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World Journal of Pharmaceutical Sciences
ISSN (Print): 2321-3310; ISSN (Online): 2321-3086
Published by Atom and Cell Publishers © All Rights Reserved
Available online at: http://www.wjpsonline.com/
Research Article

PUTATIVE DRUG TARGET IDENTIFICATION FOR SEPTIC ARTHRITIS
THROUGH DATA MINING APPROACH
Anup Tripathi, Sachidanand Singh, Atul Kumar*
Department of Bioinformatics, School of Biotechnology and Health Sciences, Karunya
University, Karunya Nagar, Coimbatore, Tamil Nadu, India
Received: 10-10-2013 / Revised: 15-11-2013 / Accepted: 30-11-2013

ABSTRACT
Septic arthritis is the purulent invasion of a joint by an infectious agent which produces arthritis. The main
organisms having great potential to infect human beings as well as other mammals are Staphylococcus aureus,
Streptococcus pneumoniaeand Streptococcus pyrogens. In Silico comparative analysis ofall the pathways of host
Homo sapiens and pathogens was performed by using KEGG and Protein BLAST. 25, 20 and 16 unique
pathways were identified for Staphylococcus aureus, Streptococcus pyrogensand Streptococcus pneumonia
respectively. Out of these we identified 3 enzymes for Staphylococcus aureus, 4 for Streptococcus pneumoniae
and 1 for Streptococcus pyrogens, which are non-homologous to Homo sapiens proteins. The enzymes essential
for survival of the pathogens were found out by DEG database. Further CELLO analysis results showed that
50% enzymes are found to be Extracellular, 25% to be cytoplasmic and 25% to be membranous for
Staphylococcus aureus. For Streptococcus pneumoniae, 50% enzymes are found to be Extracellular, 12%
cytoplasmic, 13% membranous and 25% as cell wall proteins. 100% enzymes were found to be membranous for
Streptococcus pyrogens. Finally the enzymes from DEG were submitted in Drug Bank database to identify
approve drug targets. This Data Mining approach found that mostly the enzymes which can act as targets belong
to extracellular level in Staphylococcus aureus, Streptococcus pneumoniae and membranous in Streptococcus
pyrogens. This findings gives an understanding of these enzymes interaction with human protein protein
interaction at extracellular and membrane level.
Keywords: Septic arthritis, Staphylococcus aureus, Streptococcus pneumoniae, Streptococcus pyrogens,
essential enzymes, KEGG, DEG, PLSPred, CELLO and Drug Bank

INTRODUCTION
Septic arthritis is a serious disease in which an
inflammatory reaction in a joint is caused by an
infection. This disease requires emergency surgical
intervention because it can cause irreversible and
irreparable joint lesions and life threatening
conditions in children and adults [1]. Most septic
joints develop as a result of hematogenous seeding
of the vascular synovial membrane due to a
bacteremic episode [2, 3]. The most common
infectious agents are Staphylococcus aureus [4],
Niesseriagonorrhea [5], Streptococcus pneumonia
[3], Streptococcus pyrogens [6] and haemophilus
influenza [7]. Microorganisms may invade the joint
via direct inoculation, contiguous spread from
infected
periarticular
tissues,
or
the
bloodstream.The major consequence of bacterial

invasion is hyperaemia and oedema of the synovial
membranes; the overproduced synovial fluid
contains polymorphonuclear leukocytes, which can
damage articular cartilage by releasing proteolytic
enzymes [8]. The direct introduction of bacteria
during joint surgery has increasingly been a source
of bacterial arthritis, particularly in association with
knee and hip arthroplasties.
In silico subtractive genomics approaches, based on
the strategy that an essential survival gene nonhomologous to any human host gene is a candidate
drug target for a given pathogen, [9, 10] have been
used to identify putative drug targets in
Staphylococcus aureus, Streptococcus pneumoniae
and Streptococcus pyrogens. In the present study, a
similar data mining approach has been carried out
to screen these organism’s genome and proteome in

*Corresponding Author Address: Mr. Atul Kumar, Department of Bioinformatics, School of Biotechnology and Health Sciences, Karunya
University, Karunya Nagar, Coimbatore, Tamil Nadu, India, E-mail ID: atul.0298@gmail.com
Atul et al., World J Pharm Sci 2014; 2(1): 25-31

order to identify its essential genes and subsequent
drug and vaccine targets from various metabolic
pathways.

CELLO [14] and PSLpred [15]. Surface membrane
proteins were selected for putative candidate
vaccine targets.

MATERIALS AND METHODS

RESULTS AND DISCUSSION

Identification of potential drug targets: For
metabolic pathway identification KEGG [11]
(Kyoto Encyclopedia of Genes and Genome)
(http://www.genome.jp/kegg/) was used. The
metabolic pathway identification numbers of the
host
Homosapiens
and
the
pathogens
Staphylococcus
aureus,
Streptococcus
pneumoniaeand Streptococcus pyrogens were
extracted from the KEGG database (Table 1).
According to KEGG database annotation, the
pathways which do not appear in host
Homosapiens but are present in above pathogens
have been identified as pathways unique to
pathogens in comparison to the host. Enzymes
present in these unique pathways as well as the
enzymes involved in several metabolic pathways
like
Carbohydrate
Metabolism,
Energy
Metabolism, Lipid Metabolism, Nucleotide
Metabolism, Amino Acid Metabolism, Glycan
Biosynthesis and Metabolism and Metabolism of
Cofactors and Vitamins were also identified from
KEGG database. The protein sequences
corresponding to the unique pathways were
retrieved from the KEGG database and a BLASTp
[12] search against the non-redundant database of
these protein sequences were performed. In this
search the e-value inclusion threshold was set to
0.005 and the search was restricted to proteins from
Homo sapiens by excluding Homo sapiens in
BLASTp. This helps in finding only those targets
which do not have detectable human homologues.
Thus potential drug targets are obtained by
selecting those enzymes which do not have hits
below e-value threshold of 0.005 in BLASTp
result.

From KEGG server all the pathways associated
with S.aureus,Streptococcus pneumoniae and
Streptococcus pyrogens were analyzed and each
enzyme of these pathways were compared with the
proteins of the host Homo sapiens with the help of
BLASTp search against the non-redundant
database restricted to Homosapiens. To remove the
homologous sequences from the BLASTp search
the e-value inclusion threshold was set as 0.005.
From KEGG, 15 strains and 25 unique pathways
were identified for Staphylococcus aureus.
Similarly for Streptococcus pyrogens 12 strains and
20 unique pathways and for Streptococcus
pneumonia 14 strains and 16 unique pathways were
identified. Finally by performing BLASTp search a
total of 3 enzymes for Staphylococcus aureus, 4 for
Streptococcus pneumoniae and 1 for Streptococcus
pyrogens, which are non-homologous to Homo
sapiens protein sequences were identified (Table
2). Inhibitors can be designed against these
sequences in order to find better drugs.
Dispensable or non-essential enzymes or pathways
are not considered to be a good drug target. Thus a
DEG server analysis of all the 8 enzymes identified
from KEGG server is necessary. To enhance the
specificity of these enzymes in various pathogens
the cutoff score was set to be > 100.From DEG, a
total of 37 essential enzymes were found out and
no essential enzymes were detected for SecE (SPY
2058), Sfb1 (SPT 1057) and Sfb1 (SNC 0837)
(Table 3).
These enzymes are finally considered as potential
drug targets. The subcellular localization analysis
of all these enzymes was also performed by two
tools: CELLO (http://cello.life.nctu.edu.tw/) and
PLSpred(http://www.imtech.res.in/raghava/pslpred/
). From the CELLO analysis (Figure 1), 50%
enzymes are found to be Extracellular, 25% to be
cytoplasmic and 25% to be membranous for
Staphylococcus
aureus.
For
Streptococcus
pneumoniae 50% enzymes are found to be
Extracellular, 12% cytoplasmic, 13% membranous
and 25% as cell wall proteins. 100% enzymes were
found to be membranous for Streptococcus
pyrogens. And the PLSPred analysis (Figure 2)
showed that for Staphylococcus aureus 33%
enzymes are cytoplasmic, 33% enzymes are Periplasmic and 34% enzymes are Extracellular. For
Streptococcus pneumonia, both the Extracellular
and Peri-plasmic enzymes were found to be 50%

Finding the essential targets: Essential genes are
the genes which are indispensable for the survival
of an organism and their functions are the
foundation of life. The non-homologous enzymes
obtained from the pBLAST result were subjected to
DEG database [13] (http://tubic.tju.edu.cn/deg/) for
analyzing the essentiality of these enzymes to
pathogens.
Finding the biological significance of the targets:
The non-homologous essential genes which do not
show any similarity with any human sequence were
considered as putative drug targets. The function
and the cellular localization of each protein of
identified targets were analyzed with Swiss-Prot
protein database (http://us.expasy.org/sprot) and by
using sub-cellular localization prediction tools:
26
Atul et al., World J Pharm Sci 2014; 2(1): 25-31

each. 100% enzymes were found to be Innermembranous for Streptococcus pyrogens.
The analysis of the essential enzymes of DEG
database result against the drug bank database [16]
was done and about 7 approved drug targets were
identified. The results are shown in Table 4.

aureus infection, Bacterial secretion system and
Bacterial invasion of epithelial cells respectively.
From these pathways non-detectable human
homologous enzymes were identified as TSST-1
(SA 1819), SCIN (SA 0221), SCIN (SA 1004) for
Staphylococcus aureus; SecE (SPY 2058) for
Stretococcuspyrogens and Sfb1 (SPJ 0350), Sfb1
(SPCG 0360), Sfb1 (SPT 1057), Sfb1 (SNC 0837)
for Streptococcus pneumonia. These nonhomologous enzymes showed 37 essential genes
through DEG database, which confirmed that these
enzymes won’t perform their activity without the
presence of essential genes. The subcellular
localization of these enzymes was also found out
with the help of CELLO and PLSPred. These
enzymes were submitted in Drug Bank database
against approve drug targets, which showed their
relevance in septic arthritis. This approach gives us
an insight to find putative target enzymes which
play an important role in causing septic arthritis.

CONCLUSION
Septic arthritis is a serious problem worldwide and
and can be life threatening with lethal development
of sepsis, meningitis and other diseases, especially
caused by Staphylococcus aureus, Stretococcus
pneumonia and Streptococcus pyrogens. For the
identification and analysis of the essential genes of
these pathogens responsible for Septic arthritis,
data mining approach was used. The unique
pathways
for
Staphylococcus
aureus,
Stretococcuspyrogensand
Streptococcus
pneumoniawere identified as Staphylococcus

Table 1: DIFFERENT STRAINS AND UNIQUE PATHWAYS OF PATHOGENS IDENTIFIED FROM
KEGG

Organisms
Staphylococcus
aureus

Strains
Staphylococcus aureus N315
Staphylococcus aureus Mu50
Staphylococcus aureus Mu3
Staphylococcus aureus JH1
Staphylococcus aureus JH9
Staphylococcus aureus MW2
Staphylococcus aureus MSSA476
Staphylococcus aureus MRSA252
Staphylococcus aureus COL
Staphylococcus aureus
USA300_TCH1516
Staphylococcus aureus
USA300_FPR3757
Staphylococcus aureus NCTC8325
Staphylococcus aureus Newman
Staphylococcus aureus ED98
Staphylococcus aureus RF122

Streptococcus
pyrogens

Streptococcus pyogenes SF370
Streptococcus pyogenes MGAS5005
27

Unique Pathways
00660 C5-Branched dibasic acid metabolism
00680 Methane metabolism
00473 D-Alanine metabolism
00550 Peptidoglycan biosynthesis
00906 Carotenoid biosynthesis
00903 Limonene and pinene degradation
00281 Geraniol degradation
00312 beta-Lactam resistance
00521 Streptomycin biosynthesis
00401 Novobiocin biosynthesis
00362 Benzoate degradation
00627 Aminobenzoate degradation
00625 Chloroalkane and chloroalkene
degradation
00642 Ethylbenzene degradation
00621 Dioxin degradation
00626 Naphthalene degradation
00624 Polycyclic aromatic hydrocarbon
degradation
05150 Staphylococcus aureus infection
05100 Bacterial invasion of epithelial cells
00440 Phosphonate and phosphinate
metabolism
00363 Bisphenol degradation
00623 Toluene degradation
00361 Chlorocyclohexane and chlorobenzene
degradation
00791 Atrazine degradation
00121 Secondary bile acid biosynthesis
00680 Methane metabolism
00473 D-Alanine metabolism
Atul et al., World J Pharm Sci 2014; 2(1): 25-31

Streptococcus pyogenes MGAS8232
Streptococcus pyogenes MGAS315
Streptococcus pyogenes SSI-1
Streptococcus pyogenes MGAS10270
Streptococcus pyogenes MGAS10750
Streptococcus pyogenes MGAS2096
Streptococcus pyogenes MGAS9429
Streptococcus pyogenesManfredo
Streptococcus pyogenes MGAS10394
Streptococcus pyogenes MGAS6180
Streptococcus pyogenes NZ131

Streptococcus
pneumoniae

Streptococcus pneumoniae TIGR4
Streptococcus pneumoniae D39
Streptococcus pneumoniae R6
Streptococcus pneumoniae CGSP14
Streptococcus pneumoniae G54
Streptococcus pneumoniae ATCC
700669
Streptococcus pneumoniae
Hungary19A 6
Streptococcus pneumoniae 70585
Streptococcus pneumoniae JJA
Streptococcus pneumoniae P1031
Streptococcus pneumoniae
Taiwan19F-14
Streptococcus pneumoniae
TCH8431/19A
Streptococcus pneumoniae 670-6B
Streptococcus pneumoniae AP200

00550 Peptidoglycan biosynthesis
00903 Limonene and pinene degradation
00521 Streptomycin biosynthesis
00621 Dioxin degradation
00626 Naphthalene degradation
00624 Polycyclic aromatic hydrocarbon
degradation
00362 Benzoate degradation
00627 Aminobenzoate degradation
00625 Chloroalkane and chloroalkene
degradation
00363 Bisphenol degradation
00660 C5-Branched dibasic acid metabolism
05100 Bacterial invasion of epithelial cells
00440 Phosphonate and phosphinate
metabolism
00523 Polyketide sugar unit biosynthesis
00643 Styrene degradation
02060 Phosphotransferase system (PTS)
03070 Bacterial secretion system
02020 Two-component system
00791 Atrazine degradation
00680 Methane metabolism
00473 D-Alanine metabolism
00550 Peptidoglycan biosynthesis
00903 Limonene and pinene degradation
00621 Dioxin degradation
00626 Naphthalene degradation
00624 Polycyclic aromatic hydrocarbon
degradation
00362 Benzoate degradation
00627 Aminobenzoate degradation
00625 Chloroalkane and chloroalkene
degradation
00363 Bisphenol degradation
00660 C5-Branched dibasic acid metabolism
05100 Bacterial invasion of epithelial cells
00440 Phosphonate and phosphinate
metabolism
00643 Styrene degradation

Table 2:ENZYMES WHICH ARE NON-HOMOLOGOUS TO THE HOST
Organism
Unique Pathway
Non homologous enzymes
Staphylococcus aureus
05150
TSST-1 (SA 1819)
SCIN (SA 0221)
SCIN (SA 1004)
Streptococcus pyrogens 03070
SecE (SPY 2058)
Streptococcus
pneumoniae

05100

Sfb1 (SPJ 0350)
Sfb1 (SPCG 0360)
Sfb1 (SPT 1057)
Sfb1 (SNC 0837)

28
Atul et al., World J Pharm Sci 2014; 2(1): 25-31

Table 3: ESSENTIAL ENZYMES OF PATHOGENS IDENTIFIED FROM DEG
Non homologous enzymes
from KEGG

Gene name

Function Class

Description

toxic shock syndrome toxin-1

Essential
enzymes from
DEG
DEG10020122

Smc

DNA packaging and segregation

Chromosome segregation
SMC protein

toxic shock syndrome toxin-1

DEG10020178

Alas

Protein synthesis

Alanyl-tRNAsynthetase

toxic shock syndrome toxin-1

DEG10020173

dnaK

Molecular chaperone

toxic shock syndrome toxin-1
toxic shock syndrome toxin-1

DEG10020268
DEG10020103

rplN
mutS2

toxic shock syndrome toxin-1

DEG10020024

guaA

toxic shock syndrome toxin-1

DEG10170336

toxic shock syndrome toxin-1

DEG10170214

SAOUHSC_0
2571
dnaK

Post-translational modification,
protein turnover, chaperones
Protein synthesis
DNA replication/modification and
repair
Nucleotide transport and
metabolism
Function unknown

toxic shock syndrome toxin-1

DEG10170324

rplN

Ribosomal proteins

Secretary antigen
precursor
Molecular chaperone
DnaK
50S ribosomal protein L14

toxic shock syndrome toxin-1

DEG10170020

guaA

Purine biosynthesis

GMP synthase

toxic shock syndrome toxin-1

DEG10170223

alaS

tRNAsynthetase

Alanyl-tRNAsynthetase

staphylococcal complement
inhibitor SA0221
staphylococcal complement
inhibitor SA0221
staphylococcal complement
inhibitor SA0221
staphylococcal complement
inhibitor SA0221
staphylococcal complement
inhibitor SA0221
staphylococcal complement
inhibitor SA0221
staphylococcal complement
inhibitor SA0221

DEG10020145

SA1147

Function unknown

Hypothetical protein

DEG10020122

Smc

DNA packaging and segregation

DEG10020195

polA

DEG10020297

SA2442

DNA replication, recombination,
and repair
Protein secretion

Chromosome segregation
SMC protein
DNA polymerase I

DEG10020039

lysS

Protein synthesis

PreproteintranslocaseSec
A homolog
Lysyl-tRNAsynthetase

DEG10020025

SA0422

Function unknown

Hypothetical protein

DEG10170190

SAOUHSC_0
1473

Lipids

BirAbifunctional
protein

staphylococcal complement
inhibitor SA0221

DEG10170159

SAOUHSC_0
1237

Peptidoglycan biosynthesis

Undecaprenyl
pyrophosphate synthase

staphylococcal complement
inhibitor SA0221
staphylococcal complement
inhibitor SA0221

DEG10170035

lysS

tRNAsynthetase

Lysyl-tRNAsynthetase

DEG10170278

SAOUHSC_0
2123

DNA replication

ATP-dependent DNA
helicase PcrA

staphylococcal complement
inhibitor SA1004
staphylococcal complement
inhibitor SA1004
staphylococcal complement
inhibitor SA1004

DEG10020038

ftsH

Cell division

Cell-division protein

DEG10020195

polA

DNA polymerase I

DEG10020207

ccpA

staphylococcal complement
inhibitor SA1004
staphylococcal complement
inhibitor SA1004
staphylococcal complement
inhibitor SA1004
staphylococcal complement
inhibitor SA1004
staphylococcal complement
inhibitor SA1004
staphylococcal complement
inhibitor SA1004
staphylococcal complement
inhibitor SA1004

DEG10020066

uvrB

DEG10020174

lepA

DEG10020151

glcT

DNA replication, recombination,
and repair (L)
Inorganic ion transport and
metabolism (P)/Signal
transduction (T)
DNA replication, recombination,
and repair (L)
General function prediction only
(R)
transcription antiterminator

DEG10020030

SA0447

Similar to unknown proteins

DEG10020197

pfk/pfkA

DEG10020132

tsf

Metabolism of carbohydrates and
related molecules
Protein synthesis

DEG10170129

SAOUHSC_0
1148

29

Protein folding

Cell division

50S ribosomal protein L14
MutS-like protein
GMP synthase

Catabolite control protein
A
Exonuclease ABC subunit
B
GTP-binding protein
Transcription
antiterminator
Conserved hypothetical
protein
6-phosphofructokinase
Homolog elongation
factor TS
Cell division
protein
Atul et al., World J Pharm Sci 2014; 2(1): 25-31
DEG10170157
pyrH
Pyrimidine biosynthesis

Uridylate kinase

DEG10170229

SAOUHSC_0
1739

Peptidoglycan biosynthesis

Hypothetical

staphylococcal complement
inhibitor SA1004

DEG10170253

SAOUHSC_0
1807

Glycolysis

6-phosphofructokinase

fibronectin-binding protein 1
SPJ 0350

DEG10070096

SP_1737

-

fibronectin-binding protein 1
SPCG 0360

DEG10070096

SP_1737

-

fibronectin-binding protein 1
SPCG 0360

DEG10070127

sulC/folE

Coenzyme metabolism

staphylococcal complement
inhibitor SA1004
staphylococcal complement
inhibitor SA1004

protein

DNA-directed RNA
polymerase, omega
subunit, putative
DNA-directed RNA
polymerase, omega
subunit, putative
GTP cyclohydrolase

Table 4: APPROVED DRUG TARGET IDENTIFICATION BY THE DRUG BANK SERVER
Target Enzyme
Target Drug
Drug Bank ID Target
toxic shock syndrome toxin-1
Phenobarbital
DB01174
GABA receptor subunit
alpha 1
staphylococcal complement inhibitor
Lincomycin
DB01627
50 S ribosomal protein L 10
preproteintranslocase subunit SecE
Lincomycin
DB01627
50 S ribosomal protein L 10
(Secretory System)
preproteintranslocase subunit SecE
Vancomycin
DB00512
DNA
(Secretory System)
preproteintranslocase subunit SecE
Clarithromycin
DB01211
GlycosyltransferaseGtfA
(Secretory System)
fibronectin-binding protein 1
Cefditoren
DB01066
Penecillin binding proteins
fibronectin-binding protein 1
Lincomycin
DB01627
50 S ribosomal protein L 10

(A)

(B)

(C)
Figure 1: Pie diagram showing subcellular localization of enzymes of Staphylococcus aureus(A),
Streptococcus pneumoniae(B), Streptococcus pyrogens(C)by CELLO

30
Atul et al., World J Pharm Sci 2014; 2(1): 25-31

(A)

(B)

(C)
Figure 2: Pie diagram showing subcellular localization of enzymes of Staphylococcus aureus(A),
Streptococcus pneumoniae(B), Streptococcus pyrogens(C)by PLSPred
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31

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PUTATIVE DRUG TARGET IDENTIFICATION FOR SEPTIC ARTHRITIS THROUGH DATA MINING APPROACH

  • 1. World Journal of Pharmaceutical Sciences ISSN (Print): 2321-3310; ISSN (Online): 2321-3086 Published by Atom and Cell Publishers © All Rights Reserved Available online at: http://www.wjpsonline.com/ Research Article PUTATIVE DRUG TARGET IDENTIFICATION FOR SEPTIC ARTHRITIS THROUGH DATA MINING APPROACH Anup Tripathi, Sachidanand Singh, Atul Kumar* Department of Bioinformatics, School of Biotechnology and Health Sciences, Karunya University, Karunya Nagar, Coimbatore, Tamil Nadu, India Received: 10-10-2013 / Revised: 15-11-2013 / Accepted: 30-11-2013 ABSTRACT Septic arthritis is the purulent invasion of a joint by an infectious agent which produces arthritis. The main organisms having great potential to infect human beings as well as other mammals are Staphylococcus aureus, Streptococcus pneumoniaeand Streptococcus pyrogens. In Silico comparative analysis ofall the pathways of host Homo sapiens and pathogens was performed by using KEGG and Protein BLAST. 25, 20 and 16 unique pathways were identified for Staphylococcus aureus, Streptococcus pyrogensand Streptococcus pneumonia respectively. Out of these we identified 3 enzymes for Staphylococcus aureus, 4 for Streptococcus pneumoniae and 1 for Streptococcus pyrogens, which are non-homologous to Homo sapiens proteins. The enzymes essential for survival of the pathogens were found out by DEG database. Further CELLO analysis results showed that 50% enzymes are found to be Extracellular, 25% to be cytoplasmic and 25% to be membranous for Staphylococcus aureus. For Streptococcus pneumoniae, 50% enzymes are found to be Extracellular, 12% cytoplasmic, 13% membranous and 25% as cell wall proteins. 100% enzymes were found to be membranous for Streptococcus pyrogens. Finally the enzymes from DEG were submitted in Drug Bank database to identify approve drug targets. This Data Mining approach found that mostly the enzymes which can act as targets belong to extracellular level in Staphylococcus aureus, Streptococcus pneumoniae and membranous in Streptococcus pyrogens. This findings gives an understanding of these enzymes interaction with human protein protein interaction at extracellular and membrane level. Keywords: Septic arthritis, Staphylococcus aureus, Streptococcus pneumoniae, Streptococcus pyrogens, essential enzymes, KEGG, DEG, PLSPred, CELLO and Drug Bank INTRODUCTION Septic arthritis is a serious disease in which an inflammatory reaction in a joint is caused by an infection. This disease requires emergency surgical intervention because it can cause irreversible and irreparable joint lesions and life threatening conditions in children and adults [1]. Most septic joints develop as a result of hematogenous seeding of the vascular synovial membrane due to a bacteremic episode [2, 3]. The most common infectious agents are Staphylococcus aureus [4], Niesseriagonorrhea [5], Streptococcus pneumonia [3], Streptococcus pyrogens [6] and haemophilus influenza [7]. Microorganisms may invade the joint via direct inoculation, contiguous spread from infected periarticular tissues, or the bloodstream.The major consequence of bacterial invasion is hyperaemia and oedema of the synovial membranes; the overproduced synovial fluid contains polymorphonuclear leukocytes, which can damage articular cartilage by releasing proteolytic enzymes [8]. The direct introduction of bacteria during joint surgery has increasingly been a source of bacterial arthritis, particularly in association with knee and hip arthroplasties. In silico subtractive genomics approaches, based on the strategy that an essential survival gene nonhomologous to any human host gene is a candidate drug target for a given pathogen, [9, 10] have been used to identify putative drug targets in Staphylococcus aureus, Streptococcus pneumoniae and Streptococcus pyrogens. In the present study, a similar data mining approach has been carried out to screen these organism’s genome and proteome in *Corresponding Author Address: Mr. Atul Kumar, Department of Bioinformatics, School of Biotechnology and Health Sciences, Karunya University, Karunya Nagar, Coimbatore, Tamil Nadu, India, E-mail ID: atul.0298@gmail.com
  • 2. Atul et al., World J Pharm Sci 2014; 2(1): 25-31 order to identify its essential genes and subsequent drug and vaccine targets from various metabolic pathways. CELLO [14] and PSLpred [15]. Surface membrane proteins were selected for putative candidate vaccine targets. MATERIALS AND METHODS RESULTS AND DISCUSSION Identification of potential drug targets: For metabolic pathway identification KEGG [11] (Kyoto Encyclopedia of Genes and Genome) (http://www.genome.jp/kegg/) was used. The metabolic pathway identification numbers of the host Homosapiens and the pathogens Staphylococcus aureus, Streptococcus pneumoniaeand Streptococcus pyrogens were extracted from the KEGG database (Table 1). According to KEGG database annotation, the pathways which do not appear in host Homosapiens but are present in above pathogens have been identified as pathways unique to pathogens in comparison to the host. Enzymes present in these unique pathways as well as the enzymes involved in several metabolic pathways like Carbohydrate Metabolism, Energy Metabolism, Lipid Metabolism, Nucleotide Metabolism, Amino Acid Metabolism, Glycan Biosynthesis and Metabolism and Metabolism of Cofactors and Vitamins were also identified from KEGG database. The protein sequences corresponding to the unique pathways were retrieved from the KEGG database and a BLASTp [12] search against the non-redundant database of these protein sequences were performed. In this search the e-value inclusion threshold was set to 0.005 and the search was restricted to proteins from Homo sapiens by excluding Homo sapiens in BLASTp. This helps in finding only those targets which do not have detectable human homologues. Thus potential drug targets are obtained by selecting those enzymes which do not have hits below e-value threshold of 0.005 in BLASTp result. From KEGG server all the pathways associated with S.aureus,Streptococcus pneumoniae and Streptococcus pyrogens were analyzed and each enzyme of these pathways were compared with the proteins of the host Homo sapiens with the help of BLASTp search against the non-redundant database restricted to Homosapiens. To remove the homologous sequences from the BLASTp search the e-value inclusion threshold was set as 0.005. From KEGG, 15 strains and 25 unique pathways were identified for Staphylococcus aureus. Similarly for Streptococcus pyrogens 12 strains and 20 unique pathways and for Streptococcus pneumonia 14 strains and 16 unique pathways were identified. Finally by performing BLASTp search a total of 3 enzymes for Staphylococcus aureus, 4 for Streptococcus pneumoniae and 1 for Streptococcus pyrogens, which are non-homologous to Homo sapiens protein sequences were identified (Table 2). Inhibitors can be designed against these sequences in order to find better drugs. Dispensable or non-essential enzymes or pathways are not considered to be a good drug target. Thus a DEG server analysis of all the 8 enzymes identified from KEGG server is necessary. To enhance the specificity of these enzymes in various pathogens the cutoff score was set to be > 100.From DEG, a total of 37 essential enzymes were found out and no essential enzymes were detected for SecE (SPY 2058), Sfb1 (SPT 1057) and Sfb1 (SNC 0837) (Table 3). These enzymes are finally considered as potential drug targets. The subcellular localization analysis of all these enzymes was also performed by two tools: CELLO (http://cello.life.nctu.edu.tw/) and PLSpred(http://www.imtech.res.in/raghava/pslpred/ ). From the CELLO analysis (Figure 1), 50% enzymes are found to be Extracellular, 25% to be cytoplasmic and 25% to be membranous for Staphylococcus aureus. For Streptococcus pneumoniae 50% enzymes are found to be Extracellular, 12% cytoplasmic, 13% membranous and 25% as cell wall proteins. 100% enzymes were found to be membranous for Streptococcus pyrogens. And the PLSPred analysis (Figure 2) showed that for Staphylococcus aureus 33% enzymes are cytoplasmic, 33% enzymes are Periplasmic and 34% enzymes are Extracellular. For Streptococcus pneumonia, both the Extracellular and Peri-plasmic enzymes were found to be 50% Finding the essential targets: Essential genes are the genes which are indispensable for the survival of an organism and their functions are the foundation of life. The non-homologous enzymes obtained from the pBLAST result were subjected to DEG database [13] (http://tubic.tju.edu.cn/deg/) for analyzing the essentiality of these enzymes to pathogens. Finding the biological significance of the targets: The non-homologous essential genes which do not show any similarity with any human sequence were considered as putative drug targets. The function and the cellular localization of each protein of identified targets were analyzed with Swiss-Prot protein database (http://us.expasy.org/sprot) and by using sub-cellular localization prediction tools: 26
  • 3. Atul et al., World J Pharm Sci 2014; 2(1): 25-31 each. 100% enzymes were found to be Innermembranous for Streptococcus pyrogens. The analysis of the essential enzymes of DEG database result against the drug bank database [16] was done and about 7 approved drug targets were identified. The results are shown in Table 4. aureus infection, Bacterial secretion system and Bacterial invasion of epithelial cells respectively. From these pathways non-detectable human homologous enzymes were identified as TSST-1 (SA 1819), SCIN (SA 0221), SCIN (SA 1004) for Staphylococcus aureus; SecE (SPY 2058) for Stretococcuspyrogens and Sfb1 (SPJ 0350), Sfb1 (SPCG 0360), Sfb1 (SPT 1057), Sfb1 (SNC 0837) for Streptococcus pneumonia. These nonhomologous enzymes showed 37 essential genes through DEG database, which confirmed that these enzymes won’t perform their activity without the presence of essential genes. The subcellular localization of these enzymes was also found out with the help of CELLO and PLSPred. These enzymes were submitted in Drug Bank database against approve drug targets, which showed their relevance in septic arthritis. This approach gives us an insight to find putative target enzymes which play an important role in causing septic arthritis. CONCLUSION Septic arthritis is a serious problem worldwide and and can be life threatening with lethal development of sepsis, meningitis and other diseases, especially caused by Staphylococcus aureus, Stretococcus pneumonia and Streptococcus pyrogens. For the identification and analysis of the essential genes of these pathogens responsible for Septic arthritis, data mining approach was used. The unique pathways for Staphylococcus aureus, Stretococcuspyrogensand Streptococcus pneumoniawere identified as Staphylococcus Table 1: DIFFERENT STRAINS AND UNIQUE PATHWAYS OF PATHOGENS IDENTIFIED FROM KEGG Organisms Staphylococcus aureus Strains Staphylococcus aureus N315 Staphylococcus aureus Mu50 Staphylococcus aureus Mu3 Staphylococcus aureus JH1 Staphylococcus aureus JH9 Staphylococcus aureus MW2 Staphylococcus aureus MSSA476 Staphylococcus aureus MRSA252 Staphylococcus aureus COL Staphylococcus aureus USA300_TCH1516 Staphylococcus aureus USA300_FPR3757 Staphylococcus aureus NCTC8325 Staphylococcus aureus Newman Staphylococcus aureus ED98 Staphylococcus aureus RF122 Streptococcus pyrogens Streptococcus pyogenes SF370 Streptococcus pyogenes MGAS5005 27 Unique Pathways 00660 C5-Branched dibasic acid metabolism 00680 Methane metabolism 00473 D-Alanine metabolism 00550 Peptidoglycan biosynthesis 00906 Carotenoid biosynthesis 00903 Limonene and pinene degradation 00281 Geraniol degradation 00312 beta-Lactam resistance 00521 Streptomycin biosynthesis 00401 Novobiocin biosynthesis 00362 Benzoate degradation 00627 Aminobenzoate degradation 00625 Chloroalkane and chloroalkene degradation 00642 Ethylbenzene degradation 00621 Dioxin degradation 00626 Naphthalene degradation 00624 Polycyclic aromatic hydrocarbon degradation 05150 Staphylococcus aureus infection 05100 Bacterial invasion of epithelial cells 00440 Phosphonate and phosphinate metabolism 00363 Bisphenol degradation 00623 Toluene degradation 00361 Chlorocyclohexane and chlorobenzene degradation 00791 Atrazine degradation 00121 Secondary bile acid biosynthesis 00680 Methane metabolism 00473 D-Alanine metabolism
  • 4. Atul et al., World J Pharm Sci 2014; 2(1): 25-31 Streptococcus pyogenes MGAS8232 Streptococcus pyogenes MGAS315 Streptococcus pyogenes SSI-1 Streptococcus pyogenes MGAS10270 Streptococcus pyogenes MGAS10750 Streptococcus pyogenes MGAS2096 Streptococcus pyogenes MGAS9429 Streptococcus pyogenesManfredo Streptococcus pyogenes MGAS10394 Streptococcus pyogenes MGAS6180 Streptococcus pyogenes NZ131 Streptococcus pneumoniae Streptococcus pneumoniae TIGR4 Streptococcus pneumoniae D39 Streptococcus pneumoniae R6 Streptococcus pneumoniae CGSP14 Streptococcus pneumoniae G54 Streptococcus pneumoniae ATCC 700669 Streptococcus pneumoniae Hungary19A 6 Streptococcus pneumoniae 70585 Streptococcus pneumoniae JJA Streptococcus pneumoniae P1031 Streptococcus pneumoniae Taiwan19F-14 Streptococcus pneumoniae TCH8431/19A Streptococcus pneumoniae 670-6B Streptococcus pneumoniae AP200 00550 Peptidoglycan biosynthesis 00903 Limonene and pinene degradation 00521 Streptomycin biosynthesis 00621 Dioxin degradation 00626 Naphthalene degradation 00624 Polycyclic aromatic hydrocarbon degradation 00362 Benzoate degradation 00627 Aminobenzoate degradation 00625 Chloroalkane and chloroalkene degradation 00363 Bisphenol degradation 00660 C5-Branched dibasic acid metabolism 05100 Bacterial invasion of epithelial cells 00440 Phosphonate and phosphinate metabolism 00523 Polyketide sugar unit biosynthesis 00643 Styrene degradation 02060 Phosphotransferase system (PTS) 03070 Bacterial secretion system 02020 Two-component system 00791 Atrazine degradation 00680 Methane metabolism 00473 D-Alanine metabolism 00550 Peptidoglycan biosynthesis 00903 Limonene and pinene degradation 00621 Dioxin degradation 00626 Naphthalene degradation 00624 Polycyclic aromatic hydrocarbon degradation 00362 Benzoate degradation 00627 Aminobenzoate degradation 00625 Chloroalkane and chloroalkene degradation 00363 Bisphenol degradation 00660 C5-Branched dibasic acid metabolism 05100 Bacterial invasion of epithelial cells 00440 Phosphonate and phosphinate metabolism 00643 Styrene degradation Table 2:ENZYMES WHICH ARE NON-HOMOLOGOUS TO THE HOST Organism Unique Pathway Non homologous enzymes Staphylococcus aureus 05150 TSST-1 (SA 1819) SCIN (SA 0221) SCIN (SA 1004) Streptococcus pyrogens 03070 SecE (SPY 2058) Streptococcus pneumoniae 05100 Sfb1 (SPJ 0350) Sfb1 (SPCG 0360) Sfb1 (SPT 1057) Sfb1 (SNC 0837) 28
  • 5. Atul et al., World J Pharm Sci 2014; 2(1): 25-31 Table 3: ESSENTIAL ENZYMES OF PATHOGENS IDENTIFIED FROM DEG Non homologous enzymes from KEGG Gene name Function Class Description toxic shock syndrome toxin-1 Essential enzymes from DEG DEG10020122 Smc DNA packaging and segregation Chromosome segregation SMC protein toxic shock syndrome toxin-1 DEG10020178 Alas Protein synthesis Alanyl-tRNAsynthetase toxic shock syndrome toxin-1 DEG10020173 dnaK Molecular chaperone toxic shock syndrome toxin-1 toxic shock syndrome toxin-1 DEG10020268 DEG10020103 rplN mutS2 toxic shock syndrome toxin-1 DEG10020024 guaA toxic shock syndrome toxin-1 DEG10170336 toxic shock syndrome toxin-1 DEG10170214 SAOUHSC_0 2571 dnaK Post-translational modification, protein turnover, chaperones Protein synthesis DNA replication/modification and repair Nucleotide transport and metabolism Function unknown toxic shock syndrome toxin-1 DEG10170324 rplN Ribosomal proteins Secretary antigen precursor Molecular chaperone DnaK 50S ribosomal protein L14 toxic shock syndrome toxin-1 DEG10170020 guaA Purine biosynthesis GMP synthase toxic shock syndrome toxin-1 DEG10170223 alaS tRNAsynthetase Alanyl-tRNAsynthetase staphylococcal complement inhibitor SA0221 staphylococcal complement inhibitor SA0221 staphylococcal complement inhibitor SA0221 staphylococcal complement inhibitor SA0221 staphylococcal complement inhibitor SA0221 staphylococcal complement inhibitor SA0221 staphylococcal complement inhibitor SA0221 DEG10020145 SA1147 Function unknown Hypothetical protein DEG10020122 Smc DNA packaging and segregation DEG10020195 polA DEG10020297 SA2442 DNA replication, recombination, and repair Protein secretion Chromosome segregation SMC protein DNA polymerase I DEG10020039 lysS Protein synthesis PreproteintranslocaseSec A homolog Lysyl-tRNAsynthetase DEG10020025 SA0422 Function unknown Hypothetical protein DEG10170190 SAOUHSC_0 1473 Lipids BirAbifunctional protein staphylococcal complement inhibitor SA0221 DEG10170159 SAOUHSC_0 1237 Peptidoglycan biosynthesis Undecaprenyl pyrophosphate synthase staphylococcal complement inhibitor SA0221 staphylococcal complement inhibitor SA0221 DEG10170035 lysS tRNAsynthetase Lysyl-tRNAsynthetase DEG10170278 SAOUHSC_0 2123 DNA replication ATP-dependent DNA helicase PcrA staphylococcal complement inhibitor SA1004 staphylococcal complement inhibitor SA1004 staphylococcal complement inhibitor SA1004 DEG10020038 ftsH Cell division Cell-division protein DEG10020195 polA DNA polymerase I DEG10020207 ccpA staphylococcal complement inhibitor SA1004 staphylococcal complement inhibitor SA1004 staphylococcal complement inhibitor SA1004 staphylococcal complement inhibitor SA1004 staphylococcal complement inhibitor SA1004 staphylococcal complement inhibitor SA1004 staphylococcal complement inhibitor SA1004 DEG10020066 uvrB DEG10020174 lepA DEG10020151 glcT DNA replication, recombination, and repair (L) Inorganic ion transport and metabolism (P)/Signal transduction (T) DNA replication, recombination, and repair (L) General function prediction only (R) transcription antiterminator DEG10020030 SA0447 Similar to unknown proteins DEG10020197 pfk/pfkA DEG10020132 tsf Metabolism of carbohydrates and related molecules Protein synthesis DEG10170129 SAOUHSC_0 1148 29 Protein folding Cell division 50S ribosomal protein L14 MutS-like protein GMP synthase Catabolite control protein A Exonuclease ABC subunit B GTP-binding protein Transcription antiterminator Conserved hypothetical protein 6-phosphofructokinase Homolog elongation factor TS Cell division protein
  • 6. Atul et al., World J Pharm Sci 2014; 2(1): 25-31 DEG10170157 pyrH Pyrimidine biosynthesis Uridylate kinase DEG10170229 SAOUHSC_0 1739 Peptidoglycan biosynthesis Hypothetical staphylococcal complement inhibitor SA1004 DEG10170253 SAOUHSC_0 1807 Glycolysis 6-phosphofructokinase fibronectin-binding protein 1 SPJ 0350 DEG10070096 SP_1737 - fibronectin-binding protein 1 SPCG 0360 DEG10070096 SP_1737 - fibronectin-binding protein 1 SPCG 0360 DEG10070127 sulC/folE Coenzyme metabolism staphylococcal complement inhibitor SA1004 staphylococcal complement inhibitor SA1004 protein DNA-directed RNA polymerase, omega subunit, putative DNA-directed RNA polymerase, omega subunit, putative GTP cyclohydrolase Table 4: APPROVED DRUG TARGET IDENTIFICATION BY THE DRUG BANK SERVER Target Enzyme Target Drug Drug Bank ID Target toxic shock syndrome toxin-1 Phenobarbital DB01174 GABA receptor subunit alpha 1 staphylococcal complement inhibitor Lincomycin DB01627 50 S ribosomal protein L 10 preproteintranslocase subunit SecE Lincomycin DB01627 50 S ribosomal protein L 10 (Secretory System) preproteintranslocase subunit SecE Vancomycin DB00512 DNA (Secretory System) preproteintranslocase subunit SecE Clarithromycin DB01211 GlycosyltransferaseGtfA (Secretory System) fibronectin-binding protein 1 Cefditoren DB01066 Penecillin binding proteins fibronectin-binding protein 1 Lincomycin DB01627 50 S ribosomal protein L 10 (A) (B) (C) Figure 1: Pie diagram showing subcellular localization of enzymes of Staphylococcus aureus(A), Streptococcus pneumoniae(B), Streptococcus pyrogens(C)by CELLO 30
  • 7. Atul et al., World J Pharm Sci 2014; 2(1): 25-31 (A) (B) (C) Figure 2: Pie diagram showing subcellular localization of enzymes of Staphylococcus aureus(A), Streptococcus pneumoniae(B), Streptococcus pyrogens(C)by PLSPred REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. Cicak N. Ultrasound system to move. Medicins Ka Naklada 2003; 67-75. Klein RS. Joint infection, with consideration of underlying disease and sources of bacteremia in hematogenous infection. Clinics in GeriatricMedicine 1988; 4(2): 375–394. Morgan DS, Fisher D, Merianos A, Currie BJ. An 18 year clinical review of septic arthritis from tropical Australia. Epidemiology and Infection 1996; 117(3): 423–428. Barton LL, Dunkle LM, Habib FH. Septic arthritis in childhood: A 13-year review.American Journal of Diseases of Children1987; 141(8): 898–900 . O’Brien JP, Goldenberg DL, Rice PA. Disseminated gonococcal infection: a prospective analysis of 49 patients and a review of pathophysiology and immune mechanisms. Medicine (Baltimore) 1983; 62 (6): 395–406. Ryan MJ, Kavanagh R, Wall PG, Hazleman BL. Bacterial joint infections in England and Wales: analysis of bacterial isolates over a four year period.British Journal of Rheumatology 1997; 36 (3): 370–373. Shirtliff ME, Mader JT. Acute Septic Arthritis. Clinical Microbiology Reviews 2002; 15 (4): 527–544. Bialik V, Volpin G, Jerushalmi J, Stein H. Sonography in the diagnosis of painful hips. International Orthopaedics 1991; 15 (2):155-159. Allsop AE. New antibiotic discovery, novel screens, novel targets and impact of microbial genomics. Current Opinion Microbiology 1998; 1 (5): 530-534. Schmid MB, Novel approaches to the discovery of antimicrobial agents. Current Opinionin Chemical Biology 1998; 2 (4): 529534. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M.KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research 1999; 27 (1): 29-34. Altschul SF, Gish W, Miller W, E.W. Myers EW, Lipman DJ. Basic local alignment search tool. Journal of Molecular Biology 1990; 215 (3):403-410. Zhang R, Ou HY, Zhang CT.DEG: A database of essential genes. Nucleic Acids Research 2004; 32 (1): D271-D272. Yu CS, Chen YC, Lu CH, Hwang JK.Prediction of protein subcellular localization. Proteins: Structure and Function 2006; 64 (3):643-651. Bhasin M, Garg A, Raghava GPS. PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 2005; 21 (10): 2522-2524. Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S,Tzur D, Gautam B, Hassanali M, DrugBank: a knowledgebase for drugs, drug actions and drug targets.Nucleic Acids Research 1991;3:901-906. 31