Open Access

Staphylococcal enterotoxin B influences the DNA methylation pattern in nasal polyp tissue: a preliminary study

  • Claudina A Pérez-Novo1Email author,
  • Yuan Zhang2, 3,
  • Simon Denil4,
  • Geert Trooskens4,
  • Tim De Meyer4,
  • Wim Van Criekinge4,
  • Paul Van Cauwenberge1,
  • Luo Zhang2 and
  • Claus Bachert1, 5
Contributed equally
Allergy, Asthma & Clinical Immunology20139:48

https://doi.org/10.1186/1710-1492-9-48

Received: 20 September 2013

Accepted: 9 December 2013

Published: 16 December 2013

Abstract

Staphylococcal enterotoxins may influence the pro-inflammatory pattern of chronic sinus diseases via epigenetic events. This work intended to investigate the potential of staphylococcal enterotoxin B (SEB) to induce changes in the DNA methylation pattern. Nasal polyp tissue explants were cultured in the presence and absence of SEB; genomic DNA was then isolated and used for whole genome methylation analysis. Results showed that SEB stimulation altered the methylation pattern of gene regions when compared with non stimulated tissue. Data enrichment analysis highlighted two genes: the IKBKB and STAT-5B, both playing a crucial role in T- cell maturation/activation and immune response.

Keywords

Staphylococcus aureus enterotoxin B Chronic rhinosinusitis and nasal polyps DNA methylation MBD2 Whole genome methylation analysis Hypermethylation

Background

Staphylococcus aureus enterotoxins acting as superantigens are known biological factors amplifying the pro-inflammatory patterns of upper airway inflammatory diseases, specifically chronic rhinosinusitis with nasal polyposis (CRSwNP) [1, 2]. Recently, it has been demonstrated that bacterial infection and viral superantigens may lead to epigenetic deregulations affecting host cell functions [3]. This study aimed to investigate the potential of S. aureus enterotoxin B (SEB) to induce changes in the gene DNA methylation pattern in inflamed nasal tissue.

Subjects and methods

A detailed description of the procedures followed in the study is provided in the Additional file 1. Briefly, nasal polyp tissues from 3 patients with chronic rhinosinusitis and nasal polyposis were fragmented and homogenized as described previously [4] and subsequently cultured during 24 h in the absence or presence of 0,5 μg/ml of SEB (Sigma-Aldrich, MO, United States). After stimulation, genomic DNA was isolated and used for a whole genome methyl-CpG-binding domain2 (MBD2)- based DNA methylation analysis [5]. The sequence reads obtained were then mapped using BOWTIE [6] and the data were summarized using a MethylCap kit specific “Map of the Human Methylome” (http://www.biobix.be) containing 1,518,879 potentially methylated sites termed methylation cores (MCs) as shown in Figure 1. Methylation was defined as the peak coverage in the MCs and was analyzed with the software package "R" version 2.11.1.
Figure 1

Example of the visual representation of the results from MBD2 DNA methylation based analysis. The figure shows the methylation cores (MC) for the differentially methylated region (exon 22) of the gene IKBKB on the genome browser "The Hitchhiker’s guide to the Genome" (http://www.biobix.be). The height of the black peaks shows the methylation level in that specific region in samples cultured in medium and with staphylococcal enterotoxin B (SEB).

Results

A summary of the methylation data and analysis is provided in the repository file 1. In order to identify the genes which methylation status was affected by SEB stimulation, the obtained methylation cores (MCs) were ranked by “Likelihood Treatment” in descending order and an arbitrary "cut-off" was applied to select the 200 top differentially methylated genes. This ranking showed that stimulation with SEB mainly resulted in de novo hypermethylation (130 MCs) rather than in hypomethylation (70 MCs) and as expected, the methylation changes mainly occurred at intragenic regions (introns and exons) and to a lesser extend at the promoter or transcription start sites, as there were many more exonic and intronic MCs than promoter MCs in the entire map (Figure 2).
Figure 2

Distribution of the genomic regions showing differential methylation cores. The figure shows the percentage of genes showing different methylation cores in nasal polyp tissue cultures stimulated with S. aureus enterotoxin B (SEB) when compared with non-stimulated tissue. Most of the methylation changes occurred in intragenic regions (exons and introns) and in less extend at the promoter genes site.

The 200 MCs primarily selected were then filtered using a “Likelihood Treatment” cut-off of 0.4 or more which translates to an estimated 40% probability that the MC is differentially methylated between samples treated or not with SEB. This cut-off value was used due to the low likelihood treatment values and low confidence obtained as result of the low coverage. This process provided a list of 43 genes exhibiting changes in the methylation state after 24 h culture with SEB (Table 1). From this list, 33 genes were hypermethylated while 10 genes showed hypomethylation. Three genes showed hypermethylations at promoter regions, and 18 and 12 genes at the intron and exon regions, respectively. Hypomethylation events were less frequent and they occurred at exonic regions in 9 genes, at introns in 1 gene and none at the promoter site (Table 1). Additionally, changes in the methylation status in other regions of these genes were also observed, but they did not pass the likelihood treatment cut-off due to low coverage; this may be solved in future studies as high coverage becomes affordable due to declining sequencing costs.
Table 1

Genes with different methylation status after stimulation with SEB

Methylation status

Location

Gene

Chr

Likelihood

Ensemble accession

Methylation score TCM

Methylation score SEB

Hypermethylation

Promoter

CTSLL2

10

0.599515

ENSG00000224036

1

7

  

Y_RNA

6

0.595036

ENSG00000201555

1

8

  

AC022026.3

10

0.589686

ENSG00000213731

0

6

 

Intron

CHD5

1

0.887026

ENSG00000116254

0

8

  

STAB2

12

0.644866

ENSG00000136011

1

8

  

ROBO1

3

0.53597

ENSG00000169855

0

5

  

AJAP1

1

0.512208

ENSG00000196581

1

6

  

TTLL1

22

0.4962

ENSG00000100271

0

4

  

GLT1D1

12

0.472979

ENSG00000151948

1

8

  

MAD1L1

7

0.47049

ENSG00000002822

0

5

  

HEATR5B

2

0.4673

ENSG00000008869

0

4

  

LGMN

14

0.459486

ENSG00000100600

0

5

  

FAM59A

18

0.458542

ENSG00000141441

0

4

  

STAT5B

17

0.458148

ENSG00000173757

0

5

  

NKD1

16

0.457063

ENSG00000140807

1

7

  

SLC25A24

1

0.431776

ENSG00000085491

1

7

  

AC073343.1

7

0.430122

ENSG00000228010

1

6

  

TMEM138

11

0.412654

ENSG00000149483

1

6

  

MPRIP

17

0.411674

ENSG00000133030

1

7

  

GAA

17

0.40881

ENSG00000171298

1

8

  

RFX3

9

0.407894

ENSG00000080298

1

6

 

Exon

ADAMTS16

5

0.552893

ENSG00000145536

1

10

  

IKBKB

8

0.533067

ENSG00000104365

1

7

  

ZNF541

19

0.513317

ENSG00000118156

1

8

  

KANK2

19

0.495541

ENSG00000197256

0

5

  

CYBA

16

0.484142

ENSG00000051523

0

4

  

UBE2I

16

0.471502

ENSG00000103275

1

6

  

OLFM1

9

0.446344

ENSG00000130558

1

7

  

MARK2

11

0.438781

ENSG00000072518

1

7

  

CORO7

16

0.434694

ENSG00000103426

1

6

  

KCNQ2

20

0.428704

ENSG00000075043

1

8

  

ASAP1

8

0.414992

ENSG00000153317

1

6

  

NOC2L

1

0.412654

ENSG00000188976

1

6

Hypomethylation

Intron

POLR3E

16

0.647331

ENSG00000058600

4

0

  

VPS13B

8

0.576756

ENSG00000132549

4

0

  

ANKRD13A

12

0.520639

ENSG00000076513

4

0

  

ZBTB20

3

0.491524

ENSG00000181722

4

0

  

AC087393.2

17

0.44588

ENSG00000233098

5

1

  

ZDHHC1

16

0.440425

ENSG00000159714

3

0

  

PDZD2

5

0.440425

ENSG00000133401

3

0

  

DLGAP2

8

0.423913

ENSG00000198010

5

1

  

NDST1

5

0.405485

ENSG00000070614

3

0

 

Exon

AC016907.1

2

0.493142

ENSG00000233862

3

0

The table shows the nasal polyp tissue genes and locations undergoing methylation changes (hypermethylation and hypomethylation) with a likelihood treatment > 0.4) after stimulation with SEB. Methylation score refers to the average methylation of the 3 samples in each experimental group. TCM: tissue culture medium or no stimulated cells, SEB: cells stimulated with S. aureus enterotoxin B.

These 43 top ranking genes were then selected for enrichment analysis in the Reactome database using the overrepresentation pathway analysis [7]. This algorithm delivered a list of “Statistically over-represented pathways” which represents all Reactome pathways containing proteins from the input gene list. This analysis resulted in 17 pathways (Table 2) containing 6 potentially affected genes (STA5B, IKBKB, STAB2, NDST1, LGMN and CYBA). Based on previously published data regarding host-cellular immune responses to bacterial exotoxins we selected three main pathways (Table 3) containing the genes: STAT5B, IKBKB, POLR3 and LGMN. These genes regulate processes influencing the response of cells to superantigens according to the biological function obtained in UniProt and the Reactome databases (Table 3).
Table 2

Biological pathway analysis of the 43 top ranked genes showing differential methylation after simulation with SEB

P-value

Number of genes mapping the pathway

Total number of genes in the pathway

Pathway identifier

Pathway name

Genes mapping to the pathway

0,004

2

58

REACT_118823

Cytosolic sensors of pathogen-associated DNA

IKBKB, POLR3E

0,012

2

110

REACT_22232

Signaling by interleukins

STAT5B, IKBKB

0,013

2

115

REACT_6966

Toll-like receptors cascades

LGMN, IKBKB

0,015

2

120

REACT_121315

Glycosaminoglycan metabolism

STAB2, NDST1

0,015

2

120

REACT_147739

MPS IX - Natowicz syndrome

STAB2, NDST1

0,015

2

120

REACT_147853

Mucopolysaccharidoses

STAB2, NDST1

0,015

2

120

REACT_147788

MPS IIIB - Sanfilippo syndrome B

STAB2, NDST1

0,015

2

120

REACT_147719

MPS VI - Maroteaux-Lamy syndrome

STAB2, NDST1

0,015

2

120

REACT_147825

MPS IV - Morquio syndrome A

STAB2, NDST1

0,015

2

120

REACT_147860

MPS IIIC - Sanfilippo syndrome C

STAB2, NDST1

0,015

2

120

REACT_147759

MPS VII - Sly syndrome

STAB2, NDST1

0,015

2

120

REACT_147734

MPS II - Hunter syndrome

STAB2, NDST1

0,015

2

120

REACT_147857

MPS I - Hurler syndrome

STAB2, NDST1

0,015

2

120

REACT_147749

MPS IIID - Sanfilippo syndrome D

STAB2, NDST1

0,015

2

120

REACT_147753

MPS IIIA - Sanfilippo syndrome A

STAB2, NDST1

0,015

2

120

REACT_147798

MPS IV - Morquio syndrome B

STAB2, NDST1

0,045

4

915

REACT_116125

Disease

STAT5B, STAB2, NDST1, CYBA

All genes used in the analysis showed a likelihood of treatment related effect > 0,4. P-value: un-adjusted, not corrected for multiple testing, representing the probability (from hypergeometric test) of finding a given number or more genes in each pathway by chance.

Table 3

Sub-pathways and biological functions of the most representative genes showing hyper-methylation after stimulation with SEB

Gene

UniProt

Pathway name

Sub-pathways

Biological function

 

ID

(Reactome)

(Reactome)

(UniProt)

IKBKB

O14920

Cytosolic sensors of pathogen-associated DNA

ZBP1 mediated induction of type I Interferons

Serine kinase that plays an essential role in the NF-kappa-B signaling pathway which is activated by multiple stimuli such as inflammatory cytokines, bacterial or viral products, DNA damages or other cellular stresses. It is involved in the transcriptional regulation of genes involved in immune response, growth control, or protection against apoptosis. May prevent the overproduction of inflammatory mediators since they exert a negative regulation on canonical IKKs.

  

Adaptative immune response

TCR signaling

 
  

Signaling by interleukins

IL-1 signaling

 
  

Toll-Like receptors cascades

TLR2, TLR3, TLR5, TLR6, TLR7, TLR8, TLR9, TLR10

 

POLR3E

Q9NVU0

Cytosolic sensors of pathogen-associated DNA

Transcription of microbial dsDNA to dsRNA

Plays a key role in sensing and limiting infection by intracellular bacteria and DNA viruses. Acts as nuclear and cytosolic DNA sensor involved in innate immune response. Can sense non-self dsDNA that serves as template for transcription into dsRNA. The non-self RNA polymerase III transcripts, such as Epstein-Barr virus-encoded RNAs (EBERs) induce type I interferon and NF- Kappa-B through the RIG-I pathway.

STAT5B

P51692

Signaling by interleukins

Signaling of IL-2, IL-3, IL-5, IL-7 and GMCSF

Carries out a dual function: signal transduction and activation of transcription. Mediates cellular responses to the cytokine KITLG/SCF and other growth factors. Binds to the GAS element and activates PRL-induced transcription.

LGMN

Q99538

Toll-Like receptors cascades

Trafficking and processing of endosomal TLR

It is involved in the processing of proteins for MHC class II antigen presentation in the lysosomal/endosomal system.

The genes for this analysis were selected from the Reactome over-representation pathway analysis.

This study did not include healthy nasal mucosa. We specifically investigated whether S. aureus enterotoxin B might influence the gene DNA methylation pattern in inflamed (nasal polyp) tissue without studying the effects of the diseased status itself. Indeed, validation experiments including a larger number of samples as well as samples from control (healthy) tissue are warrented in light of these preliminary results. Also we could not preclude effects of other staphylococcal superantigens or superantigens from other germs as the nose is a hotspot of micro-organism activity [8]. However, although methylation differences due to other enterotoxins are a distinct possibility, this should not affect the results as both SEB treated and untreated cells originated from the same patients. Only if significant concentrations of other enterotoxins were present in all 3 patients might this confound the results. In conclusion, these preliminary findings suggest DNA methylation as a possible mechanism by which superantigens may regulate immune function in the nasal mucosa.

Notes

Declarations

Acknowledgements

This work was supported by the grant to Dr. Claudina A. Pérez-Novo from the Flemish Research Board (FWO Postdoctoral mandate, Nr.FWO08-PDO-117), a grant to Prof. Claus Bachert from the Flemish Scientific Research Board (FWO Nr. A12/5-HB-KH3 and G.0436.04). Simon Denil is supported by IWT doctoral research grant SB101371. This work was partially performed on the Stevin Supercomputer Infrastructure at Ghent University, funded by Ghent University, the Hercules Foundation and the Flemish Government – department EWI. We would like also to acknowledge Jean-Pierre Renard and Sarah De Keulenaer for performing all the sequencing work.

Authors’ Affiliations

(1)
Upper Airways Research Laboratory, Department of Otorhinolaryngology, Ghent University Hospital
(2)
Department of Otolaryngology, Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University
(3)
Key Laboratory of Otolaryngology, Head and Neck Surgery (Ministry of Education of China), Beijing Institute of Otorhinolaryngology
(4)
Department of Mathematical Modelling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University
(5)
Karolinska Institutet, Division of ENT Diseases, CLINTEC

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Copyright

© Pérez-Novo et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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