Open Access

Microarray analysis of the transcriptional responses of Porphyromonas gingivalis to polyphosphate

BMC Microbiology201414:218

DOI: 10.1186/s12866-014-0218-2

Received: 2 June 2014

Accepted: 12 August 2014

Published: 24 August 2014

Abstract

Background

Polyphosphate (polyP) has bactericidal activity against a gram-negative periodontopathogen Porphyromonas gingivalis, a black-pigmented gram-negative anaerobic rod. However, current knowledge about the mode of action of polyP against P. gingivalis is incomplete. To elucidate the mechanisms of antibacterial action of polyP against P. gingivalis, we performed the full-genome gene expression microarrays, and gene ontology (GO) and protein-protein interaction network analysis of differentially expressed genes (DEGs).

Results

We successfully identified 349 up-regulated genes and 357 down-regulated genes (>1.5-fold, P < 0.05) in P. gingivalis W83 treated with polyP75 (sodium polyphosphate, Nan+2PnO3n+1; n = 75). Real-time PCR confirmed the up- and down-regulation of some selected genes. GO analysis of the DEGs identified distinct biological themes. Using 202 DEGs belonging to the biological themes, we generated the protein-protein interaction network based on a database of known and predicted protein interactions. The network analysis identified biological meaningful clusters related to hemin acquisition, energy metabolism, cell envelope and cell division, ribosomal proteins, and transposon function.

Conclusions

polyP probably exerts its antibacterial effect through inhibition of hemin acquisition by the bacterium, resulting in severe perturbation of energy metabolism, cell envelope biosynthesis and cell division, and elevated transposition. Further studies will be needed to elucidate the exact mechanism by which polyP induces up-regulation of the genes related to ribosomal proteins. Our results will shed new light on the study of the antibacterial mechanism of polyP against other related bacteria belonging to the black-pigmented Bacteroides species.

Keywords

Porphyromonas gingivalis Polyphosphate Transcriptome Microarray Gene ontology (GO) Protein-protein interaction network analysis

Background

Inorganic polyphosphate (polyP) is a chain of few or many hundreds of phosphate (Pi) residues linked by high-energy phosphoanhydride [1]. polyP has attracted considerable attention as a GRAS (generally recognized as safe) food additive by FDA with antimicrobial properties that can prevent spoilage of food [2],[3]. polyP inhibits the growth of various gram-positive bacteria such as Staphylococcus aureus[4]-[8], Listeria monocytogenes[8],[9], Sarcina lutea[7], Bacillus cereus[10], and mutans streptococci [11],[12], and of fungi such as Aspergillus flavus[5]. The ability of polyP to chelate divalent cations is regarded as relevant to the antibacterial effects, contributing to cell division inhibition and loss of cell-wall integrity [5],[13],[14]. On the other hand, large numbers of gram-negative bacteria including Escherichia coli and Salmonella enterica serovar Typhimurium are capable of growing in higher concentrations, even up to 10% of polyP [5],[7],[15].

Periodontal disease is caused by bacterial infection which is associated with gram-negative oral anaerobes. In our previous study [16], polyP (Nan+2PnO3n+1; n = the number of phosphorus atoms in the chain) with different linear phosphorus (Pi) chain lengths (3 to 75) demonstrated to have antibacterial activity against Porphyromonas gingivalis, a black pigmented, gram-negative periodontopathogen. polyP also showed antibacterial activity against other black-pigmented, gram-negative oral anaerobes such as Prevotella intermedia and Porphyromonas endodontalis[17],[18]. However, the antimicrobial mechanism of polyP against gram-negative bacteria has not yet been fully understood. In the past decade, global genome-wide studies of changes in expression patterns in response to existing and new antimicrobial agents have provided us with a deeper understanding of antimicrobial action [19]. In the present study, we performed the full-genome gene expression microarrays of P. gingivalis, and gene ontology (GO) and protein-protein interaction network analysis of the differentially expressed genes were also performed for elucidating the mechanism of antibacterial action of polyP.

Results and discussion

The complete list of the average gene expression values has been deposited in NCBI’s Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) and is accessible through GEO Series accession number GSE11471. Using filtering criteria of a 1.5 or greater fold-change in expression and significance P-values of <0.05, 706 out of 1,909 genes in P. gingivalis W83 were differentially expressed by polyP75 treatment. The expression of 349 transcripts was increased by polyP treatment while 357 showed decreased expression (Figure 1). To validate the microarray results, quantitative RT-PCR (qRT-PCR) of selected genes was performed. Five of the genes were selected from the up-regulated group and the other five from the down-regulated group in the polyP-treated P. gingivalis cells. We used 16S rRNA as a reference gene for normalization of the qRT-PCR data. There was a high correlation between the expression ratios determined by the microarray and the qRT-PCR (r = 0.926) (Figure 2).
https://static-content.springer.com/image/art%3A10.1186%2Fs12866-014-0218-2/MediaObjects/12866_2014_Article_218_Fig1_HTML.jpg
Figure 1

Differential gene expression in P. gingivalis W83 by polyP75 treatment. Differentially expressed genes with 1.5 fold change and P-value < 0.05 were plotted. X-axis presents fold difference between log2 expression of polyP75 treatment and no treatment, and y-axis shows the –log10 P -value. Up-regulated genes (over-expressed in polyP75 treatment) were represented as red color and down-regulated genes were colored in blue.

https://static-content.springer.com/image/art%3A10.1186%2Fs12866-014-0218-2/MediaObjects/12866_2014_Article_218_Fig2_HTML.jpg
Figure 2

Comparison of transcription measurements by microarray and qRT-PCR. The relative transcription levels for 10 genes are listed in Table 6. The qRT-PCR log2 values were plotted against the microarray data log2 values. The correlation coefficient (r) for comparison of the two datasets is 0. 92.

To broadly characterize the differentially expressed gene (DEG, up- and down-regulated genes) set, GO category enrichment analysis was performed. This analysis identified distinct biological themes associated with each group of the up-regulated and the down-regulated genes. The down-regulated genes were associated with GO terms related to metabolic process (GO:0008152, P = 0.0004), pyridine nucleotide biosynthetic process (GO:0019363, P = 0.0012), regulation of cell shape (GO:0008360, P = 0.002), and polysaccharide biosynthetic process (GO:0000271, P = 0.0015). The up-regulated genes were associated with GO terms related to cellular iron ion homeostasis (GO:0006879, P < 0.0001), ribosome (GO:0005840, P = 0.0032), transposase activity (GO:0004803, P < 0.0001), and DNA binding (GO:0003677, P < 0.0001).

Using 202 DEGs belonging to the above biological themes, we generated the protein-protein interaction network based on a database of known and predicted protein interactions. The network analysis identified 162 DEGs that have direct interaction with one another (Figure 3), and 5 biological meaningful clusters related to 1) iron/hemin acquisition, 2) energy metabolism and electron carriers, 3) cell envelope and cell division, 4) ribosome, and 5) transposon functions.
https://static-content.springer.com/image/art%3A10.1186%2Fs12866-014-0218-2/MediaObjects/12866_2014_Article_218_Fig3_HTML.jpg
Figure 3

Protein-protein interaction network of differentially expressed functional genes. The network was constructed based on the STRING database. Nodes (symbolized as circles and square) and edges (linking lines) represent DEGs and interactions among DEGs, respectively. Up-regulated genes were represented as a circular shape and down-regulated genes were presented as a square shape. Node color represents the functional annotation of each gene. By applying MCODE clustering algorithm, 5 clusters with the score greater than 3 were obtained.

Hemin acquisition and energy metabolism

In prokaryotic cells, respiration occurs in the cell membrane in which electrons are transferred sequentially through lipoquinones (menaquinones and ubiquinones) and a series of membrane-bound protein carriers such as cytochrome bc1 complex, although the exact organization of enzymes in the respiratory chains varies among different bacteria [20]. P. gingivalis requires hemin as an iron source for its growth [21]. The redox potential of hemin (heme), required as a prosthetic group of cytochrome b, allows it to mediate electron transport with generation of cellular energy [22],[23].

Among 6 genes of hmu locus (PG1551 to PG1556) encoding Hmu YRSTUV, which play a major role in hemin acquisition [24], five genes, but not hmuY, exhibited more than 2-fold decrease in the expression in the presence of polyP75 (Table 1). In addition, genes related to metabolic process including energy metabolism and biosynthesis of lipoquinones, which occupy a central and essential role in electron transport [20], were significantly down-regulated by polyP (Table 2). Genes related to biosynthesis of pyridine nucleotides, known as soluble electron carriers, were also down-regulated (Table 2). These results are compatible with our previous study in which the amount of hemin accumulated on the P. gingivalis surface increased while energy-driven uptake of hemin by the bacterium decreased in the presence of polyP75 [16]. It is conceivable that polyP induce hemin deficiency in P. gingivalis, resulting in disruption of the electron transport occurring in the bacterial membrane. Notably, the up-regulation of oxidative stress response was observed under hemin-limited conditions [25]. Hence, the up-regulation of a series of genes involved in oxidative stress, i.e., 4Fe-4S ferredoxin, rubrerythrin, thioredoxin, Fe-Mn superoxide dismutase, thiol peroxidase, Dps family protein, RprY, ferritin, and HtrA (Table 1), may be due to hemin limitation induced by polyP. However, it is also possible that excessive accumulation of hemin in the vicinity of the bacterial cell surface without formation of μ-oxo bisheme by the bacterium may cause oxidative stress on P. gingivalis[16], as the formation of μ-oxo bisheme protects from hemin-mediated cell damage [23],[26],[27].
Table 1

Differentially expressed genes related to iron/hemin aquisition and oxidative stress

Locus no.a

Putative identificationa

Cellular rolea

Avg fold differenceb

PG1551

hmuY protein

Transport and binding proteins: Cations and iron carrying compounds

−1.19c

PG1552

TonB-dependent receptor HmuR

Transport and binding proteins: Cations and iron carrying compounds

−2.28

PG1553

HmuSd

Hemin acquisitiond

−2.77

PG1554

HmuTd

Hemin acquisitiond

−3.44

PG1555

HmuUd

Hemin acquisitiond

−3.29

PG1556

HmuVd

Hemin acquisitiond

−2.15

PG1729

thiol peroxidase

Cellular processes : Detoxification

3.12

PG1421

Ferredoxin, 4Fe-4S

Energy metabolism : Electron transport

28.54

PG0195

Rubrerythrin

Energy metabolism : Electron transport

15.49

PG0034

Thioredoxin

Energy metabolism : Electron transport

2.76

PG1286

Ferritin

Transport and binding proteins:

2.59

Cations and iron carrying compounds

PG0090

Dps family protein

Cellular processes:

2.45

Adaptations to atypical conditions

PG1545

Superoxide dismutase, Fe-Mn

Cellular processes : Detoxification

2.34

PG1089

DNA-binding response regulator RprY

Regulatory functions : DNA interactions

2.00

Signal transduction: Two-component systems

PG0593

htrA protein heat induced serine protease

Protein fate: Degradation of proteins, peptides, and glycopeptides

4.20

aLocus number, putative identification, and cellular role are according to the TIGR genome database.

bAverage fold difference indicates the expression of the gene by polyP addition versus no polyP addition.

cThe cut off ratio for the fold difference was < 1.5.

dPutative identification and cellular role are according to Lewis [24].

Table 2

Differentially expressed genes related to energy metabolism and biosynthesis of electron carriers

Locus no.a

Putative identificationa

Avg fold differenceb

Energy metabolism : Amino acids and amines

PG1269

Delta-1-pyrroline-5-carboxylate dehydrogenase

−2.02

PG0474

Low-specificity L-threonine aldolase

−1.93

PG1401

Beta-eliminating lyase

−1.74

PG0343

Methionine gamma-lyase

−1.64

PG1559

Aminomethyltransferase

−1.54

PG0324

Histidine ammonia-lyase

−1.53

PG1305

Glycine dehydrogenase

−1.52

PG2121

L-asparaginase

−1.51

PG0025

Fumarylacetoacetate hydrolase family protein

2.11

Energy metabolism : Anaerobic/Fermentation

PG0687

Succinate-semialdehyde dehydrogenase

−1.76

PG0690

4-hydroxybutyrate CoA-transferase

−1.66

PG0689

NAD-dependent 4-hydroxybutyrate dehydrogenase

−1.58

PG1609

Methylmalonyl-CoA decarboxylase, gamma subunit

−1.87

PG1612

Methylmalonyl-CoA decarboxylase, alpha subunit

−1.71

PG1608

Methylmalonyl-CoA decarboxylase, beta subunit

−1.64

PG0675

Indolepyruvate ferredoxin oxidoreductase, alpha subunit

−1.53

PG1809

2-oxoglutarate oxidoreductase, gamma subunit

2.18

PG1956

4-hydroxybutyrate CoA-transferase

1.74

Energy metabolism : Biosynthesis and degradation of polysaccharides

PG2145

Polysaccharide deacetylase

−1.94

PG0897

Alpha-amylase family protein

−1.85

PG1793

1,4-alpha-glucan branching enzyme

−1.67

Energy metabolism : Electron transport

PG0776

Electron transfer flavoprotein, alpha subunit

−2.30

PG0777

Electron transfer flavoprotein, beta subunit

−1.91

PG1638

Thioredoxin family protein

−1.88

PG1332

NAD(P) transhydrogenase, beta subunit

−1.83

PG1119

Flavodoxin, putative

−1.69

PG0429

Pyruvate synthase

−1.64

PG1077

Electron transfer flavoprotein, beta subunit

−1.57

PG1858

Flavodoxin

−2.57

PG2178

NADH:ubiquinone oxidoreductase, Na translocating, E subunit

−1.51

PG0034

Thioredoxin

2.76

PG0195

Rubrerythrin

15.49

PG0548

Pyruvate ferredoxin/flavodoxin oxidoreductase family protein

2.58

PG0616

Thioredoxin, putative

1.52

PG1421

Ferredoxin, 4Fe-4S

28.54

PG1813

Ferredoxin, 4Fe-4S

1.65

Energy metabolism : Glycolysis/gluconeogenesis

PG0130

Phosphoglyceromutase

−1.68

Energy metabolism : Purines, pyrimidines, nucleosides, and nucleotides

PG1996

Deoxyribose-phosphate aldolase

−1.73

Energy metabolism : Pentose phosphate pathway

PG1747

Ribose 5-phosphate isomerase B, putative

−2.45

PG0230

Transaldolase

2.05

PG1595

Ribulose-phosphate 3-epimerase

2.22

Energy metabolism: Sugars

PG1633

Galactokinase

−1.89

Energy metabolism : TCA cycle

PG1614

Succinate dehydrogenase

2.25

PG1615

Succinate dehydrogenase

1.60

Energy metabolism : Other

PG1522

Mandelate racemase/muconate lactonizing enzyme family protein

−2.24

PG0279

NADP-dependent malic enzyme

1.82

PG1017

Pyruvate phosphate dikinase

1.75

PG1513

Phosphoribosyltransferase, putative/phosphoglycerate mutase family protein

3.05

PG1859

Glycerate kinase family protein

1.76

Biosynthesis of pyridine nucleotides

PG0058

Nicotinic acid mononucleotide adenyltransferase

−1.93

PG1578

Quinolinate synthetase

−1.62

PG0057

Nicotinate phosphoribosyltransferase

−1.61

PG0678

Pyrazinamidase/nicotinamidase, putative

2.00

Biosynthesis of menaquinone and ubiquinone

PG1870

Methlytransferase, UbiE/COQ5 family

−2.60

PG1467

Methlytransferase, UbiE/COQ5 family

−2.46

PG1523

Naphthoate synthase

−1.89

PG1521

O-succinylbenzoic acid--CoA ligase

−1.78

PG1525

Isochorismate synthase, putative

−1.50

aLocus number, putative identification, and cellular role are according to the TIGR genome database.

bAverage fold difference indicates the expression of the gene by polyP addition versus no polyP addition.

Cell envelope and cell division

Among genes involved in biosynthesis and degradation of surface polysaccharides and lipopolysaccharides, 9 genes were repressed and 5 genes increased by polyP. Among genes related to biosynthesis and degradation of murein sacculus and peptidoglycan, 7 genes were down-regulated (Table 3). For most bacteria, the peptidoglycan cell wall is both necessary and sufficient to determine cell shape [28]. In P. gingivalis W83 genome there is a group of genes called division/cell wall (DCW) cluster, which are involved in cell division and synthesis of peptidoglycan [29]-[31]: PG0575 (penicillin-binding protein 2), PG0576 (murE), PG0577 (mraY), PG0578 (murD), PG0579 (ftsW), PG0580 (murG), PG0581 (murC), PG0582 (ftsQ), PG0583 (ftsA), and PG0584 (ftsZ). Among these, mraY, murD, ftsW, murG, murC, and ftsQ (PG0577- PG0582) were down-regulated by polyP75. It seems that the reduced expression of the genes related to cell envelope biosynthesis in polyP-exposed P. gingivalis may be a result from disruption of the electron transport and reduced production of ATP, since ATP is fundamental for many metabolic processes in bacteria including cell wall biosynthesis and protein synthesis [32]. These transcriptional changes are partially in agreement with the previous report using Bacillus cereus in which polyP inhibited the bacterial cell division [10]. However, unlike B. cereus, formation of elongated aseptate cells and growth phase-dependent bacteriolysis were not observed in P. gingivalis exposed to polyP [16]. It was proposed that polyP, because of its metal ion-chelating nature, may affect the ubiquitous bacterial cell division protein FtsZ, whose GTPase activity is known to be strictly dependent on divalent metal ions. Then, polyP may consequently block the dynamic formation (polymerization) of the Z ring, which would explain the aseptate phenotype of B. cereus[10]. B. cereus exposed to polyP, however, showed normal DNA replication, chromosome segregation, and synthesis of the lateral cell wall [10]. In the present study, P. gingivalis W83 decreased the expression of genes in relation to biosynthesis of cell wall, purine, pyrimidine, nucleoside, and nucleotide, and replication of DNA in the presence of polyP75 (Table 3). These results probably indicate that polyP affects the overall proliferation process including biosynthesis of nucleic acids, DNA replication, biosynthesis of cell wall, and cell division in P. gingivalis.
Table 3

Differentially expressed genes related to cell envelope and cell division

Locus no.a

Putative identificationa

Avg fold differenceb

Cell envelope : Biosynthesis and degradation of murein sacculus and peptidoglycan

PG0575

Penicillin-binding protein 2

−1.41c

PG0576

UDP-N-acetylmuramoylalanyl-D-glutamyl-2, 6-diaminopimelate ligase

−1.42c

PG0577

Phospho-N-acetylmuramoyl-pentapeptide-transferase

−1.56

PG0578

UDP-N-acetylmuramoylalanine--D-glutamateligase

−1.58

PG0580

N-acetylglucosaminyl transferase

−1.78

PG0581

UDP-N-acetylmuramate--L-alanine ligase

−1.81

PG1342

UDP-N-acetylenolpyruvoylglucosamine reductase

−2.17

PG0729

D-alanylalanine synthetase

−1.80

PG1097

Mur ligase domain protein/alanine racemase

−1.58

Cellular process: Cell division

PG0579

Cell division protein FtsW

−1.74

PG0582

Cell division protein FtsQ

−1.80

PG0583

Cell division protein FtsA

−1.32 c

PG0584

Cell division protein FtsZ

−1.36 c

Cell envelope : Biosynthesis and degradation of surface polysaccharides and lipopolysaccharides

PG1155

ADP-heptose--LPS heptosyltransferase, putative

−1.94

PG1783

Glycosyl transferase, group 2 family protein

−1.87

PG2223

Glycosyl transferase, group 2 family protein

−1.77

PG1815

3-deoxy-manno-octulosonate cytidylyltransferase

−1.73

PG1712

Alpha-1,2-mannosidase family protein

−1.69

PG1345

Glycosyl transferase, group 1 family protein

−1.66

PG2162

Lipid A disaccharide synthase

−1.65

PG1560

dTDP-glucose 4,6-dehydratase

−1.57

PG1880

Glycosyl transferase, group 2 family protein

−1.53

PG0072

UDP-3-O-[3-hydroxymyristoyl] glucosamine N-acyltransferase

1.83

PG0750

Glycosyl transferase, group 2 family protein

1.51

PG1048

N-acetylmuramoyl-L-alanine amidase, family 3

2.96

PG1135

Bacterial sugar transferase

5.28

PG1143

Sugar dehydrogenase, UD-glucose/GDP-mannose dehydrogenase family

1.89

Cell envelope : Other

PG1019

Lipoprotein, putative

−5.47

PG1180

Hypothetical protein

−4.15

PG1713

Lipoprotein, putative

−2.01

PG1767

Lipoprotein, putative

−1.96

PG0490

Hypothetical protein

−1.74

PG1005

Lipoprotein, putative

−1.65

PG1948

Lipoprotein, putative

−1.56

PG0670

Lipoprotein, putative

−1.54

PG2155

Lipoprotein, putative

−1.53

PG1600

Hypothetical protein

−1.52

PG0188

Lipoprotein, putative

1.66

PG0192

Cationic outer membrane protein OmpH

2.68

PG0193

Cationic outer membrane protein OmpH

2.18

PG0717

Lipoprotein, putative

1.95

PG0906

Lipoprotein, putative

1.94

PG1452

Lipoprotein, putative

1.52

PG1828

Lipoprotein, putative

1.87

PG2105

Lipoprotein, putative

1.98

PG2224

Hypothetical protein

2.19

DNA metabolism : DNA replication, recombination, and repair

PG1814

DNA primase

−2.01

PG1993

Excinuclease ABC, C subunit

−1.77

PG1255

Recombination protein RecR

−1.64

PG1253

DNA ligase, NAD-dependent

−1.62

PG0237

Uracil-DNA glycosylase

−1.58

PG1378

A/G-specific adenine glycosylase

−2.83

PG1622

DNA topoisomerase IV subunit A

−2.02

PG1794

DNA polymerase type I

−1.51

PG2009

DNA repair protein RecO, putative

2.34

Purines, pyrimidines, nucleosides, and nucleotides : 2′-Deoxyribonucleotide metabolism

PG1129

Ribonucleotide reductase

−2.30

PG0953

Deoxyuridine 5′-triphosphate nucleotidohydrolase

−2.14

Purines, pyrimidines, nucleosides, and nucleotides : Nucleotide and nucleoside interconversions

PG0512

Guanylate kinase

−1.89

Purines, pyrimidines, nucleosides, and nucleotides : Pyrimidine ribonucleotide biosynthesis

PG0529

Carbamoyl-phosphate synthase small subunit

−1.70

PG0357

Aspartate carbamoyltransferase catalytic subunit

−1.54

Purines, pyrimidines, nucleosides, and nucleotides : Salvage of nucleosides and nucleotides

PG0558

Purine nucleoside phosphorylase

−1.51

PG0792

Hypoxanthine phosphoribosyltransferase

2.25

aLocus number, putative identification, and cellular role are according to the TIGR genome database.

bAverage fold difference indicates the expression of the gene by polyP addition versus no polyP addition.

cThe cut off ratio for the fold difference was < 1.5.

In several transcriptional profiling studies using gram-positive bacteria, a cell wall stress stimulon that includes genes involved in peptidoglycan biosynthesis was induced in the cells challenged with cell wall-active antibiotics [33],[34]. The bacterial cells appeared to respond to the cell wall-active antibiotics by attempting to raise the rate of peptidoglycan biosynthesis in order to compensate for the damaged and partially missing cell wall [35],[36]. Overall, the results indicate that the mode of action of polyP against P. gingivalis may be different from not only that of the cell wall-active antibiotics against gram-positive bacteria, but also that of polyP against gram-positive bacteria.

Ribosomal proteins

In bacteria, production of ribosome requires up to 40% of the cell's energy in rapidly growing bacteria and is therefore tightly regulated on several levels [37]. It seems that bacteria with kinetically impaired ribosomes can to some extent increase the number of ribosomes accumulated under poor growth conditions or under antibiotic challenge in order to compensate for their slower function [38],[39]. It has been reported that antibiotics that target the ribosome or translation factors up-regulate synthesis of ribosomal proteins and accumulate ribosome precursors in Streptococcus pneumoniae[40]. Similarly, in Clostridium difficile, genes encoding many ribosomal proteins were coordinately up-regulated by antibiotics such as amoxicillin, clindamycin, and metronidazole [38]. Therefore, it is conceivable that the up-regulation of the genes encoding ribosomal proteins of polyP- exposed P. gingivalis (Table 4) may reflect a compensatory response for slower or disturbed function of the ribosome.
Table 4

Differentially expressed genes related to ribosome

Locus no.a

Putative identificationa

Avg fold differenceb

Protein synthesis : Ribosomal proteins

PG0037

50S ribosomal protein L19

3.23

PG0167

Ribosomal protein L25

1.86

PG0314

Ribosomal protein L21

1.90

PG0315

50S ribosomal protein L27

1.78

PG0385

Ribosomal protein S21

3.98

PG0592

50S ribosomal protein L31

4.01

PG0656

50S ribosomal protein L34

6.80

PG0989

50S ribosomal protein L20

3.43

PG0990

Ribosomal protein L35

1.74

PG1723

Ribosomal protein S20

2.94

PG1758

Ribosomal protein S15

6.23

PG1959

Ribosomal protein L33

2.02

PG1960

Ribosomal protein L28

2.03

PG2117

30S ribosomal protein S16

2.93

PG2140

Ribosomal protein L32

3.40

PG0205

Peptide chain release factor 3

1.50

aLocus number, putative identification, and cellular role are according to the TIGR genome database.

bAverage fold difference indicates the expression of the gene by polyP addition versus no polyP addition.

Meanwhile, ribosome biosynthesis of bacteria is governed by transcriptional and translational regulatory mechanisms that provide a balanced and coordinated production of individual ribosomal components [41]. It has been suggested that some free ribosomal proteins act as autogenous feedback inhibitors that cause selective translational inhibition of the synthesis of certain ribosomal proteins whose genes are in the same operon as their own. This inhibition is due to the structural homology between certain ribosomal protein binding regions on 16S rRNA and the mRNA target site for the ribosomal protein [42]-[44]. Although autogenous regulation is known to be a general strategy of balancing ribosomal protein synthesis in bacteria [41], mechanisms for controlling ribosomal protein gene expression in P. gingivalis have not yet been characterized. Further studies will be needed to elucidate the regulatory mechanisms involved in ribosomal protein synthesis in P. gingivalis.

Transposon functions

The majority of the up-regulated genes related to mobile and extrachromosomal element functions were the genes encoding transposases (Table 5). Transposition is generally known to be triggered by cellular stress, i.e., nutritional deficiency, chemicals, and oxidative agents. Little is known about the transposition in P. gingivalis, but up-regulation of transposase-related insertion sequence elements was noticed in P. gingivalis W50 after treatment with H2O2[45]. Thus, it seems quite reasonable to speculate that induction of transposase is associated with oxidative stress-like response which occurred in P. gingivalis W83 due to the presence of polyP.
Table 5

Differentially expressed genes related to transposon functions

Locus no.

Putative identification

Avg fold difference

Mobile and extrachromosomal element functions: Transposon functions

PG0019

ISPg4 transposase

1.57

PG0050

ISPg4, transposase

1.81

PG0177

ISPg4, transposase

1.87

PG0194

ISPg3, transposase

2.18

PG0225

ISPg4, transposase

1.80

PG0261

ISPg3, transposase

2.20

PG0459

ISPg5, transposase

1.60

PG0487

ISPg4, transposase

1.98

PG0798

ISPg3, transposase

2.11

PG0819

Integrase

1.80

PG0838

Integrase

3.36

PG0841

Mobilizable transposon, excision protein, putative

3.78

PG0842

Mobilizable transposon, hypothetical protein, putative

2.84

PG0872

Mobilizable transposon, xis protein

3.87

PG0873

Mobilizable transposon, tnpC protein

9.34

PG0874

Mobilizable transposon, int protein

2.42

PG0875

Mobilizable transposon, tnpA protein

1.68

PG0970

ISPg4, transposase

1.79

PG1032

ISPg3, transposase

2.23

PG1061

ISPg6, transposase

2.03

PG1261

ISPg4, transposase

2.06

PG1262

ISPg3, transposase

2.11

PG1435

Integrase

2.77

PG1454

Integrase

1.88

PG1658

ISPg4, transposase

1.83

PG1673

ISPg4, transposase

1.77

PG2194

ISPg4, transposase

1.85

PG0461

ISPg7, transposase

−2.77

PG0277

ISPg2, transposase

−1.58

PG0865

ISPg2, transposase

−1.53

PG1746

ISPg2, transposase

−1.63

PG2176

ISPg2, transposase

−1.58

PG1350

ISPg2, transposase

−1.53

Conclusions

We observed that polyP causes numerous events of differential transcription in P. gingivalis. Down-regulated genes were related to iron/hemin acquisition, energy metabolism and electron carriers, and cell envelope and cell division. In contrast, up-regulated genes were related to ribosome and transposon functions. polyP probably exerts its antibacterial effect through inhibition of iron/hemin acquisition by the bacterium, resulting in severe perturbation of energy metabolism, cell envelope biosynthesis and cell division, and elevated transposition. Although the up-regulation of the genes related to ribosomal proteins may possibly reflect autogenous feedback inhibition to regulate the synthesis of certain ribosomal proteins in metabolically disturbed P. gingivalis by polyP, the exact mechanisms underlying this polyP-induced up-regulation of the genes have yet to be elucidated. The current information obtained from the gene ontology and protein-protein interaction network analysis of the differentially expressed genes determined by microarray will shed new light on the study of the antibacterial mechanism of polyP against other related bacteria belonging to the black-pigmented Bacteroides species.

Methods

Chemicals

polyP with a chain length of 75 (polyP75; sodium polyphosphate, glassy, Nan+2PnO3n+1; n = 75) was purchased from Sigma Chemical Co. (St. Louis, MO), dissolved in distilled water to a concentration of 10% (wt/vol), sterilized using a 0.22-μm filter, and stored at −20°C until use.

Bacterial strain and growth conditions

P. gingivalis strain W83 (kindly supplied by Dr. Koji Nakayama, Nagasaki University Graduate School of Biomedical Sciences) was cultured at 37°C anaerobically (85% N2, 10% H2, and 5% CO2) in half-strength brain heart infusion (BHI) broth (Becton Dickinson, Sparks, MD) supplemented with 0.5% yeast extract (Difco Laboratories, Detroit, MI), 5 μg/ml of hemin (Sigma), and 1 μg/ml of vitamin K1 (Sigma).

RNA isolation and cDNA synthesis

Use of high concentrations of antibacterial agents for extended periods of time changes the expression of a large set of genes and the effect may be secondary to the action of the drug [46]. Meanwhile, at sub-lethal concentrations, bacteria may sense antibiotics as extracellular chemicals to trigger different cellular responses such as an altered antibiotic resistance/tolerance profile [47]. Hence, we performed the full-genome gene expression microarrays of P. gingivalis W83 exposed to polyP75 at a concentration of 0.03%, which was previously determined to be MIC against the bacterium [16], for a short period of time. P. gingivalis culture grown to early exponential phase (OD600 = 0.3) was divided in half. One aliquot was left untreated, while the other one was treated with 0.03% polyP75. After incubation of both the bacterial cultures for 2 h under anaerobic conditions, the bacterial cells were harvested, and total RNA was extracted from the cells using Trizol Reagent (Invitrogen, Carlsbad, CA). RNA quality was monitored by Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA), and RNA quantity was measured by spectrophotometer. All the samples used in this study exhibited A260/A280 ratio of at least 1.8. cDNA was synthesized with 20 μg of total RNA using SuperScript® II Reverse Transcriptase (Invitrogen).

Microarray analysis

Two individual Cy3-labeled cDNA samples were hybridized into DNA microarrays (Nimblegen Systems, Inc., Madison, WI) containing the whole genome of 1,909 genes of P. gingivalis W83 for 16 h at 42°C. Five replicates of the genome were included per chip. An average of 19 different 60-mer probes which had at least three mismatches compared to other 60-mers represented each gene in the genome. A quality control check (hybridization) was performed for each array, which contained on-chip control oligonucleotides. Data were extracted from the scanned images using an Axon GenePix 4000B microarray scanner and NimbleScan Version 2.3. Quantile normalization was performed across replicate arrays, and RMA (Robust Multichip Average) analysis was performed to generate gene expression values. Genes evidencing statistically significant changes in expression (>1.5-fold difference) were identified via t-tests (P < 0.05).

Assessment of array data quality

To confirm the microarray results using qRT-PCR, 10 genes were selected, and specific primers for the selected genes (Table 6) were designed using Primer3 (http://fokker.wi.mit.edu/primer3/). All quantifications were normalized to the P. gingivalis 16S rRNA gene. The transcriptional ratio from qRT-PCR analysis was logarithm-transformed and then plotted against the average log2 ratio values obtained by microarray analysis [48].
Table 6

Real-time quantitative RT-PCR confirmation of selected genes

Locus no.a

Primer sequence (5′-3′)b

Product size (bp)

16S rRNA

F: TGTTACAATGGGAGGGACAAAGGG

118

R: TTACTAGCGAATCCAGCTTCACGG

PG0090

F: CAGAAGTGAAGGAAGAGCACGAAC

197

R: GTAGGCAGACAGCATCCAAACG

PG0195

F: TCCACGGCTGAGAACTTGCG

149

R: TGCTCGGCTTCCACCTTTGC

PG1545

F: CCAAACCCTCAACCACAATC

142

R: GGTACCGGCTGTGTTGAACT

PG0593

F: CGTGTGGGAGAGTGGGTATTGG

175

R: CGCCGCTGTTGCCTGAATTG

PG1089

F: CCATCGCGATCGATGATCAGGTAA

104

R: GGCATAGTTGCGTTCAAGGGTTTC

PG1019

F: TTCGCAGTATCCCATCCAAC

126

R: TCCGGCTCATAGACTTCCAA

PG1180

F: CAGTCTGCCACAGTTCACCA

124

R: CCCTACACGGACACTACCGA

PG1983

F: GCTCTGTGGTGTGGGCTATC

146

R: GGATAACAGGCAAACCCGAT

PG0885

F: CAGATCCAAATCGGGACTGA

156

R: GTAGAGCAAGCCATGCAAGC

PG1181

F: GATGAATTCGGGCGGATAAT

184

R: CCTTGAAGTGCTCCAACGAC

aBased on the genome annotation provided by TIGR (http://cmr.jcvi.org/cgi-bin/CMR/GenomePage.cgi?org=gpg).

bPrimers were designed using Primer3 program for the study except for the primers of P. gingivalis 16S rRNA and PG1089 [49], which were prepared based on the primer sequences published previously. The 16S rRNA gene was used as the reference gene for normalization. F, forward; R, reverse.

Gene ontology (GO) enrichment analysis

The GO term annotations for P. gingivalis were downloaded from the Gene Ontology website (http://www.geneontology.org/GO.downloads.annotations.shtml, UniProt [multispecies] GO Annotations @ EBI, Apr. 2013). To test the GO category enrichment, we calculated the fraction of gene in the test set (F test ) associated with each GO category. Then, we generated the random control gene set that has the same number gene of test set. In this process, the random control gene was selected by matching the length of the test gene. The fraction of genes in this randomly selected control set (F control ) associated with the current GO category was calculated. This random sampling process was repeated 10,000 times. Finally, the P-value for the enriched GO category in a test gene set was calculated as the fraction of times that F test was lower than or equal to F control .

Protein-protein interaction network analysis

The protein-protein interaction network data including score were obtained from the STRING 9.1 (http://string-db.org) [50], for P. gingivalis W83. We used Cytoscape software [51] for network drawing, in which nodes and edges represented DEGs and interactions among DEGs, respectively. DEGs with no direct interaction were discarded, and the final dataset consisting of 611 DEGs and 1,641 interactions were used for the network construction. In order to find significant interaction between DEGs, we applied the confidence cutoff as 0.400 (medium confidence).

To understand the biological functions of the DEGs in the network, we annotated 202 DEGs belonging to 8 relevant biological functional clusters and then generated the sub-network using these DEGs in the whole DEGs network constructed above. Cytoscape plug-in MCODE [52] was used to decompose the sub-network and 5 clusters with the score greater than 3 were identified.

Abbreviations

polyP: 

Inorganic polyphosphate

GO: 

Gene ontology

DEG(s): 

Differentially expressed genes (s)

qRT-PCR: 

Quantitative RT-PCR

Declarations

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011–0009233) and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2012R1A5A2051384).

Authors’ Affiliations

(1)
Department of Maxillofacial Biomedical Engineering, School of Dentistry, and Institute of Oral Biology, Kyung Hee University
(2)
Department of Life and Nanopharmaceutical Sciences, Kyung Hee University

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