Open Access

Characterization of global transcription profile of normal and HPV-immortalized keratinocytes and their response to TNF treatment

  • Lara Termini1, 2Email author,
  • Enrique Boccardo1,
  • Gustavo H Esteves3,
  • Roberto HirataJr3,
  • Waleska K Martins1, 2,
  • Anna Estela L Colo1, 2,
  • E Jordão Neves3,
  • Luisa Lina Villa1 and
  • Luiz FL Reis1, 2
BMC Medical GenomicsBMC series ¿ open, inclusive and trusted20081:29

DOI: 10.1186/1755-8794-1-29

Received: 01 February 2008

Accepted: 27 June 2008

Published: 27 June 2008

Abstract

Background

Persistent infection by high risk HPV types (e.g. HPV-16, -18, -31, and -45) is the main risk factor for development of cervical intraepithelial neoplasia and cervical cancer. Tumor necrosis factor (TNF) is a key mediator of epithelial cell inflammatory response and exerts a potent cytostatic effect on normal or HPV16, but not on HPV18 immortalized keratinocytes. Moreover, several cervical carcinoma-derived cell lines are resistant to TNF anti-proliferative effect suggesting that the acquisition of TNF-resistance may constitute an important step in HPV-mediated carcinogenesis. In the present study, we compared the gene expression profiles of normal and HPV16 or 18 immortalized human keratinocytes before and after treatment with TNF for 3 or 60 hours.

Methods

In this study, we determined the transcriptional changes 3 and 60 hours after TNF treatment of normal, HPV16 and HPV18 immortalized keratinocytes by microarray analysis. The expression pattern of two genes observed by microarray was confirmed by Northern Blot. NF-κB activation was also determined by electrophoretic mobility shift assay (EMSA) using specific oligonucleotides and nuclear protein extracts.

Results

We observed the differential expression of a common set of genes in two TNF-sensitive cell lines that differs from those modulated in TNF-resistant ones. This information was used to define genes whose differential expression could be associated with the differential response to TNF, such as: KLK7 (kallikrein 7), SOD2 (superoxide dismutase 2), 100P (S100 calcium binding protein P), PI3 (protease inhibitor 3, skin-derived), CSTA (cystatin A), RARRES1 (retinoic acid receptor responder 1), and LXN (latexin). The differential expression of the KLK7 and SOD2 transcripts was confirmed by Northern blot. Moreover, we observed that SOD2 expression correlates with the differential NF-κB activation exhibited by TNF-sensitive and TNF-resistant cells.

Conclusion

This is the first in depth analysis of the differential effect of TNF on normal and HPV16 or HPV18 immortalized keratinocytes. Our findings may be useful for the identification of genes involved in TNF resistance acquisition and candidate genes which deregulated expression may be associated with cervical disease establishment and/or progression.

Background

Human papillomaviruses (HPVs) are double-stranded DNA tumor viruses that infect keratinocytes of the anogenital tract epithelium [1]. Persistent infection by high risk HPV types (e.g., HPV-16, -18, -31, and -45) is the main risk factor for the development of cervical intraepithelial neoplasia and cervical cancer [2, 3]. High-risk HPV DNA is detected in more than 90% of cervical carcinomas worldwide [4] and it has been shown that HPV types 16 and 18 can immortalize normal cells in culture, a function that is attributed to E6 and E7 oncogenes [5]. These are the only HPV genes consistently retained and expressed in cervical carcinomas. Besides, their continued expression is required to maintain the malignant phenotype [68]. The proteins encoded by these genes disturb cell proliferation and differentiation by physical and functional interaction with several cellular factors involved in cell cycle regulation [9]. E6 is best known for its ability to bind to p53 and induce its ubiquitin-dependent degradation [10, 11], whereas E7 was initially recognized by its ability to interact with members of the retinoblastoma protein family, namely pRb, p107 and p130 [12] and its capacity of enhancing their degradation [13].

Persistence of HPV infections and development of neoplasia is influenced by local cell-mediated immune response [14]. Tumor necrosis factor-alpha (TNF) is one of the main mediators of skin and mucosa inflammation and has a potent antiproliferative effect on normal primary human keratinocytes (PHKs). This cytokine is a key regulator of diverse inflammatory and immune processes in human epithelia and its expression by keratinocytes is enhanced in response to tissue injury, inflammation, viral infection, and UV radiation [1517]. Furthermore, TNF has been identified as a key mediator for the regression of HPV-induced lesions [1821]. Previous studies from our group had shown that TNF exerts a potent cytostatic effect on normal and HPV16 immortalized keratinocytes. On the other hand, keratinocytes immortalized by HPV18 or SV40, as well as HPV16 or HPV18-positive cervical tumor-derived cell lines continue to proliferate normally in the presence of this cytokine [22, 23]. In addition, it has been observed that continuous HPV18-gene expression in malignant HeLa-fibroblasts hybrids, as well as increased tumorigenicity of HPV16-transformed human keratinocytes is associated with TNF resistance [24, 25]. These observations underscore the importance of TNF-resistance acquisition in HPV mediated pathogenesis and suggest that this event could be an important factor in HPV-associated neoplasia outcome. However, the molecular basis of HPV-mediated TNF resistance has not been elucidated.

The aim of the present study was to characterize and compare the global transcription profile of normal and HPV-immortalized keratinocytes. Furthermore, we sought to analyze their response to TNF in order to identify differences that contribute to explain their divergent response to this cytokine. For this purpose, we used microarray analysis to determine transcriptional changes upon 3 and 60 hours after TNF exposure. The 3 hours treatment would favor the identification of immediate early TNF regulated genes. On the other hand, the 60 hours treatment was used because the cytostatic effect exerted by this cytokine on normal and HPV16-immortalized keratinocytes reaches its maximum at this time-point [22, 23]. Our experimental setting allowed us to: 1) identify genes that are differentially expressed between TNF-sensitive and TNF-resistant cells; 2) identify genes that are differentially modulated by TNF at two-time points (3 and 60 hours); 3) analyze the effect of HPV-induced immortalization on TNF-regulated genes and, 4) find genes that are differentially expressed between cells immortalized by two different high-risk HPV types. Using this approach, we identified differentially expressed genes that are involved in different cell processes such as immune and inflammatory responses, cell differentiation, cell death, proliferation, extracellular matrix remodeling and DNA repair. The implications of these results are discussed.

Methods

Cell Culture and TNF treatment

Cultures of primary human keratinocytes (PHK), recovered from newborn foreskin (Cambrex, Walkersville, MD, USA), were maintained in keratinocyte serum-free medium – KSFM (Life Technology, Inc., Gaithersburg, MD, USA) for 3 to seven passages [26]. HF698 and HF18Nco are cell lines obtained from human keratinocytes immortalized by HPV16 and HPV18 whole genome, respectively. These cell lines (from now on referred as HPV16 and HPV18, respectively) were kindly provided by R. Schlegel, Georgetown University Medical Center, Washington, DC [27], were grown in 3+1 medium, consisting of a mixture of 3 parts KSFM and 1 part DMEM, supplemented with 10% fetal calf serum. Cells were grown in 100-mm tissue culture dishes to 30% confluence and treated with 2 nM of human recombinant TNF (Boehringer Mannheim, Germany), for 3 or 60 hours. Cells were then trypsinized, washed 3 times with PBS and frozen until RNA extraction. For all time points, RNA was obtained from two independent experiments, including the control plates.

RNA extraction, amplification, labeling, and hybridizations

For each sample, total RNA was extracted using TRIzol Reagent (Life Technologies, Inc., Grand Island, NY, USA) following the procedure recommended by the manufacturer. Three micrograms of target and reference (a pool of RNA from all control conditions) total RNA were linearly amplified using T7-based protocol, converted to cyanine-modified cDNA, and labeled as described previously [28].

Hybridizations were performed in duplicate, using dye-swap, on a cDNA platform of ORESTES representing 4,600 unique genes with known full-length sequence selected from the clone collection derived from the Human Cancer Genome Project [29]. cDNA amplification, purification, identity verification and printing were performed as previously described [28]. A detailed description of the cDNA microarray platform used and the raw data of this study are available at the GEO website under the accession numbers GPL1930 and GSE4524, respectively [30]. Slides were scanned on a confocal laser scanner (Arrayexpress; Packard Bioscience, USA) and, for each spot, signal and background intensities were measured using histogram method of Quantarray software (version 3.0, Packard BioScience, BioChip Technologies LLC, USA).

Statistical Analysis

Data analysis was performed with R project for statistical computing [31] and tools of the associated project, Bioconductor [32]. Prior to analysis, signal intensity was corrected by background subtraction, and data normalized by loess method, using span = 0.4 and degree = 2. For the identification of differentially expressed genes, we used ANOVA model when just one variable was considered. For the identification of differentially expressed genes in a pair-wise manner, we used t-test and determined the nominal p-value for each individual gene. Those nominal p-values can be conservatively adjusted for multiple testing with the Bonferroni correction by multiplying them by the number of genes in our chip. For clustering samples on the basis of their expression profile, we applied hierarchical clustering based on correlation distance and complete linkage.

Northern Blotting

For Northern blot analysis, 15 μg of total RNA was fractionated through a 1% denaturing agarose gel and transferred by capillarity onto Hybond N filters (GE Healthcare BioSciences, NJ, USA). Prehybridization, hybridization, and washes were performed as described by Church and Gilbert [33]. The KLK7 and SOD2 cDNA probes were the same used for immobilization in the array. The human GAPDH cDNA probe was used as control for ensuring equal RNA loading. Probes were labeled by random priming, using Ready-To-Go Labeling Beads (GE Healthcare Bio-Sciences, NJ, USA) and [α-32P]dCTP (3000 Ci/mmol). Nylon filters were exposed to Kodak Hyperfilm (GE Healthcare BioSciences, NJ, USA) with intensifying screen.

Electrophoretic mobility shift assay (EMSA)

Nuclear extracts were obtained from monolayer cultures of PHK, and from cell lines HPV16 and HPV18 treated with 2 nM of human recombinant TNF for 1 h. Briefly, cell plates were washed with ice-cold PBS and cells were scraped in 5 ml of PBS. Cells were transferred to a 15 ml Falcon tube and centrifuged at 3000 rpm for 3 min. Cell pellets were ressuspended in 4 ml of lysis buffer (10 mM HEPES pH 7,9, 10 mM KCl, 0,2 mM EDTA, 1 mM DTT), incubated on ice for 5 min, centrifuged and ressuspended in 4 ml of lysis buffer. Nuclei obtained were centrifuged at 2000 rpm for 2 min, ressuspended in 100 μl of extraction buffer (20 mM HEPES pH7,9, 0,42 M NaCl, 2 mM EDTA, 1 mM DTT, 1 mM PMSF, 2 μM pepstatin, 0,6 μM leupeptin, 25 mU/ml aprotinin) and incubated on ice for 30 min. Finally, the samples were centrifuged at 12000 rpm for 15 min at 4°C. The supernatants were stored at -80°C. The protein concentration was determined by the Bradford method (Bio-Rad, CA, USA).

For gel retardation the following double-stranded oligonucleotide, corresponding to the NF-κB binding sequence, was used: forward-5'-GCCTGGGAAAGTCCCCTCAACT-3' (Invitrogen, CA, USA) was used. The annealed oligonucleotide was labeled with [γ-32]ATP (Amersham, Buckinghamshire, UK; 3,000 Ci/mmol) using TK polynucleotide kinase according to the manufacturer instructions (Biolabs, MA, USA) and purified using Sephadex G50 columns followed by phenol:chloroform extraction and precipitation using 10 μg of salmon sperm DNA as a carrier (Invitrogen, CA, USA). DNA pellets were ressuspended in binding buffer (20 mM HEPES pH 7,9, 20% glycerol, 0,1 M KCl, 2 mM EDTA, 1 mM PMSF, 2 μM pepstatin, 0,6 μM leupeptin, 25 mU/ml aprotinin) to a final concentration of 2,5 fmol/μl. The incorporated radioactivity was quantitated using a LS6500 scintillation counter (Beckman Coulter, CA, USA).

The binding of NF-κB was performed in a reaction containing 5 μg of protein extract, 5 μg of BSA, 5 μg of salmon sperm DNA and binding buffer to a final volume of 32 μl on ice. After 10 min, 8 μl of the [γ-32]ATP 5'-end-labeled double-stranded oligonucleotide probe was added, and the incubation was continued for an additional 15 min at 30°C. The DNA-protein complexes were resolved on 4% nondenaturing polyacrylamide gels (29:1 cross-linking ratio), dried, and exposed overnight to X-ray films (Amersham, Buckinghamshire, UK).

Results

Glass arrays containing 4.800 cDNA sequences were used in order to determine the effects of HPV infection in human keratinocytes as well as the impact of TNF treatment on global gene expression, in HPV negative or positive cells.

In order to identify differentially expressed genes as a function of a unique variable (cell type or TNF-treatment) our dataset was first analyzed by one way ANOVA. The comparisons performed allowed us to determine 1) genes that are differentially expressed between TNF-sensitive and TNF-resistant cells; 2) identify genes that are differentially modulated by TNF at two-time points (3 and 60 hours); 3) analyze the effect of HPV-induced immortalization on TNF-regulated genes and, 4) find genes that are differentially expressed between cells immortalized by two different high-risk HPV types (Figure 1).
https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-1-29/MediaObjects/12920_2008_Article_29_Fig1_HTML.jpg
Figure 1

Experimental setup for the analysis of HPV and TNF effects on keratinocytes gene expression. In order to characterize and compare the global transcription profile of normal and HPV-immortalized keratinocytes and to analyze their response to TNF we used an experimental setting that allowed us to: 1) identify differential expressed genes between normal PHK, HPV16 and HPV18-immortalized keratinocytes (comparisons represented by dashed arrows); 2) identify genes modulated by TNF upon treatment for three and sixty hours (comparisons represented by solid arrows) and; 3) compare the effect of TNF between normal PHK and cells immortalized by two different high-risk HPV types (comparisons represented by round dot arrows).

Differentially expressed genes as a function of cell type or TNF treatment

The identification of differentially expressed genes, with statistical significance, as a function of cell type was performed by ANOVA. Samples and differentially expressed genes (cutoff p-value <10-10) were grouped hierarchically, using correlation distance and complete linkage (Figure 2). As it can be observed, normal (PHK) and HPV16-immortalized keratinocytes (HPV16), which are sensitive to TNF cytostatic effect, grouped together while TNF-resistant HPV18-immortalized cell line (HPV18) formed an independent branch. This indicates that TNF-sensitive cell lines share a group of genes which are regulated in a way that clearly differentiate them from the TNF-resistant one. Samples were further clusterized by the time in culture after the last medium change (3 or 60 hs) and finally separated as a function of TNF treatment. This clusterization pattern may reflect differences in cell density and other cultures variables such as nutrients availability or medium conditioning. Initially, all treatments were performed using 30% cell density cultures. As expected, due to TNF cytostatic effect on normal and HPV16-immortalized keratinocytes, cell density at the end of the 60 hours period was different between treated (40–50%) and control cells (70–80%) for these cell lines. On the other hand, both cytokine treated and control HPV18-immortalized cells reached 80–90% cell density by the end of the 60 hours period. Flow-cytometry analysis revealed that the TNF effect on sensitive cells was characterized by the accumulation of cells in the G1-phase of the cell cycle. Conversely, TNF-induced G1-arrest was not observed in HPV18-immortalized keratinocytes [[22, 23] and data not shown]. Finally, no differences in cell density were observed for cultures corresponding to 3 hours-treatment group.
https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-1-29/MediaObjects/12920_2008_Article_29_Fig2_HTML.jpg
Figure 2

Hierarchical grouping based on differentially expressed genes as a function of cell type. These genes where identified by the ANOVA method and the samples where grouped considering the correlation distance and complete linkage. After sample grouping the genes (p values <10-10) were hierarchically grouped by their correlation distances. High gene expression is shown in red, low gene expression is shown in green and black indicates non-differential gene expression. Samples: Primary human keratinocytes: controls and treated for 3 or 60 hours with TNF, respectively (PHK_3H, PHK_60H, PHK_3H.TNF, PHK_60H.TNF); HPV16-immortalized keratinocytes: controls and treated for 3 or 60 hours with TNF, respectively (HPV16_3H, HPV16_60H, HPV16_3H.TNF, HPV16_60H.TNF); HPV18-immortalized keratinocytes: controls and treated with 3 or 60 hours for TNF, respectively (HPV18_3H, HPV18_60H, HPV18_3H.TNF, HPV18_60H.TNF).

Among the differentially regulated genes we found some related with inflammatory response (SOD2, TGFB1, CD44, INHBA, OAS1, SIMP), epidermal development, differentiation and proliferation (ADAMST1, RARRES1, CREG, HBP17, MCM2, PRSS1, S100P, CREG1), proteolysis regulation (KLK7, PI3, LXN), and cell adhesion (CD44, PARVA, PROS1). The name and function of the genes described are listed in Table 1. We next determined the global changes in gene expression as a function of TNF treatment. The name and annotated function of the identified genes that best distinguish samples based on TNF treatment (cutoff p-value <10-2,9) are listed in Table 2. As expected, many of these genes are involved in the inflammatory response and/or are direct targets of TNF e.g. CCL20, CD44, HLA-F, IL1F9, NFKBIA, INHBA, SOD2, MARCKS, RFX5. Samples and genes were hierarchically clusterized on the basis of their correlation distance using complete linkage (Figure 3). Samples from TNF-treated normal keratinocytes (PHK) grouped together and apart from the others. HPV-positive samples exhibited a complex clusterization pattern suggesting that the presence of either HPV16 or 18 has an impact on TNF-regulated gene expression. Furthermore, the grouping of treated PHKs apart from the other samples could reflect the fact that PHKs are the only normal cells used in this study and, as such, the only cell type expected to have an unaltered TNF-signaling network. This could contribute to explain the differences in gene expression upon TNF treatment observed between normal and HPV-immortalized keratinocytes.
Table 1

Name and function of the differentially expressed genes that best distinguish samples by cell type variable

GENE

UniGene ID

GENE NAME

FUNCTION

ADAMTS1

Hs.534115

disintegrin-like and metalloprotease (reprolysin type)

negative regulation of cell proliferation

ARHGEF10

Hs.443460

Rho guanine nucleotide exchange factor (GEF) 10

GTPase activator activity

CD44

Hs.502328

CD44 antigen

cell adhesion

CITED1

Hs.40403

Cbp/p300-interacting transactivator

transcription regulator activity

CREG

Hs.5710

cellular repressor of E1A-stimulated genes 1

cell proliferation

CSTA

Hs.518198

cystatin A (stefin A)

cysteine protease inhibitor activity

D4S234E

Hs.518595

DNA segment on chromosome 4 (unique) 234 expressed sequence

dopamine receptor signaling pathway

DHRS3

Hs.289347

dehydrogenase/reductase (SDR family) member 3

fatty acid metabolism

EPB41L1

Hs.437422

erythrocyte membrane protein band 4.1-like 1

structural molecule activity

FLJ20105

Hs.47558

FLJ20105

regulation of transcription

FLJ21511

Hs.479703

FLJ21511

unknown function

FLJ21616

Hs.591836

FLJ21616

regulation of transcription

FLJ30525

Hs.7962

FLJ30525

unknown function

GALNT11

Hs.647109

UDP-N-acetyl-alpha-D-galactosamine

transferase activity, transferring glycosyl groups

GC20

Hs.315230

translation factor sui1 homolog

regulation of translational initiation

GLDC

Hs.584238

glycine dehydrogenase

glycine metabolism

GSR

Hs.271510

glutathione reductase

glutathion metabolism

HBP17

Hs.1690

fibroblast growth factor binding protein 1

regulation of cell proliferation

INHBA

Hs.28792

inhibin, beta A

cell cycle arrest, negative regulation of immune cell differentiation

JPH3

Hs.592068

junctophilin 3

unknown function

KIAA0368

Hs.368255

KIAA0368

ER-associated protein catabolism

KLK7

Hs.151254

kallikrein 7 (chymotryptic, stratum corneum)

epidermis development, proteolysis and peptidolysis, chymotrypsin activity

LCN2

Hs.204238

lipocalin 2 (oncogene 24p3)

transporter activity

LXN

Hs.478067

latexin

enzyme inhibitor activity

MAL2

Hs.201083

T-cell differentiation protein 2

Unknown function

MCM2

Hs.477481

minichromosome maintenance deficient 2

cell cycle

MGC45400

Hs.389734

transcription elongation factor A (SII)-like 8

translation elongation factor activity

MGEA5

Hs.500842

meningioma expressed antigen 5 (hyaluronidase)

glycoprotein catabolism

MYO5B

Hs.200136

acetyl-Coenzyme A acyltransferase 2

fatty acid metabolism

NMES1

Hs.112242

normal mucosa of esophagus specific 1

unknown function

NMU

Hs.418367

neuromedin U

neuropeptide signaling pathway, digestion

NT5E

Hs.153952

5'-nucleotidase, ecto (CD73)

DNA metabolism

OAS1

Hs.524760

2',5'-oligoadenylate synthetase 1

immune response to viral infections

ODC1

Hs.467701

ornithine decarboxylase 1

polyamine biosynthesis

PARVA

Hs.607144

parvin, alpha

cell adhesion, actin binding

PEX3

Hs.7277

peroxisomal biogenesis factor 3

peroxisome organization

PI3

Hs.112341

protease inhibitor 3, skin-derived (SKALP)

elastase-specific inhibitor

PLAU

Hs.77274

plasminogen activator

chemotaxis

PPGB

Hs.517076

protective protein for beta-galactosidase

intracellular protein transport

PROS1

Hs.64016

protein S (alpha)

cell adhesion, endopeptidase inhibitor activity

PRSS11

Hs.501280

protease, serine, 11 (IGF binding)

insulin-like growth facto binding, regulation of cell growth

RARRES1

Hs.131269

retinoic acid receptor responder

negative regulation of cell proliferation

RDH-E2

Hs.170673

epidermal retinal dehydrogenase 2

oxidoreductase activity

RPL15

Hs.381219

ribosomal protein L15

protein biosynthesis

RUTBC3

Hs.474914

RUN and TBC1 domain containing 3

unknown function

S100P

Hs.440880

S100 calcium binding protein P

cell cycle progression and differentiation

SEPT10

Hs.469615

septin 10

cell cycle

SF3B4

Hs.516160

myotubularin related protein 11

inositol or phosphatidylinositol phosphatase activity

SIMP

Hs.475812

immunodominant MHC-associated peptides

protein amino acid glycosylation

SMOC2

Hs.487200

SPARC related modular calcium binding

calcium ion binding

SOD2

Hs.487046

superoxide dismutase 2

age-dependent response to reactive oxygen species, cellular defense response

STAF65 (gamma)

Hs.6232

SPTF-associated factor 65 gamma

regulation of transcription, DNA- dependent

SYTL3

Hs.436977

synaptotagmin-like 3

intracellular protein transport

TFRC

Hs.529618

transferrin receptor (p90, CD71)

endocytosis

TGFB1

Hs.645227

transforming growth factor, beta 1

cell proloferation

TPD52

Hs.368433

tumor protein D52

morphogenesis

TPX2

Hs.244580

microtubule-associated protein homolog

cell proliferation

TRIM31

Hs.493275

tripartite motif-containing 31

protein ubiquitination, ubiquitin ligase activity

YME1L1

Hs.499145

YME1-like 1 (S. cerevisiae)

protein catabolism

ZNF198

Hs.644041

zinc finger protein 198

regulation of transcription, DNA- dependent

*Genes are listed in alphabetical order. The cutoff p-value was set as <10-10.

Table 2

Name and function of the differentially expressed genes that best distinguish samples by TNF treatment variable

GENE

UniGene ID

GENE NAME

FUNCTION

ADORA2b

Hs.167046

adenosine A2b receptor

activation of MAPK

AKAP1

Hs.463506

A kinase (PRKA) anchor protein 1

RNA binding

BTG2

Hs.519162

BTG family, member 2

negative regulation of cell proliferation

C3

Hs.529053

complement component 3

inflammatory response

CCL20

Hs.75498

chemokine (C-C motif) ligand 20

inflammatory response

CD44

Hs.502328

CD44 antigen

cell adhesion

cig5

Hs.17518

radical S-adenosyl methionine domain containing 2

Catalytic activity

CLCA4

Hs.546343

chloride channel, calcium activated, family member 4

chloride transport

DC-UbP

Hs.179852

dendritic cell-derived ubiquitin-like protein

Protein modification

FAD104

Hs.159430

fibronectin type III domain containing 3B

cell differentiation

FLJ21511

Hs.479703

FLJ21511

Unknown function

FMNL3

Hs.179838

formin-like 3

cell organization and biogenesis

GFPT2

Hs.30332

glutamine-fructose-6-phosphate transaminase 2

carbohydrate biosynthesis

HLA-F

Hs.519972

major histocompatibility complex, class I, F

antigen presentation, endogenous antigen

IL1F9

Hs.211238

interleukin 1 family, member 9

inflammatory response

INHBA

Hs.28792

inhibin, beta A

cell cycle arrest, negative regulation of immune cell differentiation

KIAA0303

Hs.133539

microtubule associated serine/threonine kinase family member 4

protein kinase activity

KIAA1279

Hs.279580

KIAA1279

Unknown function

LAP3

Hs.479264

leucine aminopeptidase 3

Protein metabolism

MARCKS

Hs.75061

MARCKS-like 1

calmodulin binding, macrophage activation

MGAT4B

Hs.437277

mannosyl (alpha-1,3-)-glycoprotein beta-1,4-N-acetylglucosaminyltransferase, isoenzyme B

cytokine activity

MGC45400

Hs.389734

transcription elongation factor A (SII)-like 8

translation elongation factor activity

MMP9

Hs.297413

matrix metalloproteinase 9

proteolysis and peptidolysis

NFKBIA

Hs.81328

nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha

cytoplasmic sequestering of NF-kappaB

NMES1

Hs.112242

normal mucosa of esophagus specific 1

Unknown function

OAS1

Hs.524760

2',5'-oligoadenylate synthetase 1

immune response to viral infections

PLAU

Hs.77274

plasminogen activator

chemotaxis

RDH-E2

Hs.170673

epidermal retinal dehydrogenase 2

oxidoreductase activity

RFX5

Hs.166891

regulatory factor X, 5

inflammatory response, HLA class II expression

RIG-1

Hs.17466

retinoic acid receptor responder (tazarotene induced) 3

negative regulation of cell proliferation

RIPK2

Hs.103755

receptor-interacting serine-threonine kinase 2

inflammatory response

SASH1

Hs.193133

SAM and SH3 domain containing 1

Negative regulation of cell cycle

SDCBP

Hs.200804

syndecan binding protein (syntenin)

intracellular signaling cascade, interleukin-5 receptor binding

SEC24A

Hs.211612

SEC24 related gene family, member A

intracellular protein transport

SERPINB2

Hs.514913

encoding serine (or cysteine) proteinase inhibitor, clade B (ovalbumin), member 2

anti-apoptosis

SF3B4

Hs.412818

myotubularin related protein 11

RNA splicing

SOD2

Hs.487046

superoxide dismutase 2

age-dependent response to reactive oxygen species, cellular defense response

TMSB4

Hs.522584

thymosin, beta 4, X-linked

cytoskeleton organization and biogenesis

VMP1

Hs.444569

transmembrane protein 49

Unknown function

*Genes are listed in alphabetical order. The cutoff p-value was set as <10-2,9.

https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-1-29/MediaObjects/12920_2008_Article_29_Fig3_HTML.jpg
Figure 3

Hierarchical grouping based on differentially expressed genes as a function of TNF treatment. These genes where identified by the ANOVA method and the samples where grouped considering the correlation distance and complete linkage. After sample grouping the genes (p values <10-2,9) were hierarchically grouped by their correlation distances. High gene expression is shown in red, low gene expression is shown in green and black indicates non-differential gene expression. Samples: Primary human keratinocytes: controls and treated for 3 or 60 hours with TNF, respectively (PHK_3H, PHK_60H, PHK_3H.TNF, PHK_60H.TNF); HPV16-immortalized keratinocytes: controls and treated for 3 or 60 hours with TNF, respectively (HPV16_3H, HPV16_60H, HPV16_3H.TNF, HPV16_60H.TNF); HPV18-immortalized keratinocytes: controls and treated for 3 or 60 hours with TNF, respectively (HPV18_3H, HPV18_60H, HPV18_3H.TNF, HPV18_60H.TNF).

Since our experimental setting included the comparative analysis of global gene expression at two time points (3 or 60 hours), we searched for genes that best differentiate our samples as a function of time. We found 48 genes that clearly differentiate samples from the analyzed time points. The name and annotated function of the identified genes (cutoff p-value <10-9) are listed in additional file 1. Hierarchical clusterization divided samples in two main branches (additional file 2). Each branch was exclusively composed of samples from the same time point, namely, 3 or 60 hours. Samples from the 3 hours-time point formed a secondary branch that divided normal from HPV-immortalized keratinocytes. On the other hand, samples from the 60 hours-time point formed a secondary branch that divided normal and HPV16-immortalized keratinocytes (TNF-sensitive samples) from HPV18-immortalized keratinocytes (TNF-resistant samples).

Differentially expressed genes between TNF-sensitive and TNF-resistant cells

In order to identify differentially expressed genes between specific samples we performed a series of pair-wise comparisons. For each pair-wise comparison, we generated a list of differentially expressed genes with p-value lower than 0,01. The complete list of all pair-wise comparisons performed is presented in additional file 3. We next aimed to characterize genes that were differentially expressed between TNF-sensitive (PHK and HPV16) and TNF-resistant cells (HPV18). To achieve this goal we selected the thirty genes with the lowest p-values that best distinguish both PHK and HPV16 from HPV18 and the thirty genes with lowest p-values that best distinguish both PHK+TNF and HPV16+TNF from HPV18+TNF (considering treatment with TNF for 3 h). Twelve genes were common to both lists giving a total of 48 different genes identified (Table 3). Using the expression profile of these 48 genes, samples were grouped hierarchically, based on their correlation distance and complete linkage (Figure 4).
Table 3

List of differentially expressed genes that best distinguish TNF-resistant cells (HPV18) from TNF-sensitive cells (PHK and HPV16), in normal culture conditions or upon treatment with TNF for 3 hours

  

(PHK and HPV16) vsHPV18

(PHK_TNF and HPV16_TNF) vsHPV18_TNF

GENE

GENE NAME

FOLD

p VALUE

FOLD

p VALUE

ABCE1

ATP-binding cassette, sub-family E (OABP), member 1

0.592

0.001384

----

----

ACBD5

acyl-Coenzyme A binding domain containing 5

----

----

0.378

0.00086

ALDH3A2

encoding aldehyde dehydrogenase 3 family, member A2

1.623

0.00122

----

----

APG12L

APG12 autophagy 12-like (S. cerevisiae)

1.739

0.000924

----

----

APPBP1

amyloid beta precursor protein binding protein 1

0.540

0.000283

----

----

ARF4L

ADP-ribosylation factor 4-like

----

----

0.578

5.70E-05

BCLAF1

BCL2-associated transcription factor 1

0.611

0.000876

----

----

BOC

brother of CDO

----

----

2.337

6.00E-06

CCNA2

cyclin A2

0.577

0.000604

----

----

CDCA2

cell division cycle associated 2

----

----

0.539

7.20E-05

CDK2AP1

CDK2-associated protein 1

----

----

1.523

0.000148

CPSF3

cleavage and polyadenylation specific factor 3

0.586

0.000466

----

----

CYP1B1

cytochrome P450, family 1, subfamily B, polypeptide 1

0.499

0.001386

----

----

DEK

DEK oncogene

0.480

0.000278

----

----

FAM31C

family with sequence similarity 31, member C

----

----

2.548

0.000663

FLJ20105

hypothetical protein LOC54821

0.026

5.00E-06

0.029

2.00E-06

GALNAC4S-6ST

B cell RAG associated protein

1.827

0.000145

1.673

0.000215

H105E3

encoding NAD(P) dependent steroid dehydrogenase-like

----

----

0.569

6.80E-05

HLCS

holocarboxylase synthetase

----

----

1.551

0.00011

JPH3

junctophilin 3

0.154

0.000192

----

----

KIAA0795

kelch-like 18 (Drosophila)

0.857

0.001325

----

----

KIAA1023

IQ motif containing E

1.575

0.000723

----

----

KIF1B

kinesin family member 1B

----

----

0.570

0.000632

KLK7

encoding kallikrein 7 (chymotryptic, stratum corneum)

0.421

0.000416

0.374

2.30E-05

LCN2

lipocalin 2 (oncogene 24p3)

----

----

0.216

0.000686

LOC151242

protein phosphatase 1, regulatory (inhibitor)

1.928

0.000268

----

----

Lrp2bp

low density lipoprotein receptor-related protein binding protein

0.629

0.000574

----

----

MAPRE1

encoding microtubule-associated protein, RP/EB family, member 1

0.410

0.001068

0.503

4.20E-05

MBD2

methyl-CpG binding domain protein 2

0.680

0.001247

----

----

MGC35048

hypothetical protein MGC35048

----

----

0.499

0.000211

MRPS6

mitochondrial ribosomal protein S6

----

----

1.460

0.000161

MYO5B

acetyl-Coenzyme A acyltransferase 2

0.353

0.000104

0.292

4.10E-05

NMES1

normal mucosa of esophagus specific 1

0.324

0.000294

0.244

7.00E-06

NPR2

encoding natriuretic peptide receptor B/guanylate cyclase B

----

----

0.435

0.000234

ODC1

ornithine decarboxylase 1

1.660

0.000886

----

----

PI3

protease inhibitor 3, skin-derived (SKALP)

0.213

0.000274

0.207

1.80E-05

PROS1

protein S (alpha)

1.807

0.000633

----

----

PTP4A1

protein tyrosine phosphatase type IVA, member 1

0.478

0.000385

0.497

1.80E-05

RRAGA

Ras-related GTP binding A

----

----

1.510

0.000301

RUTBC3

RUN and TBC1 domain 3

0.518

0.000704

0.450

0.000131

S100P

S100 calcium binding protein P

0.101

0

0.102

0

SDCBP

syndecan binding protein (syntenin)

0.456

0.000395

----

----

SFRP1

secreted frizzled-related protein 1

----

----

0.502

5.10E-05

SLC35B3

solute carrier family 35, member B3

----

----

1.990

0.000304

STAF65 (gamma)

SPTF-associated factor 65 gamma

0.021

1.00E-06

0.028

0

THBS1

thrombospondin 1

----

----

2.712

0.000225

VMP1

likely ortholog of rat vacuole membrane protein 1

----

----

1.547

0.000899

YME1L1

YME1-like 1 (S. cerevisiae)

0.373

2.60E-05

0.333

5.00E-06

*Genes are listed in alphabetical order. Underlined genes were identified as differentially expressed between TNF sensitive and TNF resistant cells in both culture conditions.

https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-1-29/MediaObjects/12920_2008_Article_29_Fig4_HTML.jpg
Figure 4

Supervised hierarchical grouping based on differentially expressed genes between normal/HPV16-immortalized keratinocytes and HPV 18-immortalized ones after treatment with TNF for 3 hours. High gene expression is shown in red, low gene expression is shown in green and black indicates non-differential gene expression. Samples: Primary human keratinocytes: controls and treated for 3 hours with TNF, respectively (PHK_3H, PHK_3H.TNF); HPV16-immortalized keratinocytes: controls and treated for 3 hours with TNF, respectively (HPV16_3H, HPV16_3H.TNF); HPV18-immortalized keratinocytes: controls and treated for 3 hours with TNF, respectively (HPV18_3H, HPV18_3H.TNF).

Using this approach we observed that genes involved with cell cycle control (CCNA2, CDCA2, CDK2AP1), epidermis development, differentiation and proliferation (KLK7, ALDH3A2, PI3, APG12L, BCLAF1, DEK, MAPRE1, S100P, RRAGA, SFRP1), protein ubiquitination (APPBP1) and cell adhesion (BOC, PROS1, SDCBP, THBS1, JPH3), among others, were differentially expressed between TNF-sensitive and TNF-resistant cells (Table 3). These analyses were also performed considering TNF treatment for 60h (available as additional files 4 and 5).

Validation on KLK7 and SOD2 as differentially expressed genes

We identified a group of genes whose differential expression could be associated with the differential response to TNF of the cell lines studied, namely: KLK7 (kallikrein 7), SOD2 (superoxide dismutase 2), S100P (S100 calcium binding protein P), PI3 (protease inhibitor 3, skin-derived), CSTA (cystatin A), RARRES1 (retinoic acid receptor responder 1), and LXN (latexin). Based on the reported function as well as the expression profile observed, KLK7 and SOD2 genes were selected for further analysis. The expression pattern of these genes observed by microarray was confirmed by Northern Blot in control and TNF-treated (60 hours) samples from all cell lines used (Figures 5A and 5B). As it can be observed, KLK7 is equally expressed in TNF-treated or untreated HPV18-immortalized cells but is not detected in PHK or HPV16-immortalized cells, even after cytokine treatment. On the other hand, we observed that SOD2 expression is up-regulated by TNF in both PHK and HPV16-immortalized cells but not in HPV18-immortalized cells, confirming the data obtained by microarray (Figures 5A and 5B).
https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-1-29/MediaObjects/12920_2008_Article_29_Fig5_HTML.jpg
Figure 5

Differential expression of KLK7 and SOD2 transcripts. A. Detail of the supervised hierarchical grouping based on differentially expressed genes between normal/HPV16-immortalized keratinocytes and HPV 18-immortalized ones, after treatment with TNF for 60 hours. B. Northern blot analysis of KLK7 and SOD2 transcription levels. Arrows indicate the two alternative splicing products of KLK7 in HPV18-immortalized keratinocytes (GenBank # NM_005046); the SOD2 transcript is induced by TNF in both PHK and HPV16-immortalized cells but not in HPV18-immortalized cells (GenBank # NM_00636). A probe against GAPDH was used to monitor comparable loading between samples.

NF-κB is differentially activated in HPV-16- and HPV-18-infected cells

It has been reported that NF-κB activation plays an important role in SOD2 induction by TNF. So we hypothesized that the differential expression of SOD2 could be due to the presence of different levels of activated NF-κB after TNF treatment between TNF-sensitive and TNF resistant cells. In order to address this hypothesis NF-κB activation was determined by electrophoretic mobility shift assay (EMSA) using specific oligonucleotides and nuclear protein extracts. Interestingly, we observed that normal as well as HPV16-immortalized keratinocytes exhibited a clear activation of NF-κB as shown by the increase of this factor levels in nuclear protein extracts after TNF treatment (Figure 6). On the other hand, NF-κB activation in TNF-resistant HPV18-immortalized cells was below the level of detection (Figure 6, lanes 9 and 10). This prompted us to analyze if NF-κB activation was also altered in other HPV-positive cell lines previously reported to be resistant to TNF cytostatic effect [[22, 23], and data not shown]. To address this issue we performed EMSA using nuclear protein extracts obtained from HPV16-positive (SiHa) or HPV18-positive (HeLa and SW756) cervical cancer derived cell lines cultures. We observed that TNF-resistant cells exhibited reduced NF-κB activation when compared to normal PHK (additional file 6). Altogether, these observations suggest that alteration of TNF-signaling pathway leading to NF-κB activation is a common event in HPV-positive cell lines resistant to this cytokine.
https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-1-29/MediaObjects/12920_2008_Article_29_Fig6_HTML.jpg
Figure 6

Analysis of TNF-induced NF-κB activation in normal and HPV16 or 18-immortalized keratinocytes. Subconfluent cultures of normal and HPV16 or 18-immortalized keratinocytes treated with 2 nM of TNF for 1 h were used to obtain nuclear protein extracts. For each EMSA reaction, 5 μg of nuclear protein were incubated with 50 fmol of [γ-32P]ATP-labeled double-stranded oligonucleotide and a 50X excess of unlabeled oligonucleotide (lanes 3 and 7). Specificity of binding was further demonstrated by incubation of 1 μg of nuclear protein with the described amount of labeled consensus oligonucleotide and a 50X excess of a labeled oligonucleotide carrying a single-base mutation at the NF-κB binding site (lane 4), and incubation of nuclear extract in the absence of any labeled probe (lane 8). NF-κB DNA binding reactions were carried out as described under "Material and Methods". DNA binding complexes are indicated.

Discussion

Production and secretion of inflammatory cytokines are among the main events that take place upon viral infection. These molecules coordinate host cell-mediated immune response by recruiting cellular elements from the immune system and by regulating gene expression on target cells [34, 35]. The pleiotropic cytokine TNF is a key regulator of inflammation of the epithelia with a well-documented capacity to induce growth arrest in normal or HPV16-immortalized keratinocytes, mainly in the G0/G1 phase of the cell cycle [36]. Conversely, we have previously reported that HPV18-immortalized and both HPV16 or HPV18-transformed cell lines are resistant to TNF-induced growth arrest [22, 23].

In order to address the yet unknown molecular bases of this difference we applied cDNA microarray technology to compare the global gene expression profiles of TNF-sensitive normal and HPV16-immortalized keratinocytes with that of TNF-resistant HPV18-immortalized ones. Some limitations of this study are the use of a reduced number of samples and the existence of differences in cell culture conditions which are inherent to our experimental setting, i.e. the differences in cell density at the different time-points described above. However, using this approach we identified a group of genes that clearly distinguish both cells groups (Figure 2 and Table 1). This indicates that TNF-sensitive cell lines share a group of genes which are regulated in a way that clearly differentiate them from the TNF-resistant one.

On the other hand, when we analyzed changes in global gene expression as a function of TNF treatment we observed that HPV16 and HPV18 samples could not be distinguished from each other while normal keratinocytes could be readily discriminated (Figure 3). This observation suggests that the presence of either HPV16 or 18 has an impact on TNF-regulated gene expression. In line with these observations, several studies have shown that HPV positive cells exhibit impaired TNF pathways [37, 38]. Moreover, it has been reported that the effects of TNF on HPV-harboring cells depends on variables as cell type studied, the virus type present and culture conditions (i.e., growth factors availability). This cytokine is capable of inducing the proliferation of HPV16-immortalized human cervical epithelial cells cultures in the absence of growth factors through an autocrine, EGF receptor-dependent, pathway [39]. Besides, TNF can upregulate E6/E7 RNA expression and cyclin-dependent kinase activity in these cells [40]. Conversely, it has been reported that TNF exerts a potent cytostatic effect on HPV16-immortalized keratinocytes while HPV18-immortalized as well as cervical carcinoma-derived HPV-positive cell lines remain unaffected [22, 23]. Furthermore, it has been observed that increased tumorigenicity of human keratinocytes transformed by HPV16 is associated with resistance to TNF cytostatic effect [24]. Finally, it was demonstrated that TNF downregulates HPV18 transcription in non-malignant HeLa-fibroblasts hybrids, while viral expression in tumorigenic hybrids segregants as well as in parental HeLa cells remained undisturbed [25]. On the other hand, it has been consistently observed that TNF negatively regulates normal keratinocytes proliferation in monolayer [22, 23, 36] as well as in organotypic cell cultures [41, 42]. Altogether, these data support the notion that acquisition of resistance to TNF by HPV-infected cells may represent an important step towards malignancy.

Despite the existence of similarities between the two high-risk HPV types used to generate the cell lines studied, the fact that HPV16 and HPV18 are different viruses that exhibit clear differences in their biological activities must be highlighted. For instance, epidemiological studies have shown that HPV18 is more associated to cervical adenocarcinomas while HPV16 is more prevalent in squamous cell carcinomas [4345]. Furthermore, compared to other HPV types HPV18 has been associated with increased transforming potential in cell culture systems and with poorer cancer prognosis at the clinical level [26, 27, 46, 47]. On the other hand, HPV16 exhibits a greater potential to establish persistent infections that can progress to high-grade lesions [48, 49]. Although we cannot explain the molecular bases of the differences in gene expression between these cell lines, we believe that this may reflect the divergences that exist between these HPV types.

We next searched for genes that best distinguish between TNF-sensitive and TNF-resistant cells by pair-wise comparison both before and after cytokine treatment for 3 or 60 hours. By this means we identified 48 and 52 different genes, respectively, that set apart TNF-sensitive from TNF resistant cells (Figure 4, Table 3, additional files 4 and 5). The functional characterization of these genes shows that they are involved in critical cellular processes such as regulation of proliferation, differentiation and cell adhesion. Altogether, the differential expression of these genes may contribute to the differential response to the cytostatic effect of TNF observed in these cells.

Two genes, namely KLK7 and SOD2, were selected for further analysis based on their reported function and expression profile (Figure 5A). KLK7 expression pattern was validated by Northern blot and showed that it is equally expressed in TNF-treated or untreated HPV18-immortalized cells but is not detected in PHK or HPV16-immortalized cells (Figure 5B). Kallikreins are a sub-group of serine proteases with different physiological functions. In humans, kallikreins are encoded by 15 structurally similar, steroid hormone-regulated genes that co-localize to chromosome 19q13.4, representing the largest cluster of contiguous protease genes in the entire genome [5052]. These proteins mediate the proteolytic degradation of cohesive intracellular structures associated to epithelial differentiation. Recent data also suggest that kallikreins may be causally involved in carcinogenesis, particularly in tumor metastasis and invasion, and, thus, may represent attractive drug targets to consider for therapeutic intervention [50]. Consistent with our findings, it has been observed that KLK7 expression is up-regulated in cervical tumors as well as in cells lines derived from them. On the other hand, normal keratinocytes express low levels of this protein [53, 54]. Furthermore, KLK7 expression has been found up-regulated in breast [55] and ovary tumors [56] and is being considered a new tumor progression marker.

The superoxide dismutase 2 (SOD2) expression pattern was also validated by Northern blot (Figure 5B). This gene is up-regulated in TNF-sensitive but not in TNF-resistant cells. The superoxide dismutase 2 (SOD2) belongs to a family of enzymes involved in the conversion of superoxide radicals in molecular oxygen. Reactive oxygen metabolites have multifactorial effects on the regulation of cell growth and malignant invasion. Furthermore, numerous in vivo studies have shown that the superoxide dismutases can be highly expressed in aggressive human solid tumors [5759].

Previous reports have shown that activation of the transcription factor NF-κB is essential for the induction of SOD2 by TNF and IL-1β [60, 61]. Here we show that TNF-sensitive cells exhibit higher levels of activated NF-κB than TNF-resistant ones after cytokine treatment (Figure 6 and additional file 6). Several studies have shown that NF-κB is a negative regulator of keratinocytes proliferation in the epidermis, and that it plays an important role in cell differentiation and tissue homeostasis [6264]. In stratified epithelia NF-κB is found in the cytoplasm of proliferating cells from the basal layer while it is detected in the nuclei of non-proliferating cells from the upper layers. Furthermore, it has been observed that NF-κB superexpression is associated with epidermal hypoplasia while its down-regulation promotes hyperplasia [62]. Overall, these data suggest that alterations in TNF-mediated NF-κB activation pathways can play a role in the development and progression of HPV-associated epithelial and mucosal lesions.

Conclusion

Progression of HPV-associated lesions depends on the many alterations caused by this virus in the infected cells. We have identified multiple genes differentially regulated by TNF in HPV16 and HPV18 immortalized keratinocytes. Among them we found KLK7 (kallikrein 7), SOD2 (superoxide dismutase 2), S100P (S100 calcium binding protein P), PI3 (protease inhibitor 3, skin-derived), CSTA (cystatin A), RARRES1 (retinoic acid receptor responder 1), and LXN (latexin). The differential expression of the KLK7 and SOD2 transcripts was further confirmed at the RNA level. Moreover, we present evidence that differential SOD2 expression correlates with the levels of NF-κB activation exhibited by TNF-sensitive and TNF-resistant cells.

This is the first time that the effect of TNF on global gene expression of normal and HPV-immortalized keratinocytes is addressed at two time points. The thorough analysis of the expression pattern of the identified genes may contribute to the understanding of critical differences between transient and chronic events. Furthermore, it may provide insights of the molecular mechanisms of HPV-induced TNF resistance, contribute to the identification of key functions and pathways associated to specific HPV types and, finally, lead to the identification of new cervical tumor progression markers.

Declarations

Acknowledgements

This work was supported in part by Fundação de Amparo à Pesquisa do Estado de São Paulo grant 98/14335-2. L. Termini had a PhD fellowship from Fundação de Amparo à Pesquisa do Estado de São Paulo (grant 01/01006-5).

We would like to thank Dr. Alex Fiorini, Chamberlein Neto, Mariana Santos and Aline Pacífico for technical assistance, Ana Carolina Quirino Simões and Lucas Fahham for the review of the statistical analysis, and Dr. Ana Paula Lepique and members of the Laboratory of Functional Genomics and the Laboratory of Virology of Ludwig Institute for Cancer research for helpful discussions.

Authors’ Affiliations

(1)
Ludwig Institute for Cancer Research
(2)
Hospital do Câncer A. C. Camargo
(3)
Instituto de Matemática e Estatística da Universidade de São Paulo

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  65. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://​www.​biomedcentral.​com/​1755-8794/​1/​29/​prepub

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