen interaction [36]. Type II secretion pathway supports the translocation of proteins associated with the virulence factors, across the outer membrane [37]. Two component systems, essential for the growth and survival in adverse environmental conditions, are ubiquitous in bacteria, and have been reported to be involved in virulent pathogen [38, 39]. Thus, SecD, SecF, SecY, and chemotaxis protein methyltransferase would be effective drug targets against Leptospira.

The computational genomics approach [13, 2528] stated herein, is likely to speed up drug discovery process by removing hindrances like dead-ends or toxicity that are encountered in classical approaches. Further, homology modeling of these targets will help in identifying the best possible sites that can be targeted for novel drug design.

Conclusion

Drug design and discovery in the post genomic era is shattering old paradigm and routinely reconstructing the drug discovery protocol by including the eons of information encoded in our genome. Subtractive genomic approach identifies putative drug targets thus presenting an opportunity to deal with a manageable number of efficient data through further pathway studies and experimental design. The identification of 88 novel drug targets in the present study provide a basis for computer-aided drug design against L. interrogans to overcome the challenges of severe leptospirosis. However, these targets should be experimentally validated for their role in bacterial survival and virulence. Qualitative tertiary structure prediction and identification of functionally important residues of these drug targets would be useful for screening novel inhibitors from millions of compounds from ligand databases. Such a strategy will enable to locate critical pathways and steps in pathogenesis; to target these steps by designing new drugs; and to inhibit the infectious agent of interest with new antimicrobial agents. The drug targets from the unique pathways would also be extended as common targets for designing inhibitors against Gram-negative bacterial pathogens.

Acknowledgments

The study was supported by grants from DBT, Ministry of Science and Technology, Government of India, New Delhi. We are grateful to Dr. B. Vengamma, Director for providing constant support and encouragement for research at SVIMS Bioinformatics Centre, where this work has been performed. We thank Prof. K. Venkateswarlu (SK University, Anantapur) for critically reading the manuscript.

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