Computers in Biology and Medicine 48 (2014) 17–27

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Computational gene network study on antibiotic resistance genes of Acinetobacter baumannii P. Anitha, Anand Anbarasu, Sudha Ramaiah n Bioinformatics Division, School of Biosciences & Technology (SBST), VIT University, Vellore 632014, India

art ic l e i nf o

a b s t r a c t

Article history: Received 10 January 2014 Accepted 14 February 2014

Multi Drug Resistance (MDR) in Acinetobacter baumannii is one of the major threats for emerging nosocomial infections in hospital environment. Multidrug-resistance in A. baumannii may be due to the implementation of multi-combination resistance mechanisms such as β-lactamase synthesis, PenicillinBinding Proteins (PBPs) changes, alteration in porin proteins and in efflux pumps against various existing classes of antibiotics. Multiple antibiotic resistance genes are involved in MDR. These resistance genes are transferred through plasmids, which are responsible for the dissemination of antibiotic resistance among Acinetobacter spp. In addition, these resistance genes may also have a tendency to interact with each other or with their gene products. Therefore, it becomes necessary to understand the impact of these interactions in antibiotic resistance mechanism. Hence, our study focuses on protein and gene network analysis on various resistance genes, to elucidate the role of the interacting proteins and to study their functional contribution towards antibiotic resistance. From the search tool for the retrieval of interacting gene/protein (STRING), a total of 168 functional partners for 15 resistance genes were extracted based on the confidence scoring system. The network study was then followed up with functional clustering of associated partners using molecular complex detection (MCODE). Later, we selected eight efficient clusters based on score. Interestingly, the associated protein we identified from the network possessed greater functional similarity with known resistance genes. This network-based approach on resistance genes of A. baumannii could help in identifying new genes/proteins and provide clues on their association in antibiotic resistance. & 2014 Elsevier Ltd. All rights reserved.

Keywords: A. baumannii Resistance genes Protein network Clustering analysis Gene expression data

1. Introduction Acinetobacter baumannii is an opportunistic pathogen, which is classified as non-fermentative Gram negative bacilli. This organism causes serious nosocomial and community-acquired infections such as bacteremia, pneumonia, meningitis, urinary tract infection, in humans [1] particularly with patients in intensive care units [2]. Several classes of antibiotics such as β-lactams, chloramphenicol, macrolides, tetracyclines, aminoglycosides and fluoroquinolones are used in the treatment of these emerging infections. During the last decade, studies on A. baumannii confirmed the resistance of these pathogens to the antibiotics mentioned above. It is observed that the resistance has been gradually increasing since then. This poses difficulty in selecting the effective therapeutic regimen. A. baumannii strains possess intrinsic or acquired resistance [3] mechanisms via enzymatic degradation of the drugs, modification

n Corresponding author. Tel.: þ 91 416 2556, þ91 416 2547; fax: þ91 416 2243 092. E-mail address: [email protected] (S. Ramaiah).

http://dx.doi.org/10.1016/j.compbiomed.2014.02.009 0010-4825 & 2014 Elsevier Ltd. All rights reserved.

or protection of the target and decreased permeability to or active efflux pumps [1]. Occasionally, the combinations among these multiple resistance cases are also responsible for the antibiotic inactivation [4]. The most prevalent mechanism of resistance in A. baumannii is via β-lactamase mediated hydrolysis. In addition to this, efflux-mediated resistance is also an established factor that affects antibiotic permeability in A. baumannii. Overexpression of these efflux systems reduces the accumulation of the antibiotics [5]. Among efflux pump families, the AdeABC (Acinetobacter drug efflux) pump belongs to the ResistanceNodulation-cell Division (RND) family, which is the most commonly found efflux system in Acinetobacter spp. [6,7]. There are reports on AdeABC efflux pump-mediated antibiotic resistance. This pump shows resistance to aminoglycosides, β-lactams, chloramphenicol, erythromycin, tetracyclines in A. baumannii. It is probable that β-lactamases and outer-membrane alterations combine together to confer resistance to β-lactam agents [8]. A. baumannii acquired resistance genes may be encoded in plasmid, transposons, or integrons. The presence of these mobile genetic elements increases the dissemination of resistance among species [6,9–12]. Thus, it has become an emerging threat

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P. Anitha et al. / Computers in Biology and Medicine 48 (2014) 17–27

worldwide [13]. Among mobile genetic elements, integrons play an important role in dissemination of MDR. Integrons are genetic elements that contain the components of a site-specific recombination system that recognizes and captures mobile gene cassettes [14,15]. The basic structure of an integron possesses a gene for an integrase (intI), a recombination site (attI) and a promoter (PC) that permits the expression of the gene cassettes incorporated in the variable region [15]. This variable region codes for one or more antibiotic resistance genes such as aminoglycoside resistant genes, β-lactamase genes like blaVEB-1, blaOXA [16], blaIMP-1, blaVIM and blaSIM [17], sulfonamide resistance gene (dfr1), chloramphenicol resistance genes (cmlA and catB) and rifampin resistance gene (arr-2) [16,11]. So far, three integron elements have been characterized; Classes 1 and 2 are usually associated with antimicrobial resistance gene cassettes in A. baumannii [14,15]. In the case of Carbapenems resistance, blaNDM, blaKPC and blaGES genes are considered to be an emerging concern in A. baumannii. The blaNDM is present in ISAba125, which appears to be the main genetic component for dissemination [18]. The presence of blaKPC gene in Acinetobacter calcoaceticus–baumannii complex suggests the possibility of horizontal transmission, as this carbapenemase has been associated with mobile genetic elements [19]. A recent study performed in Belgium also suggests that a series of blaGESproducing A. baumannii are isolated from different geographical origins. This blaGES carbapenemase gene is carried by plasmid [20]. Colistin (also known as polymyxin E) is the last resort antibiotic used in treating the MDR A. baumannii. Unfortunately, resistance to colistin in A. baumannii has been reported worldwide. However, recent studies suggest that colistin resistance occurs due to the mutation in lpxA, lpxC and lpxD genes, which results in the loss of lipopolysaccharide (LPS) where colistin cannot bind with LPS [21]. On the whole, plasmid-mediated resistance is of great concern because the acquisition of resistance genes can completely change the situation of drug resistance among Acinetobacter spp. [22]. Hence, the studies on these resistance genes, and their expression toward the antibiotic resistance have received close attention among clinicians. In combination, these multiple resistance genes exert resistance to antibiotics. This hint signifies that interaction can take place between the genes or with their gene products. Hence, it would be meaningful to screen the interacting proteins which actively participate in resistance pathways with similar or identical functions. Network-based approach is a useful method in representing complex biological systems [23]. Henceforth, generating a gene or protein network would be effective in exploring the functional association among the associated proteins. Hence, in this study, we have incorporated multiple computational methods to construct a gene/protein network of various antibiotics resistant gene of A. baumannii. We collected 41 resistance genes of A. baumannii from Antibiotic Resistance Gene Database (ARDB) [24], and carried out gene/ protein network analysis for 41 resistance genes using STRING. Out of these 41, we obtained confident networks for 15 resistance genes. Hence, we selected these 15 resistance genes for further analysis. Our research emphasizes on the functional study of 15 resistance genes and their 168 associated functional partners which are extracted based on the confidence score (highest, high and medium). Then, we employed MCODE to obtain more precise interconnecting cluster among 168 interacted partners. Interestingly, we found 8 clusters with a total of 52 functional partners. For each cluster, the significant (p-value r 0.05) Gene Ontology (GO) annotations were extracted from the Database for Annotation, Visualization and Integrated Discovery (DAVID) and STRING. Based on our analysis of functional partners, we predicted that a majority of them would be related to antibiotic resistance, and some of them were believed to be potential antibacterial targets.

2. Materials and methods 2.1. STRING network analysis STRING v9.0 a pre-computed database was used to predict the gene/protein–protein associations, which included direct (physical) and indirect (functional) associations. STRING database could be accessed by giving the protein identifier or raw amino acid sequence of the protein as an input. The tool listed the predicted functional associations (functional partners) for the protein, which was ranked by estimated confidence score. These associated predictions were done using prediction algorithms based on the genomic information or transferring associations/interactions between organisms. These associations were integrated with different sources such as genomic context, high-throughput experimental data, mining of databases, literature and analysis of co-expressed genes. All these associations were assigned with a probabilistic confidence score, where the STRING utilized special scoring framework, based on benchmarks of the different types of associations against a common reference set. The results are listed as four distinguished categories of highest confidence score (0.9–1), high confidence (0.7–0.8), medium confidence (0.4–0.6) and low confidence (upto 0.1). Confidence score is an indicator of interactions between nodes that are connected by multiple paths [25–29].

2.2. Functional enrichment analysis 2.2.1. STRING tool Gene Ontology (GO) and annotations were collected from the Uniprot [30]. STRING based GO was grouped using the p-value. The p-value of r0.05 indicates significant results. The p-values and functional annotations such as biological process, molecular function and cellular component for functional partners were extracted from STRING dataset.

2.2.2. DAVID tool DAVID version 6.7 was used for functional enrichment of the functional partners in the networks. It is a pre-computed database which is displayed in a tabular format containing similar annotation; It sorts the enriched gene-sets into different, with partially overlapping groups of all genes [31–33].

2.3. Clustering analysis MCODE algorithm was used to identify the highly interacting nodes present in the network. This was the first algorithm used to screen the protein complex. It was based on vertex weighting, complex prediction and optimal post- processing by assigning the weight to the vertex in local neighborhood density from the dense regions according to given parameters. The extracted clusters were ranked by scoring through density and size [34]. For our analysis, we set the parameters as node score cutoff: 0.2, haircut: true, fluff: false, K-core: 4–8 and max depth from Seed: 100.

2.4. Gene interaction network construction Graphical network model was generated using Cytoscape software for visualizing, modeling, and analyzing molecular and genetic interaction networks. Cytoscape provided graphical layouts that supported three different algorithms, namely springembedded layout, hierarchical layout, and circular layout. The larger networks can be visualized using Cytoscape version 2.8.3 and 3.0.1 [35].

P. Anitha et al. / Computers in Biology and Medicine 48 (2014) 17–27

3. Results We constructed gene network on 15 resistance gene of A. baumannii and evaluated the functional enrichment of functional partner using STRING and DAVID with the p-value r0.05. Further, the precise 8 clusters were derived using MCODE from the 168 functional partners. 3.1. Generated network module We collected 41 resistance genes of A. baumannii from ARDB [24]. For each resistance gene, the network was generated using STRING; of which 15 (adeA, aphA1, sul1, aacA4, strB, blaVEB-1, blaTEM-1, cat (cata1), teta, dfrA1, dfrA10, blaOXA-51, blaOXA-20, blaOXA-10 and aac3) resistance genes are found to have functional partners. Therefore, based on the confidence score (0.9–0.4), we collected the functional partners of each resistance gene which included 45 for blaOXA-51, 25 for sul1, 23 for adeA, 13 for dfrA10, 12 for dfrA1, 9 for teta, 8 for aphA1, 7 is for blaOXA-10, 7 for cat, 6 for blaOXA-20, 4 for aacA4, each 3 for strB and blaVEB-1, 2 for blaTEM-1 and 1 for aac3. These were retrieved from various A. baumannii strains such as A. baumannii AB0057, A. baumannii AB307, A. baumannii ACICU and A. baumannii AYE. A total of 168 functional partners were identified. Among them 46, 54 and 68 functional partners shared the highest, high and medium confidence scores respectively. These are categorized in Table 1, which also includes the 15 resistance gene IDs and the derived associated functional partners along with their respective strain information. Similarly, we generated the network using Cytoscape software which showed the interaction between 15 resistance genes with the 168 functional partners as depicted in Fig. 1. In addition, we also highlighted the best eight clusters (C 1–C 8) extracted from MCODE through the clustering analysis. 3.2. Functional enrichment analysis Functional enrichment analysis was carried out using STRING and DAVID. We collected the GO terms such as cellular process, molecular function and biological process for 15 resistance genes and for their 168 functional partners. Further, the significant GO terms were selected based on the p-value r 0.05. Out of 168 functional partners, 131 GO terms were extracted, in which 57 contributed to the biological processes, 60 for molecular function and 14 for cellular process from the STRING. Subsequently, we analyzed the functional enrichment in DAVID to ensure the significance of functions. Although, these results coincided with DAVID, we observed difference in p-value. From the annotation analysis, the GO terms (gene expression data) of the functional partners from STRING and DAVID were compared with the p-value, which is depicted in Table 2. Table 3 summarizes the important pathways Kyoto Encyclopedia of Genes and Genomes (KEGG) (folate biosynthesis [abn00790], one carbon pool by folate [aby00670], penicillin and cephalosporin biosynthesis [aby00311] and pantothenate and CoA biosynthesis [abm00770]) and three categories Integrative protein signature database (INTERPRO), (Swissprot) SP _(Protein Information Resource) PIR keywords and (Protein Information Resource) PIR_SUPERFAMILY of the functional partners from DAVID. 3.3. Clustering analysis Clustering analysis was performed using MCODE. The clusters were filtered on default parameter of MCODE to ensure the efficiency of functional partners towards antibiotic resistance. As shown in Table 4, we identified eight efficient clusters based on their score value. Around 52 functional partners were found to be

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present in eight clusters. Among the clusters, the first cluster possessed 11 nodes with a score value of 10.6 while the second cluster contained 12 nodes with score value of 6. Subsequently, the third, fourth and fifth clusters contained 5 nodes each with score values 5, 5 and 4.5 respectively. Next, the sixth cluster was incorporated with 6 nodes with a score value of 3.6. Seventh and eighth clusters included 4 nodes with the score value of 4.5 and 3.3 respectively. For each functional partner, the information on the respective central gene (resistance gene) and species are also provided in Table 4. These clusters are highlighted in Fig. 1. The enlarged view of eight clusters (C 1–C 8) is represented in Fig. 2.

4. Discussion Gene network, clustering and functional enrichment analysis Genes or proteins involved in the antibiotic resistance, and their related gene network analyses are of immense importance because it provides valuable information on biological and molecular complexes, signaling pathways of those genes, which are considered as reason for the resistance. From the constructed protein network, we predicted densely interconnected clusters among 168 functional partners using MCODE. This clustering analysis employed the local graph density to find protein complex. Accordingly, we found eight (8) efficient clusters. These clusters were well supported with the GO terms such as biological process, molecular function and cellular process along with their related pathway. After finding the biologically informative clusters, it was necessary to elucidate the relationship between the proteins. For this purpose, we employed text mining to confirm their association in drug resistance and to study their various functions. Our focus was mainly on the candidate genes or proteins present in the clusters, and to discuss the potential relationship between them and the antibiotic resistance mechanism. Accordingly, out of 8 clusters, we found that three (1, 2 and 4) clusters had significant functional enrichment of folate biosysthesis pathway, which is shown in Tables 2 and 3. These three clusters included a total of 28 genes. Therefore, we combined these clusters and studied their contribution in the drug resistance mechanism. The obtained results indicated that the functional partners in first cluster (glyA, thyA, purU1, purT, metH, fmt, folA, purH, purN, dfrA1 and dfrA10) and in second cluster (folE, folK, folB, ABAYE3612, folC, sul1, int, cat, blaTEM rpsL, tetR and aphA1) were involved in the folate biosysthesis and one carbon pool folate pathway since folate biosynthesis remains a key target for antimicrobial therapy. As various studies suggest, folate is an essential metabolite required mostly for one carbon transfer reactions and is a critical precursor for the synthesis of purines, pyrimidines and amino acids. So, targeting the essential enzymes like dihydropteroate synthase and dihydrofolate reductase in pathway with sulfadrugs antimicrobial agent has been successful for treatment [36]. But, due to the presence of mobile genetic elements and mutation in gene, the drug resistance rate is increasing around the world [9–12,36]. Mainly, the two genes (dfrA1 and dfrA10) present in the class 1 integron are responsible for carrying these resistance genes and are also involved in dissemination among various species. Specifically, it confers resistance to trimethoprim, which inhibits the essential enzyme dihydrofolate reductase [36] that is considered as an important antibiotic for the treatment. In addition, overexpressions of these chromosomal Dfrs also result in trimethoprim resistance [37]. We observed that one of the most important antibiotic resistance genes in cluster is sul1 (dihydropteroate synthase) and int (integrase), which involves in the folic acid-containing compound

20

Table 1 Functional partners with highest, high and medium confidence score. S. no. 1 2

Organism

aac3 adeA

A. baumannii AYE – A. baumannii adeB, adeC, adeJ, 24_328, adeI AB0057 A. baumannii adeB, adeC, adeJ, adeK, adeI, 24_328 AB307 A. baumannii adeB, adeC, adeJ, 24_328, adeI ACICU

dfrA1 dfrA10 sul1

6

aacA4

7 8 9

strB blaVEB-1 blaTEM-1

10 11

blaOXA-10 blaOXA-51

12

blaOXA-20

13

aphA1

14

cat (cata1) teta

15

Highest (0.9–1)

High (0.7–0.8)

Medium (0.6–0.4)

ABAYE3575 adeK, 29_167, 9_135

– –

29_167, 29_170

–

adeK, 29_161

–

A. baumannii AYE thyA, glyA, folC fmt, purU1, meth purN A. baumannii AYE thyA, glyA, folA fmt, folC, purU1 A. baumannii folk, folC, folB, pabC AB0057 A. baumannii AYE folk, folC, folB, pabC A. baumannii ACICU_00588, ACICU_03115, ACICU_02343, ACICU ACICU_02806,

purT, purH, dfrA10, folA, ABAYE3646 metH, purT, purN, dfrA1, ABAYE3612, ABAYE3613, purH AB57_2475, ABAYE3568, folE

– – –

ABAYE1418, ABAYE3569, folE ACICU_02342, ACICU_02562, ACICU_02553, ACICU_02924, qacdeltaE, folA, glmM

– –

A. baumannii ACICU A. baumannii A. baumannii A. baumannii AB0057 A. baumannii A. baumannii AB307 A. baumannii A. baumannii 17978 A. baumannii AB0057 A. baumannii ACICU A. baumannii

–

ACICU_01675

AYE aphE AYE – –

ABAYE3639, ABAYE3598 ABAYE1713, IS1999, aadB AB57_2186, urea

– – –

AYE – adc

ABAYE1713, ABAYE3618 ABBFA_001601

AYE – –

ABAYE1713 A1S_1851, adc

cmlA1, blaVEB-1, sulDeltaI, panB, ABAYE3617 ABBFA_002804, ABBFA_000125, ABBFA_003465, ABBFA_001166, ABBFA_000194, ABBFA_000832, panD, panB adc A1S_3358,A1S_0807, A1S_3241, A1S_0045,2_314, panD, A1S_3466, carO, omp, gyrA AB57_3401

41_208, int, ACICU_01672

SDF

–

AB57_2186

–

ACICU_01963

–

–

ABSDF2854, ABSDF1250, ABSDF0837, ABSDF2955, bioF, allA, panB, ampD, panD ABSDF2854, bioF ABSDF1250, ABSDF0837, ABSDF2955, panB, panD, allA, ampD

A. baumannii – ACICU A. baumannii AYE –

ACICU_01963

ACICU_00225, ACICU_00882, sul1, qacdeltaE, ACICU_00224

1_194

A. baumannii – AB0057 A. baumannii – AB0057 A. baumannii AYE –

tetR

cmlA1, strB, repAci1, ABAYE3576, ABAYE3587, ABAYE2171, ABAYE3693 repAci1, blaTEM, folA, int, aphA1, rpsL

tetR

ABAYE3646, int, ABAYE3575, cat

ABAYE3639, lysR

ABAYE3598, ABAYE3597

P. Anitha et al. / Computers in Biology and Medicine 48 (2014) 17–27

3 4 5

Target gene

Table 2 List of GO terms of the functional partners. Target gene

Functional partners

GO terms

p-Value STRING

DAVID

Transporter activity Lipid binding Membrane Transmembrane transport Protein transport Protein transporter activity

4.49E-06 9.99E-09 7.17E-03 5.47E-02 – –

– 0.005818353 – – 0.04100762 0.0751

dfrA1, glyA, dfrA10, folA, ABAYE3646 dfrA1, fmt, dfrA10, ABAYE3646 purU1 metH, purN purT, purH dfrA1, dfrA10, folA, ABAYE3646 dfrA1, dfrA10, folA, ABAYE3646 folC, purT fmt purU1

GO:0006545 GO:0009165 GO:0006413 GO:0006189 GO:0050661 GO:0004146 GO:0005524 GO:0008168 GO:0016742

2.48E-07 1.04E-08 3.02E-03 1.01E-02 2.80E-05 9.83E-06 1.00E-04 5.15E-02 5.46E-10

0.008758431 3.79E-08 – 0.031777741 0.051971526 0.007578115 – – –

purH, purT, purN, glyA, fmt, purU1 thyA, glyA purH, purT, folA, purN, thyA, purU1, dfrA10 ABAYE3612

GO:0004372 GO:0005737 GO:0009165 GO:0004156

Glycine biosynthetic process Nucleotide biosynthetic process Translational initiation ‘de novo’ IMP Biosynthetic process NADP binding Dihydrofolate reductase activity ATP binding Methyltransferase activity Hydroxymethyl-, formyl- and related transferase activity Glycine hydroxymethyltransferase activity Cytoplasm Nucleotide biosynthetic process Dihydropteroate synthase activity

– 1.10E-10 3.93E-02 9.83E-06

8.40E-12 – 3.79E-08 –

sul 1

folA ACICU_02553 folE, folA, ACICU_02562 folA folB, folE, folC, AB57_2475, folK, pabC, ACICU_03115 ACICU_02562 ACICU_03115 folk, pabC AB57_2475, ABAYE1418, ACICU_00588 folB, metH, folK, pabC, folE, folC, AB57_2475, ACICU_03115, ACICU_02562 ACICU_02924 ACICU_02924, folE folK, folB, folC, folE, AB57_2475, ABAYE1418, ACICU_00588, ACICU_02343, ACICU_02342, ACICU_03115, ACICU_02562

GO:0006545 GO:0008152 GO:0006730 GO:0046677 GO:0006732 GO:0005524 GO:0003848 GO:0006760 GO:0016887 GO:0008270 GO:0009396

Glycine biosynthetic process Metabolic process One-carbon metabolic process Response to antibiotic Coenzyme metabolic process ATP binding 2-Amino-4-hydroxy-6- hydroxymethyl dihydropteridine diphosphokinase activity Folic acid and derivative metabolic process ATPase activity Zinc ion binding Folic acid-containing compound biosynthetic process

1.38E-04 1.33E-02 2.52E-04 – 1.33E-02 1.57E-02 1.57E-05 – 7.14E-03 1.76E-02 5.10E-11

– – – 3.50E-07 5.05E-08 – – 5.33E-12 – – 3.64E-09

aacA4

int ACICU_01672, ACICU_01675 ACICU_01672, ACICU_01675

GO:0006310 GO:0019290 GO:0015343

DNA recombination Siderophore biosynthetic process Siderophore transmembrane transporter activity

3.03E-03 1.44E-04 2.71E-03

– 0.00424722 0.00924636

strB

strB, aphE, ABAYE3639, ABAYE3598, ABAYE3639, ABAYE3598, strB, aphE strB, aphE ABAYE3639, ABAYE3598

GO:0046677 GO:0045892 GO:0050299 GO:0034071 GO:0003677

Response to antibiotic Negative regulation of transcription, DNA-dependent Streptomycin 3ʺ-kinase activity Aminoglycoside phosphotransferase activity DNA binding

3.33E-08 4.58E-04 1.56E-05 – 3.50E-02

0.00424636 – 2.53E-04 5.07E-04 –

blaVEB-1

blaVEB-1, ABAYE1713 aadB

GO:0008800 GO:0008871

Beta-lactamase activity Aminoglycoside 2ʺ-nucleotidyltransferase activity

9.33E-05 1.00E þ 00

0.006067 –

blaTEM blaOXA-10

blaTEM blaOXA-10 blaOXA-10 blaOXA-10, ABAYE1713, blaVEB-1 blaOXA-10 cmlA1

GO:0046677 GO:0017001 GO:0008800 GO:0017144 GO:0016999 GO:0005215

Response to antibiotic Antibiotic catabolic process Beta-lactamase activity Drug metabolic process Antibiotic metabolic process Transporter activity

7.96E-07 2.07E-05 – 2.43E-05 – 1.00E þ 00

– – 0.0060671 3.54E-05 0.02435722 –

adeB, adeK, adeC, adeA, adeA, adeA,

dfrA1

adeC, adeJ, adeK, 29_167, 24_328, 9_135, 29_170 adeC, 9_135, 29_170 adeK, 9_135, 29_170 adeI, 29_161 adeI adeI

P. Anitha et al. / Computers in Biology and Medicine 48 (2014) 17–27

GO:0005215 GO:0008289 GO:0016020 GO:0055085 GO:0015031 GO:0008565

adeA

21

– 3.50E-07 4.63E-04 0.031890546 0.052080909 0.21227678 – 3.08E-04 1.53E-08 5.91E-05 1.28E-04 7.73E-03 4.86E-02 4.88E-01 ABAYE3587, aphA1 cat, tetR, folA, rpsL, blaTEM, aphA1 tetR, teta ABAYE3639, ABAYE3598 tetR, ABAYE3639, ABAYE3598 ABAYE3598, lysR ABAYE3598, lysR, tetR, ABAYE3646, ABAYE3575 ABAYE3646, ABAYE3575 aphA1 cat (cata1) teta

GO:0046677 GO:0046677 GO:0046677 GO:0045892 GO:0006351 GO:0003677 GO:0015074

GO:0015940 GO:0017144 GO:0009058 ABBFA_001166, panB, panD A1S_3241, A1S_1851 ABBFA_002804, bioF blaOXA-51

Response to antibiotic Response to antibiotic Response to antibiotic Negative regulation of transcription, DNA-dependent Transcription, DNA-dependent DNA binding DNA integration

7.59E-05 – 6.01E-02

GO terms Functional partners Target gene

Table 2 (continued )

Pantothenate biosynthetic process Drug metabolic process Biosynthetic process

STRING

p-Value

0.00511695 0.024357 –

P. Anitha et al. / Computers in Biology and Medicine 48 (2014) 17–27

DAVID

22

biosynthetic process [GO: 0009396] and DNA recombination [GO:0006310]. We also observed that these genes were well integrated with other genes in the network. The sul1 showing resistance to sulfonamide in bacteria is a growing concern which targets dihydropteroate synthase in folate synthesis pathway. This sul1 is usually located in the 30 conserved region of a class 1 integron and 50 contain a conserved integrase gene (int) in gene cassettes [38] and is responsible for dissemination. As shown in Table 4 and Fig. 2, the fourth cluster (4) (ACICU_00588, ACICU_02553, ACICU_03115, ACICU_02343 and ACICU_02562) is related with folic acid-containing compound biosynthetic processes and associated with sul1 gene. Interestingly, we noticed that the int1 were also involved in folic acid-containing compound biosynthetic process [GO:0009396] and that it was connected strongly with CAT1 gene, which was responsible for the chloramphenicol acetyltransferase 1 (CAT) synthesis that are associated with chromosomal and plasmid DNA. It also shows resistance to chloramphenicol antibiotic [39]. Another important associated gene that confers resistance to β-lactams is blaTEM-1 (response to antibiotic [GO:0046677]). The product of blaTEM-1 hydrolyzes penicillins and ampicillin confers resistance [40,41]. In addition, the other two interacted genes rpsL and aphA1, the spontaneous gene mutation in rpsL (30S ribosomal protein S12) also leads to streptomycin resistance [42] and aphA1 (response to antibiotic [GO:0046677]) is an aminoglycoside resistance gene which encodes kanamycin resistance [43]. Along with this, TetR is also associated with tetracycline resistance. It blends with Mg2 þ that leads to conformational change which renders the TetR protein binding to DNA. As a result, TetR and TetA were expressed and involved in resisting tetracycline [44]. Our result revealed that various resistance genes are associated with each other and are involved in resisting different classes of antibiotics which have become a serious problem in the field of medicine. However, it is clear that functional partners in a cluster share similar pathways. Surprisingly, there are literature reports which suggest that targeting the folate biosynthesis pathway in bacteria have been successful in combating the resistance of antibiotics. The development of antibiotics to those targets is acknowledged, and several drugs are validated for clinical use [45]. Our result also suggests that FolA, FolC and FolK are an attractive enzymatic target [46]. But, FolA protein is also capable of mediating resistance to trimethoprim [37], especially, thyA gene (thymidylate synthase) encoded in ORF upstream from the folA gene, which is an essential enzyme in the production of tetrahydrofolate [47,48]. This is essential for DNA replication. This unique mechanism of flavindependent thymidylate synthase makes it an attractive target for antibiotic drug development [49]. The closely interacted FolB is a dihydroneopterin aldolase enzyme in the folate biosynthesis pathway. It is an important antibacterial drug target [45]. Also mutant fmt gene (methionyl-tRNA formyltransferase) showed resistance actinonin is a peptide deformylase inhibitor [50]. However, these functional partners (glyA, purN, purT, purH, metH, ABAYE3646, ABAYE3612, ABAYE3646 and ABAYE3646) pose the GO terms such as glycine biosynthetic process [GO:0006545], nucleotide biosynthetic process [GO:0009165], ‘de novo’ IMP biosynthetic process [GO:0006189], pteridine-containing compound metabolic process [GO:0042558], NADP binding [GO:0050661] and dihydrofolate reductase activity [GO:0004146] (Table 2). Due to the inadequate text mining reports, their role in antibiotic resistance is unclear. Similarly, the functional partners of third (3) and sixth (6) cluster shared the significant GO terms transporter activity [GO:0005215], lipid binding [GO:0008289] involved in molecular function, integral to membrane [GO:0016021] and membrane [GO:0016020] of cellular component (Table 2) also resulted in antibiotics resistance. These functional partners posed multidrug efflux proteins that are involved in antibiotics resistance. Our results suggest that adeA gene is closely

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23

Table 3 List of pathway and three categories of functional partners. Target gene Functional partners

Category

dfrA1

purH, purT, folA, metH, purN, glyA, fmt, thyA, purU1, dfrA10 folA, folC

KEGG pathway

abn00790: Folate biosynthesis

0.02817113

sul1 blaOXA-10

folA, folK, pabC, folE, folC, Ab57_2475, ABAYE1418 KEGG pathway blaOXA-10, blaVEB-1, ABAYE1713 KEGG pathway blaOXA-10, blaVEB-1 KEGG pathway

abn00790: Folate biosynthesis aby00311: penicillin and cephalosporin aby02020: Two-component system

5.70E-14 4.87E-06 0.048785351

blaVEB-1 blaOXA-51 adeA

ABAYE1713, blaVEB-1 panD, panB adeJ, adeB adeK, adeC

KEGG Pathway KEGG Pathway INTERPRO INTERPRO

0.001424907 0.013614893 0.011385758 0.005705102

adeK, adeC int

INTERPRO INTERPRO

aby00311: penicillin and cephalosporin biosynthesis abm00770: Pantothenate and CoA biosynthesis IPR001036: Acriflavin resistance protein IPR010131: RND efflux system, outer membrane lipoprotein, NodT IPR003423: Outer membrane efflux protein IPR013762 Integrase-like, catalytic core, phage

cat

folA, rpsl, cat, tetR folA, cat, tetR folA, int, cat, tetR tetR tetR

SP_PIR keywords SP_PIR keywords SP_PIR keywords INTERPRO PIR_SUPERFAMILY

Antibiotic resistance 2.72E-09 Transposable element 1.06E-07 Plasmid 4.17E-05 IPR004111:Tetracycline transcriptional regulator, TetR, C-terminal 0.001011307 PIRSF003216:tetracycline repressor 0.00183255

teta

tetR, tetA tetR, tetA lysR, tetR, tetA blaOXA-10, ABAYE1713, blaVEB-1

SP_PIR SP_PIR SP_PIR SP_PIR

Transposable element Antibiotic resistance Transcription regulation Hydrolase

KEGG pathway

keywords keywords keywords keywords

DAVID p-value abn00670: One carbon pool by folate

3.05E-17

0.012518962 0.044835

9.26E-06 4.09E-05 0.004480166 0.013883

Table 4 List of Clusters from MCODE. Cluster Score Nodes Edges Gene or proteins

Target gene

Organisms

1

dfrA1, dfrA10

A. baumannii AYE

10.6

11

88

glyA, thyA, purU1, purT, metH, fmt, purH, purN, dfrA10, dfrA1 folA

dfrA1, dfrA10, sul1, cat A. baumannii AYE, A. baumannii ACICU, (cata1) A. baumannii AB0057

2

6

12

68

aphA1 folE, folk, folB int cat, rpsL blaTEM tetR ABAYE3612 folC sul1

aphA1, cat sul1 aacA4, cat cat (cata1) blaTEM, cat cat (cata1), teta dfrA10 dfrA1, dfrA10, sul1 sul1, blaOXA-20

A. A. A. A. A. A. A. A. A. A.

3

5

5

56

adeC, adeB, adeI, adeA 29_161

adeA adeA

A. baumannii AB0057, A. baumannii AB307 A. baumannii ACICU

4

5

5

10

ACICU_00588 ACICU_02553 ACICU_03115 ACICU_02343 ACICU_02562

sul1

A. baumannii ACICU

5

4.5

5

19

panD, blaOXA-51

blaOXA-51

panB bioF ABBFA_002804

blaOXA-51 blaOXA-51 blaOXA-51

A. baumannii AB307, A. baumannii AYE, A. baumannii 17978, A. baumannii SDF A. baumannii AB307, A. baumannii SDF, A. baumannii AYE A. baumannii SDF A. baumannii AB307

24_328, adeK, adeJ

adeA

29_170, 29_167 9_135

adeA adeA

A. A. A. A.

6

3.6

6

18

baumannii baumannii baumannii baumannii baumannii baumannii baumannii baumannii baumannii baumannii

baumannii baumannii baumannii baumannii

AYE, A. baumannii AB0057, AB0057, A. baumannii AYE, ACICU, A. baumannii AB0057, AB0057 AB0057 AB0057 AYE AYE, A. baumannii AB0057 AB0057, A. baumannii AYE, ACICU

AB0057, A. baumannii AB307, ACICU AB307 AB0057

7

4.5

4

9

cmlA1, blaOXA-10 blaVEB-1, aadB

blaOXA-10, aphA1 blaOXA-10, blaVEB-1

A. baumannii AYE A. baumannii AYE

8

3.3

4

5

aphE strB ABAYE3575, ABAYE3646

strB strB, aphA1 teta, aac3

A. baumannii AYE A. baumannii AYE A. baumannii AB0057

associated with 11 functional partners in A. baumannii 17978, A. baumannii AB0057 and A. baumannii AYE. We noticed that two clusters (3 and 6) were found significant from the overall network.

The third cluster consisted of adeA, adeB, adeC, 29_161 and adeI genes and the sixth cluster included adek, adeJ, 24_328, 29_170, 29_167 and 9_135 (Fig. 2).

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Fig. 1. Overall graphical representation of gene/protein network on resistance genes of A. baumannii. The network encompassed of 168 functional partners (node) and it is represented in red (functional partners in non-cluster) and blue color (functional partners in cluster). The circle highlight the best eight clusters and labeled as C 1–C 8 (MCODE). Each functional partner in clusters is shown in blue color. The interconnecting edge between the two functional partners (sul1 and int) in C 2 cluster is highlighted in thick black color. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Generally, the transport systems (efflux pumps proteins) present in bacteria are involved in transporting the various compounds like antibiotics and physiological substrates into or outside (efflux) the cells. Among families of efflux-pump proteins, the resistance-nodulation-cell divisions (RND) family is strongly associated with MDR in A. baumannii [5]. The AdeABC efflux pump of A. baumannii conferred resistance to various classes of antibiotics. The genes that encoded AdeABC efflux pump were located on the bacterial chromosome. The adeA, adeB and adeC genes encoding the periplasmic accessory protein was located adjacent to the gene encoding transporter protein, which was located adjacent to the OMP. The efflux transporter (AdeB) captured its substrates either from within the phospholipid bilayer of the inner membrane or the cytoplasm [5,51]. AdeABC conferred resistance by extruding aminoglycosides, β-lactams, fluoroquinolones, tetracyclines, tigecycline, macrolides, chloramphenicol and trimethoprim [5]. Three multidrug efflux pumps, encoded by adeABC and adeIJK, were shown to contribute to drug resistance in A. baumannii [7,52–54]. In comparison to other clusters, the fifth cluster claimed to be very important since β-lactamases were responsible for resistance to penicillins, extended-spectrum cephalosporins, monobactams and Carbapenems. Among these β-lactamase classes (A, B, C and D), class D OXA-type resistance gene is considered as a growing concern. Similarly, OXA β-lactamases are also found as part of integrons [6]. This blaOXA-51 is naturally occurring and is linked with ISABA1 which confer high level resistance to Carbapenems [55]. This interacted with panD, panB, bioF and ABBFA_002804 in cluster (Fig. 2). The

enriched GO terms of panB is pantothenate biosynthetic process [GO: 0015940] and panD is referred to the alanine biosynthetic process [GO: 0006523] and bioF (8-amino-7-oxononanoate synthase) in Pantothenate and CoA biosyntheses (Table 3). The functions of those functional partners in drug resistance remain unclear. But, apart from the cluster, we observed A1S_3241, A1S_1851 and adc in network that possess, drug metabolic process [GO: 0017144], antibiotic metabolic process [GO: 0016999], hydrolase activity, acting on carbon–nitrogen (but not peptide) bonds, in linear amides [GO: 0016811] and beta-lactamase activity [GO: 0008800] and thus found to be involved in antibiotics resistance (Table 2). On the other hand, the seventh cluster consisted of blaVEB-1, aadB, blaOXA-10 and cmlA1. The enriched gene ontology of genes are: beta-lactamase activity [GO: 0008800], response to antibiotic [GO: 0046677], transporter activity [GO: 0005215] and the antibiotic catabolic process [GO: 0017001]. The blaVEB-1 gene cassette encoded the extended spectrum β-lactamase, blaVEB-1 that was increasingly isolated from worldwide Gram-negative rods [50–52] and was involved in penicillin and cephalosporin biosynthesis [aby00311]. This blaVEB-1 is commonly inserted into the variable region of different class 1 integrons and mostly associated with bla (blaOXA-10)-like cassette encoding a narrow-spectrum oxacillinase-type β-lactamase and arr-2like gene cassettes conferring resistance to rifampin. This is also associated with a downstream-located aadB gene cassette encoding an aminoglycoside adenylyltransferase which is involved in aminoglycoside resistance [56–58]. In addition, it associated with cmlA gene which conferred nonenzymatic resistance to chloramphenicol and

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Fig. 2. Schematic representation of MCODE clusters. The enlarged view of eight efficient clusters C 1, C 2, C 3, C 4, C 5, C 6, C 7 and C 8 of antibiotic resistant genes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

functions as a drug efflux pump [58]. This cluster resembled the importance of antibiotic resistance because different resistance genes were linked together, this resisted all existing antibiotics and this indicates the complication in treating these A. baumannii infections. Finally, the functional partners strB, aphE, ABAYE3646, ABAYE3575 were found to be present in cluster eighth (Table 4 and Fig. 2). The enriched gene ontology of these proteins were response to antibiotic [GO: 0046677] and negative regulation of transcription, DNAdependent [GO: 0045892], DNA binding [GO: 0003677], streptomycin 3ʺ-kinase activity [GO: 0050299] and ATP binding [GO: 0005524] (Table 2). The str gene cluster containing at least four genes (strR, strA, strB, and strC) was involved in streptomycin biosynthesis or streptomycin resistance [59]. These antibiotic resistance genes were found to be inserted into the antibiotic resistance islands (AbaRs) in transposons of A. baumannii. The aphE gene showed significant homology to the aph gene, encoding aminoglycoside 30 -phosphotransferase, APH (30 ). The aphE gene expressed streptomycin (SM) resistance and a SM phosphorylating enzyme in S. lividans strains [60]. Apart from these functional partners, we found the other two functionally important proteins ABAYE3639 and ABAYE3598, which are associated with strB in network. Our antibiotic resistance gene related gene network analyses revealed that out of 168 functional partners, 52 (eight cluster) were implicated either in antibiotics resistance or they could be considered as an effective antibacterial targets. To our surprise, apart from the cluster, there were a few other proteins in network also gaining an equal concern because they play various biological functions which might result in drug resistance and might be antibacterial targets in future. These are significantly comprehensible with the functional enrichment. Overall, this study implies the importance of drug

resistance in A. baumannii and provides an insight on the network of genes/proteins that contribute to antibiotic resistance.

5. Conclusion To conclude, A. baumannii is one of the most thriving organisms leading to nosocomial infections around the world. It possesses various antibiotic resistance genes that resist a wide range of antibiotics. Obviously, there is a constant increase in resistance, and it is imperative to understand the reasons behind increased dissemination of resistance genes that play a major role in MDR. Thus, gene network analysis on various resistance genes reveal the role of genes, and their associated functional partners which participate in enhancing the resistance to antibiotics by wandering in similar pathway and sharing the significant functional annotation. The outcome of our study might be helpful in understanding the mechanism of antibiotic resistance in A. baumannii and the interactions between resistance genes.

6. Summary A. baumannii causes a serious health problem in medicine by exhibiting resistance to all available antibiotics. There are multiple resistance genes involved in antibiotic resistance mechanism. In the present study, computational gene networks analysis is carried out on these resistance genes to elucidate the functional relationship between the central gene and their associated genes/ proteins in drug resistance. Accordingly, the functionally interacted network is constructed with 168 functional partners from 15

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resistance genes using STRING. Then, functional enrichment analyses on these functional partners are carried out using DAVID and STRING. We collected 131 GO terms which includes the biological process, molecular function, cellular process and their related pathway. In addition, we used MCODE to extract the best eight clusters from the network. Our results suggest that, the associated genes are related to antibiotic resistance mechanism and some of them might be potential antibacterial targets in future. This study provides comprehensive evidence on 15 selected resistance genes and their functionally associated genes in various antibiotics resistance mechanisms. It also provides information on the diverse biological process which includes gene functions and complex cellular mechanisms of the associated genes/proteins. This would help in better understanding the functions of associated partners and their impact on drug resistance. To conclude, the study provides useful information for researchers exploring in the field of antibiotic resistant A. baumannii and also for researchers in genome based drug discovery and development.

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Conflict of interest statement The authors declare that there is no conflict of interest.

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