In silico identification of common putative drug targets in Leptospira interrogans

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.

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 [25–28]. 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 [30–35]. 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, 25–28] 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.