Abstract

   Periodontitis is a chronic inflammatory disease affecting the soft
   tissue and bone that surrounds the teeth. Despite extensive research,
   distinctive genes responsible for the disease have not been identified.
   The objective of this study was to elucidate transcriptome changes in
   periodontitis, by investigating gene expression profiles in gingival
   tissue obtained from periodontitis-affected and healthy gingiva from
   the same patient, using RNA-sequencing. Gingival biopsies were obtained
   from a disease-affected and a healthy site from each of 10 individuals
   diagnosed with periodontitis. Enrichment analysis performed among
   uniquely expressed genes for the periodontitis-affected and healthy
   tissues revealed several regulated pathways indicative of inflammation
   for the periodontitis-affected condition. Hierarchical clustering of
   the sequenced biopsies demonstrated clustering according to the degree
   of inflammation, as observed histologically in the biopsies, rather
   than clustering at the individual level. Among the top 50 upregulated
   genes in periodontitis-affected tissues, we investigated two genes
   which have not previously been demonstrated to be involved in
   periodontitis. These included interferon regulatory factor 4 and
   chemokine (C-C motif) ligand 18, which were also expressed at the
   protein level in gingival biopsies from patients with periodontitis. In
   conclusion, this study provides a first step towards a quantitative
   comprehensive insight into the transcriptome changes in periodontitis.
   We demonstrate for the first time site-specific local variation in gene
   expression profiles of periodontitis-affected and healthy tissues
   obtained from patients with periodontitis, using RNA-seq. Further, we
   have identified novel genes expressed in periodontitis tissues, which
   may constitute potential therapeutic targets for future treatment
   strategies of periodontitis.

Introduction

   Periodontitis is a chronic inflammatory disease characterized by the
   destruction of periodontal tissue. This common disease, primarily
   initiated by periodontal pathogens, is an outcome of a complex
   interaction between periodontal microorganisms and the host
   inflammatory response [35][1]. The host response involves
   proinflammatory cytokines, chemokines, prostaglandins, Toll-like
   receptors and proteolytic enzymes, which have all been demonstrated to
   play an important role in the pathogenesis of periodontitis [36][2],
   [37][3].

   Studies have been performed combining in vivo and in vitro approaches
   to identify genes responsible for periodontitis. To date, there are a
   few published microarray studies investigating the gene expression
   profile in periodontits. One microarray study reported no significant
   differences in gene expression at different pathological sites in
   patients with chronic and aggressive periodontitis [38][4], whereas Kim
   et al. [39][5] and Demmer et al. [40][6] showed a number of genes that
   were upregulated in periodontitis compared to healthy controls. In
   addition, Beikler et al. [41][7] demonstrated that in periodontitis
   sites, the expression of immune and inflammatory genes was
   down-regulated following non-surgical therapy. With regard to in vitro
   studies, gene expression profiling has been performed on gingival
   fibroblasts from inflamed and healthy gingival tissues, for a limited
   number of inflammatory markers, such as interleukin (IL)-1, IL-6, IL-8,
   tumor necrosis factor- α (TNF-α) and CD14 [42][8]. Furthermore,
   microarray analysis has also been performed on periodontal ligament
   cells and gingival keratinocytes [43][9], [44][10]. With regard to
   disease susceptibility at a genomic level, one genome-wide association
   study (GWAS) has been conducted in patients with aggressive
   periodontitis showing an association between aggressive periodontitis
   and intronic single nucleotide polymorphism rs1537415, which is located
   in the glycosyltransferase gene GLT6D1 [45][11].

   Despite research investigating periodontitis gene expression profiles
   through microarray analysis, specific genes responsible for the disease
   have not yet been found. However, the recent development of massively
   parallel sequencing has provided a more comprehensive and accurate tool
   for gene expression analysis through sequenced based assays of
   transcriptomes, RNA-Sequencing (RNA-Seq). This method enables analysis
   of the complexity of whole eukaryotic transcriptomes [46][12] and
   studies comparing RNA-Seq and microarrays have shown that RNA-Seq has
   less bias, a greater dynamic range, a lower frequency of false positive
   signals and higher reproducibility [47][13], [48][14]. The aim of the
   present study was to investigate the general pattern of the gene
   expression profile in periodontitis using RNA-Seq. We also aimed to
   investigate the local variation in gene expression at site level,
   comparing periodontitis-affected and healthy gingival tissues obtained
   from the same patient.

Materials and Methods

Ethics Statement

   The study was performed in accordance with the Declaration of Helsinki
   and the current legislation in Sweden and after approval from the
   Karolinska Institutet Ethical Research Board. The Regional Ethics Board
   in Stockholm approved the collection of the biopsies and informed
   consent was obtained from all patients.

Collection of gingival tissue samples

   A total of 10 nonsmoking individuals (20 biopsies), were included in
   the study. Four patients in the study group had other types of
   diseases: patient 2 was undergoing investigations for the disease
   sarcoidosis, patient 3 had diabetes type-2, patient 7 had a history of
   osteoarthritis and patient 10 was diagnosed with asthma. All
   participants were examined for periodontal disease and those with a
   tooth site demonstrating a probing depth ≥6 mm, clinical attachment
   level ≥5 mm and bleeding on probing were included in the
   periodontitis-affected group, according to the clinical parameters
   previously used as indicators of periodontitis [49][15], [50][16],
   [51][17]. During flap surgery, two adjacent gingival biopsies with
   identical clinical status were harvested from a periodontal pocket
   affected by periodontitis. The sizes of the specimens were
   approximately 2×2 mm, and included the connective tissue and the
   epithelium. In the same subjects, two adjacent gingival biopsies with
   identical clinical status and of about the same size were also obtained
   from a clinically healthy gingival pocket. Clinically healthy pockets
   were defined as sites with no gingival/periodontal inflammation, no
   bleeding on probing, a probing depth ≤3.5 mm and a clinical attachment
   level ≤3.5 mm. One of the biopsies from each site was stored in RNA
   Later (Applied Biosystems, USA) overnight at 4°C and thereafter stored
   at −80°C for subsequent RNA isolation. The second biopsy from each site
   was used for histological and immunohistochemical analysis.

Hematoxylin-Eosin staining

   Deparaffinized serial sections of gingival tissues were formalin fixed
   (4% neutral buffered formalin) and paraffin embedded. For assessment of
   orientation of the epithelium and connective tissue as well as the
   degree of inflammation, deparaffinized serial sections (4 µm) were
   prepared and sections of each biopsy were stained with
   Hematoxylin-Eosin (H&E). The degree of inflammatory cell infiltration
   was evaluated by three blinded observers, using a relative scale from 0
   to 3, and statistical differences between periodontitis-affected and
   healthy sites were tested using the Wilcoxon signed-rank test.

Immunohistochemical stainings in gingival tissue

   For staining of the T cell marker CD3, interferon regulatory factor 4
   (IRF4) and chemokine (C-C motif) ligand 18 (CCL18), gingival tissues
   were rinsed in phosphate buffered saline (PBS) with 0.1% Saponin
   (PBS-Saponin buffer) for 10 min. After an antigen retrieval procedure,
   10 mM Tris, 1 mM EDTA (pH 9.0) for CD3 and 0.01 M Citrate acid (pH 6.0)
   for IRF4 and CCL18, sections were blocked in 1% H[2]O[2] in PBS-Saponin
   for 60 min at room temperature (RT) for CD3 and for 45 min at RT for
   IRF4 and CCL18. Subsequently, tissues were rinsed in PBS-Saponin for 10
   min and further treated with 3% bovine serum albumin (BSA) diluted in
   PBS-Saponin for 30 min at RT. The expression of CD3, IRF4 and CCL18 was
   investigated using CD3 polyclonal rabbit antibody (1 µg/ml,
   PBS-Saponin) from Dako Sweden AB (Stockholm, Sweden), IRF4 polyclonal
   rabbit antibody (0.5 µg/ml, PBS-Saponin) from Atlas antibodies
   (Stockholm, Sweden) and CCL18 polyclonal rabbit anti-human antibody
   (0.5 µg/ml, PBS-Saponin) from Sigma-Aldrich (St. Louis, MO, USA).
   Normal rabbit IgG from R&D systems (MN, USA) was used as negative
   control. After incubation with primary antibody, sections were blocked
   with 1% normal goat serum in PBS for 15 min. Afterwards, sections were
   incubated with a biotinylated secondary antibody provided in the
   Vectastain ABC-Elite Complex Kit (Vector labs, Burlingame, CA, USA)
   followed by application of the Elite ABC solution for 40 min at RT in
   the dark. Thereafter, sections were washed with PBS and the peroxidase
   activity was visualized with 0.3% (v/v) in DAB buffer containing 0.1%
   (v/v) H[2]O[2]. Finally, the slides were washed with distilled water,
   dehydrated through an ethanol series (70%, 95%, 99.9%) into xylene,
   mounted, and photographed using a light microscope. For CD3 stainings,
   the amount of positive cells was evaluated by three blinded observers,
   using a relative scale from 0 to 3, and statistical differences between
   periodontitis-affected and healthy biopsies were tested using the
   Wilcoxon signed-rank test.

RNA extraction

   RNA was extracted from gingival biopsies using steel-bead matrix tubes
   and a tabletop Fast-Prep homogenizer by two sequential centrifugations
   for 20 s at speed 6.5 (Qbiogene, Irvie, CA, USA). The RNA was purified
   on RNeasy Spin Columns (Qiagen, Valencia, CA, USA), treated with DNAse
   H to ensure degradation of DNA, and thereafter eluated in RNase-free
   water. The average RNA yield was 15.6 µg. RNA quality was assessed
   using the RNA 6000 NanoLabChip Kit of the Bioanalyzer system from
   Agilent Technologies (Santa Clara, CA, USA).

Transcriptome sample preparation for sequencing

   A total amount of 2–3 µg per sample was used as input material for the
   RNA sample preparations. All samples had RIN values above 8. The
   samples were bar-coded and prepared according to the protocol (Cat#
   RS-930-1001) from the manufacturer (Illumina, San Diego, CA, USA), as
   previously described by Stranneheim et al. [52][18]. All sample
   preparation reagents were taken from the Illumina mRNA Sample
   Preparation Kit or ordered from vendors specified in the mRNA sample
   preparation protocol, except for automation specific reagents:
   carboxylic acid beads used for precipitation; the ethanol and
   tetraethylene glycol (EtOH/TEG) and the Polyethylene Glycol and sodium
   chloride (PEG/NaCl) precipitation buffers.

Clustering and sequencing

   The clustering of the bar-coded samples was performed on a cBot Cluster
   Generation System using an Illumina HiSeq Single Read Cluster
   Generation Kit according to the manufacturer's instructions. The
   library preparations were sequenced on an Illumina HiSeq 2000 as
   single-reads to 100 bp. Two sequencing runs were performed according to
   the manufacturer's instructions where two and three lanes were used in
   the first sequencing and second sequencing run, respectively ([53]Table
   S1). The runs generated a total of 402 million reads with an average of
   15 million reads per sample that passed the Illumina Chastity filter;
   these reads were included in the study.

Sequence analysis

   All sequences were aligned to the human genome reference hg19 with
   TopHat [54][19], [55][20] version 1.1.4 and Samtools [56][21] version
   0.1.8 using TopHat standard parameters except for parameters
   –solexa1.3-quals -p 8 –GTF Homo_sapiens.GRCh37.59.gtf. Annotations from
   Ensembl and RefSeq, downloaded from University of California, Santa
   Cruz (UCSC) Genome Browser, were used to assign features to genomic
   positions. Sequences aligned to the human genome were assigned to
   features and counted using HTSeq version 0.4.6 with parameters -m
   intersection-strict -s no -t exon. The R/Bioconductor package DESeq
   [57][22] was used to call differential gene expression on read counts
   generated by HTSeq and to perform hierarchical clustering of samples.
   All biological replicates for healthy and periodontitis-affected had
   R^2 (Spearman) correlation of gene expression (read counts) above 0.92.

Functional analyses of gene lists using WebGestalt

   Analyses of gene categories and pathways was performed using the
   WEB-based Gene Set Analysis Toolkit v2 (WebGestalt) [58][23] with
   parameters: Id Type: Ensembl_gene_stable_id, Ref Set: Entrez Gene,
   Significance Level: p<0.05, Statistics Test: Hypergeometric, MTC: BH,
   Minimum: 2. KEGG analysis was used for pathway enrichment analysis and
   the Gene ontology (GO) category Biological process was used for the
   functional annotation analysis.

Results

Patients and gingival tissues

   A total of 10 patients, six males and four females, with a mean age of
   50±8, were included in the study. For each patient, a total of four
   gingival biopsies of about the same size were obtained from
   periodontitis-affected and healthy gingiva, with two biopsies from each
   site. Bleeding status, probing depth and degree of inflammation in the
   gingival tissues for each of the two gingival sites was recorded
   ([59]Table 1). To assess the degree of gingival inflammation in the
   periodontitis-affected and healthy tissues, histological and
   immunohistochemistry staining was performed using H&E and anti-CD3
   ([60]Fig. 1). Scoring of the degree of inflammatory cell infiltration,
   assessed by H&E staining, and the amount of CD3 positive cells showed
   significantly higher inflammation in tissue from periodontitis-affected
   sites (p<0.01 for H&E and p<0.05 for CD3; [61]Table 1).

Table 1. Patient characteristics and periodontal status.

   Patient characteristics Periodontitis-affected sites Healthy sites
   Patient Gender Age Probing depth (mm) InflammationH&E (0–3)[62]^a ^,
   [63]^c Inflammation CD3 (0–3)[64]^b ^, [65]^d Probing depth (mm)
   Inflammation H&E (0–3)[66]^a ^, [67]^c Inflammation CD3 (0–3)[68]^b ^,
   [69]^d
   1 M 52 7 3 2 3 2 0
   2 M 45 8 1 1 3 1 1
   3 M 52 7 3 3 3 1 1
   4 F 47 7 3 3 3 1 2
   5 F 37 7 2 2 3 1 1
   6 M 59 7 2 3 3 2 1
   7 F 66 7 2 - 3 0 -
   8 M 48 6 2 2 3 0 0
   9 M 42 8 3 1 2 1 1
   10 F 54 6 2 1 3 1 1
   [70]Open in a new tab
   ^a

   0 = no evidence of inflammatory infiltration, 1 = slight inflammatory
   infiltration, 2 = moderate inflammatory infiltration and 3 = severe
   inflammatory infiltration.
   ^b

   0 = no CD3 positive cells, 1 = low amount of CD3 positive cells,
   2 = moderate amount of CD3 positive cells, 3 = high amount of CD3
   positive cells and – = not enough material to perform staining.
   ^c

   Significant difference between periodontitis-affected and healthy sites
   (p<0.01).
   ^d

   Significant difference between periodontitis-affected and healthy sites
   (p<0.05).

Figure 1. H&E and CD3-stained paraffin-embedded gingival biopsies obtained
from one representative patient with periodontitis.

   [71]Figure 1
   [72]Open in a new tab

   A. H&E staining of inflammatory cells in periodontitis-affected
   sections. B. H&E staining of inflammatory cells in healthy gingival
   sections. C. Staining of the T-cell marker CD3 in
   periodontitis-affected sections. D. Staining of the T-cell marker CD3
   in healthy sections. E, epithelium, C, connective tissue.

RNA-Sequencing

   We sequenced cDNA from 10 periodontitis-affected and 10 healthy
   gingival tissues, with an average of 15 million reads of 100 bp in
   length per sample. A pairwise approach, where each
   periodontitis-affected biopsy had a healthy counterpart from the same
   individual, was used to eliminate the background noise of
   individual-specific gene transcription, enabling acquisition of more
   relevant data from the cohort. Aligning the sequence reads against the
   human genome yielded a median of 68% of uniquely aligned reads across
   all samples. The expression pattern, based on RNA-Seq reads, of
   well-known inflammatory mediators IL-1β, IL-6, IL-8, TNFα, Regulated
   upon Activation, Normal T-cell Expressed, and Secreted (RANTES) and
   Monocyte Chemotactic Protein-1 (MCP-1) were analyzed in all the tissue
   samples. The expression (log[2] fold change) of these mediators was
   shown to be higher in the majority of the periodontitis-affected
   gingival tissue compared to healthy gingival tissue from the same
   patient ([73]Fig. 2).

Figure 2. Expression of the inflammatory mediators in periodontitis-affected
and healthy tissues obtained by RNA-seq.

   [74]Figure 2
   [75]Open in a new tab

   The bars show the expression (log[2] fold change) pattern based on
   RNA-Seq reads of IL-1β, IL-6, IL-8, TNFα, RANTES and MCP-1.

Distribution of gene transcripts between periodontitis-affected and healthy
gingival tissues

   A total of 22 122 different mRNA transcripts were expressed in the
   periodontitis-affected and healthy gingival tissue samples. Among these
   transcripts, 1375 were unique to the periodontitis-affected tissue
   samples whereas 511 genes were uniquely transcribed in healthy gingival
   tissues ([76]Fig. 3). KEGG enrichment analysis using WebGestalt
   [77][24] was performed among the unique genes for the
   periodontitis-affected and healthy tissues which revealed several
   regulated pathways indicative of inflammation for the
   periodontitis-affected condition ([78]Table 2 and [79]Table S1). In
   contrast, in the healthy gingival tissues, regulated pathways indicated
   a non-inflammatory profile among the unique genes, as demonstrated in
   [80]Table 3 and [81]Table S1.

Figure 3. Venn diagram of mRNA transcripts.

   Figure 3
   [82]Open in a new tab

   Venn diagram showing genes that were uniquely expressed in
   periodontitis-affected (1375) and healthy (511) gingival tissues. The
   intersection of the two circles refers to transcripts, which are
   expressed in both periodontitis-affected and healthy gingival tissues
   (20 236).

Table 2. Enriched regulated (KEGG) biological pathways among unique genes in
periodontitis-affected tissues.

   Pathway Total genes in pathway Unique genes in pathway[83]^a Adj p
   value[84]^b
   Neuroactive ligand-receptor interaction 256 19 8.18e-10
   Cytokine-cytokine receptor interaction 267 18 6.75e-09
   Chemokine signaling pathway 190 10 0.0004
   Intestinal immune network for IgA production 50 5 0.0014
   Alanine, aspartate and glutamate metabolism 31 5 0.0022
   Tyrosine metabolism 46 4 0.0103
   Calcium signaling pathway 178 7 0.0160
   Hedgehog signaling pathway 56 4 0.0161
   Systemic lupus erythematosus 140 6 0.0168
   Glycine, serine and threonine metabolism 31 3 0.0196
   Jak-STAT signaling pathway 155 6 0.0229
   Vascular smooth muscle contraction 115 5 0.0271
   Arhythmogenic right ventricular cardiomyopathy (ARVC) 76 4 0.0293
   [85]Open in a new tab
   ^a

   Lists of uniquely expressed genes within the enriched pathways can be
   found in [86]Table S1.
   ^b

   adj p value indicates the significance of the enrichment, (adj p<0.05).

Table 3. Enriched regulated (KEGG) biological pathways among unique genes in
healthy tissues.

   Pathway Total genes in pathway Unique genes in pathway[87]^a Adj p
   value[88]^b
   Neuroactive ligand-receptor interaction 256 11 8.18e-10
   Glycolysis/Gluconeogenesis 62 3 6.75e-09
   Calcium signaling pathway 178 4 0.0004
   Gap junction 90 3 0.0014
   Pyruvate metabolism 40 2 0.0022
   Tryptophan metabolism 40 2 0.0103
   [89]Open in a new tab
   ^a

   Lists of uniquely expressed genes within the enriched pathways can be
   found in [90]Table S1.
   ^b

   adj p value indicates the significance of the enrichment, (adj p<0.05).

Clustering of biopsies

   Unsupervised hierarchical clustering was performed on all gene
   transcripts having a median read count above a cutoff level set to 0.3
   read counts per feature, to exclude expression due to spurious
   transcription ([91]Fig. 4). The gingival tissues from
   periodontitis-affected sites from different patients showed a more
   similar gene expression pattern than healthy gingival tissues from the
   same patient. Clustering according to individual, where the paired
   healthy and periodontitis-affected biopsies cluster together, was only
   observed for patient 6 and 7. However, the biopsies showed a general
   trend of clustering according to the degree of inflammation as assessed
   by H&E staining ([92]Table 1), except for sample 7H, sample 2H and an
   outlier sample 1H, which clustered separately. There was also a trend
   of forming larger clusters depending on sequence run, but paired
   biopsies (periodontits-affected and healthy) from each patient were
   always analyzed in the same sequence run.

Figure 4. Clustering dendrogram and heatmap of periodontitis-affected and
healthy biopsies.

   [93]Figure 4
   [94]Open in a new tab

   Clustering of all samples was based on gene transcripts with a median
   read above three times the background noise. The length of the branch
   between two biopsies and the colors of the heatmap correspond to degree
   of similarity between the gene expression profiles. Colors can be
   interpreted using the scale bar. Numbers in parentheses denote the
   inflammation scores of the biopsies after H&E histological evaluation.

Differential gene expression between periodontitis-affected and healthy
gingival tissues

   Differential gene expression between periodontitis-affected and healthy
   gingival tissues was analyzed using read counts for each gene with the
   DeSeq package [95][22]. The analysis revealed a total of 453
   significantly (adj p<0.01) differentially expressed genes. Additional
   analyses of genes expressed in periodontitis-affected gingiva, showed
   that 381 genes were upregulated, whereas 72 genes were shown to be
   down-regulated ([96]Fig. 5, [97]Table S2).

Figure 5. Volcano plot displaying differential expression.

   Figure 5
   [98]Open in a new tab

   Differential gene expression (adj p<0.01) between
   periodontitis-affected and healthy gingival tissues. The y axis
   corresponds to the log[2] fold change value (M value), and the x axis
   displays the mean expression value.

Gene Ontology enrichment analysis of differentially expressed genes

   Investigation of functional associations of gene expression changes in
   the tissue samples was performed using WebGestalt. Gene ontology (GO)
   Biological process was used for enrichment analysis. Significant gene
   enrichments (p<0.05) as well as their parent terms are demonstrated in
   [99]Fig. 6. Several GO categories were over-represented among genes
   differentially expressed in periodontitis-affected versus healthy
   gingival tissues. The categories were mainly indicative of immune and
   inflammatory responses. Further enrichment analysis regarding Molecular
   function and Cellular components are provided in the supplementary data
   ([100]Table S3).

Figure 6. Gene ontology (GO) analysis of differentially expressed genes.

   [101]Figure 6
   [102]Open in a new tab

   All significant (p<0.05) Biological processes (GO categories) and their
   parent terms are shown. The color of each node illustrates the
   significance and can be interpreted using the scale bar, which displays
   the p value. Each node is also marked with the number of significantly
   regulated genes mapped to the GO category.

Top 50 upregulated genes in periodontitis-affected gingival tissue

   The top 50 significantly upregulated genes in periodontitis-affected
   gingival tissue with Unigene entry are displayed in [103]Table 4
   together with Ensemble ID, gene symbol, fold change, log[2] fold change
   and p value. We investigated whether there were any available reports
   on the involvement of these genes in periodontitis or other chronic
   inflammatory conditions. Among the top 50 upregulated genes, we
   identified a number of candidate genes, which were not previously
   demonstrated to be involved in periodontitis but have been shown to be
   associated with other chronic conditions such as rheumatoid arthritis
   (RA). These candidate genes included FCRL5, adenosine monophosphate
   deaminase 1 (AMPD1), CCL18, tumor-necrosis factor receptor superfamily
   17 (TNFRSF17) and leukocyte immunoglobin-like receptor, subfamily A
   (without TM domain) member 3 (LILRA3), and IRF4 which has shown to be
   involved in chronic inflammatory diseases such as RA and inflammatory
   bowel disease (IBD), ([104]Table 5).

Table 4. Top 50 upregulated genes in periodontitis-affected tissue with
Unigene entry.

   Ensemble ID Gene symbol Description Fold change Log[2] fold change p
   value
   ENSG00000188596 C12orf63 chromosome 12 open reading frame 63 69,15 6,11
   9,54e-06
   ENSG00000132704 FCRL2 Fc receptor-like 2 30,36 4,92 1,39e-10
   ENSG00000143297 FCRL5 Fc receptor-like 5 25,24 4,66 5,24e-30
   ENSG00000116748 AMPD1 adenosine monophosphate deaminase 1 (isoform M)
   24,97 4,64 5,58e-05
   ENSG00000187323 DCC deleted in colorectal carcinoma 20,69 4,37 2,37e-09
   ENSG00000137265 IRF4 interferon regulatory factor 4 20,10 4,33 1,50e-32
   ENSG00000167077 MEI1 meiosis inhibitor 1 16,77 4,07 3,24e-16
   ENSG00000101194 SLC17A9 solute carrier family 17, member 9 14,40 3,85
   2,04e-14
   ENSG00000122188 LAX1 lymphocyte transmembrane adaptor 1 14,28 3,84
   3,83e-20
   ENSG00000110777 POU2AF1 POU class 2 associating factor 1 14,12 3,82
   9,81e-26
   ENSG00000124256 ZBP1 Z-DNA binding protein 1 13,76 3,78 1,60e-14
   ENSG00000170476 MGC29506 hypothetical protein MGC29506 13,33 3,74
   1,20e-21
   ENSG00000132185 FCRLA Fc receptor-like A 12,18 3,61 2,47e-11
   ENSG00000012223 LTF lactotransferrin 12,09 3,61 8,54e-22
   ENSG00000137673 MMP7 matrix metallopeptidase 7 (matrilysin, uterine)
   11,37 3,51 8,33e-18
   ENSG00000163534 FCRL1 Fc receptor-like 1 11,14 3,48 1,54e-05
   ENSG00000177455 CD19 CD19 molecule 11,12 3,48 2,13e-08
   ENSG00000061656 SPAG4 sperm associated antigen 4 11,09 3,47 1,62e-10
   ENSG00000121895 TMEM156 transmembrane protein 156 11,00 3,46 2,35e-08
   ENSG00000015413 DPEP1 dipeptidase 1 (renal) 10,93 3,45 8,81e-06
   ENSG00000048462 TNFRSF17 tumor necrosis factor receptor superfamily,
   member 17 10,43 3,38 2,71e-08
   ENSG00000169962 TAS1R3 taste receptor, type 1, member 3 10,42 3,38
   2,28e-06
   ENSG00000102096 PIM2 pim-2 oncogene 10,09 3,34 2,43e-23
   ENSG00000183508 FAM46C family with sequence similarity 46, member C
   9,94 3,31 2,19e-24
   ENSG00000168081 PNOC prepronociceptin 9,75 3,29 1,83e-07
   ENSG00000099958 DERL3 Der1-like domain family, member 3 9,45 3,24
   2,26e-16
   ENSG00000105369 CD79A CD79a molecule, immunoglobulin-associated alpha
   9,43 3,24 1,36e-18
   ENSG00000189233 C8orf80 chromosome 8 open reading frame 80 9,03 3,17
   2,42e-07
   ENSG00000004468 CD38 CD38 molecule 8,75 3,13 7,57e-10
   ENSG00000153789 FAM92B family with sequence similarity 92, member B
   8,21 3,04 2,95e-05
   ENSG00000143603 KCNN3 potassium intermediate/small conductance
   calcium-activated channel, subfamily N, member 3 7,86 2,98 3,81e-07
   ENSG00000007129 CEACAM21 carcinoembryonic antigen-related cell adhesion
   molecule 21 7,48 2,90 1,94e-05
   ENSG00000170866 LILRA3 leukocyte immunoglobulin-like receptor,
   subfamily A (without TM domain), member 3 7,45 2,90 0,000111577
   ENSG00000129988 LBP lipopolysaccharide binding protein 7,33 2,87
   7,78e-08
   ENSG00000118308 LRMP lymphoid-restricted membrane protein 7,24 2,86
   7,08e-09
   ENSG00000139193 CD27 CD27 molecule 7,21 2,85 4,63e-13
   ENSG00000073849 ST6GAL1 ST6 beta-galactosamide
   alpha-2,6-sialyltranferase 1 7,11 2,83 4,37e-20
   ENSG00000177272 KCNA3 potassium voltage-gated channel, shaker-related
   subfamily, member 3 7,07 2,82 4,64e-08
   ENSG00000108405 P2RX1 purinergic receptor P2X, ligand-gated ion
   channel, 1 6,81 2,77 3,18e-05
   ENSG00000026751 SLAMF7 SLAM family member 7 6,64 2,73 2,05e-16
   ENSG00000124772 CPNE5 copine V 6,47 2,69 5,14e-10
   ENSG00000132465 IGJ immunoglobulin J polypeptide, linker protein for
   immunoglobulin alpha and mu polypeptides 6,41 2,68 6,97e-21
   ENSG00000122224 LY9 lymphocyte antigen 9 6,39 2,68 1,71e-06
   ENSG00000007312 CD79B CD79b molecule, immunoglobulin-associated beta
   6,28 2,65 1,89e-07
   ENSG00000134873 CLDN10 claudin 10 6,17 2,63 2,82e-06
   ENSG00000172578 KLHL6 kelch-like 6 (Drosophila) 6,16 2,62 1,98e-11
   ENSG00000196549 MME membrane metallo-endopeptidase 6,01 2,59 2,29e-16
   ENSG00000006074 CCL18 chemokine (C-C motif) ligand 18 (pulmonary and
   activation-regulated) 6,00 2,59 5,68e-10
   ENSG00000173432 SAA1 serum amyloid A1 5,91 2,56 7,97e-10
   ENSG00000159618 GPR114 G protein-coupled receptor 114 5,89 2,56
   1,29e-05
   [105]Open in a new tab

Table 5. Selected upregulated genes identified in periodontitis and involved
in other chronic inflammatory diseases.

   Ensemble ID Gene symbol Description Fold change Log[2] fold change p
   value Involvement in other diseases
   ENSG00000143297 FCRL5 Fc receptor-like 5 25.24 4.66 5.98e-27 Rheumatoid
   arthritis (RA)
   ENSG00000116748 AMPD1 adenosine monophosphate deaminase 1 24.97 4.64
   0.0046 Rheumatoid arthritis (RA)
   ENSG00000137265 IRF4 interferon regulatory factor 4 20.10 4.33 2.31e-29
   Inflammatory Bowel Disease (IBD)
   ENSG00000048462 TNFRSF17 tumor necrosis factor receptor superfamily,
   member 17 10.43 3.38 4.80e-06 Rheumatoid arthritis (RA)
   ENSG00000170866 LILRA3 leukocyte immunoglobulin-like receptor,
   subfamily A (without TM domain), member 3 7.45 2.90 0.008037 Rheumatoid
   arthritis (RA)
   ENSG00000006074 CCL18 chemokine (C-C motif) ligand 18 (pulmonary and
   activation-regulated 6.00 2.59 1.22e-07 Rheumatoid arthritis (RA)
   [106]Open in a new tab

The protein expression of IRF4 and CCL18 in periodontitis-affected tissue

   The expression of two of the top 50 differentially upregulated genes,
   IRF4 and CCL18 where further investigated at the protein level in
   gingival tissue samples from five additional patients with
   periodontitis. Immunohistochemical analysis showed that the
   transcription factor IRF4 and the chemokine CCL18 were expressed at the
   protein level in gingival tissue from patients with periodontitis
   ([107]Fig. 7). IRF4 protein was expressed in cells including
   fibroblasts and inflammatory cells in the gingival connective tissue,
   as shown by morphology. For the chemokine CCL18, cellular staining of
   fibroblasts and inflammatory cells was observed, as well as some
   diffuse extracellular staining, consistent with chemokine secretion.

Figure 7. Immunohistochemical stainings of IRF4 and CCL18 in the connective
tissue of periodontitis-affected gingival sections.

   [108]Figure 7
   [109]Open in a new tab

   A. Immunohistochemical staining of IRF4. B. Immunohistochemical
   staining of CCL18.

Discussion

   This study provides a novel quantitative comprehensive mapping of gene
   expression in gingival tissues from patients diagnosed with
   periodontitis, using RNA-Seq.

   We first confirmed that the degree of inflammation was higher in
   periodontitis-affected gingival tissue compared to healthy tissues
   obtained from the same individual. Our results were based on
   immunohistological staining of CD3 positive cells, and further verified
   by RNA-Seq quantification of gene expression of the established
   inflammatory markers IL-1β, IL-6, IL-8, TNFα, RANTES and MCP-1. These
   inflammatory mediators have previously been reported to be elevated in
   patients with periodontitis [110][25], [111][26], [112][27].

   Next, we performed unsupervised clustering of the gingival tissues to
   get an overview of the data generated from the RNA-Seq analysis.
   Cluster analysis revealed that the majority of periodontitis-affected
   clustered together and the majority of the healthy gingival tissues
   also clustered together, which is in line with our results regarding
   inflammation in the tissues. The degree of inflammation, rather than
   the individual, seemed to affect the clustering, indicating a common
   gene expression profile for periodontitis. Our results, based on the
   gene expression pattern of the inflammatory markers (IL-1β, IL-6, IL-8,
   TNFα, RANTES and MCP-1) and the immunohistochemical evaluation,
   confirmed that the inflammation in periodontitis involves elevated
   levels of locally produced cytokines in the periodontium, as has been
   previously demonstrated [113][28]. However, cluster analysis revealed
   that three of the patients (patient no. 6, 7 and 2) deviated from the
   clustering pattern. For example, the healthy gingival tissue collected
   from patient 6 clustered with the periodontitis-affected tissue, which
   could be due to moderate inflammatory infiltration (H&E score 2)
   observed in the healthy gingival tissue. The clustering pattern in
   tissue from patient 7, where the healthy and diseased gingival tissue
   also clustered together, could be partly explained by the patient's
   history of osteoarthritis, which is a disease associated with elevated
   levels of circulating proinflammatory cytokines IL-6 and TNFα
   [114][29]. The cluster pattern for patient 2 differed from the rest of
   the patient group, which could be related to this patient undergoing
   investigation for the inflammatory disease sarcoidosis, and in turn
   might affect the systemic inflammatory response. Previous studies
   report that oral manifestations of sarcoidosis include aggressive
   destruction of the periodontium with rapid periodontal bone loss
   [115][30], [116][31], [117][32]. One of these studies also emphasizes
   the importance of patients diagnosed with sarcoidosis to be evaluated
   for other systemic involvements [118][31]. Thus, regarding our
   clustering pattern, it cannot be ruled out that general health
   differences might have some effect on the final outcome. However, the
   comparison of the gene expression profiles of all individuals should
   minimize potential interfering signals originating from single
   individuals affected with other diseases.

   Our RNA-Seq analysis, investigating the gene expression profile in the
   gingival tissues showed that the genes were differentially distributed
   between healthy and periodontitis-affected samples. Enrichment analysis
   among uniquely expressed genes in the periodontitis-affected tissues
   showed regulated pathways indicative of inflammation, such as cytokine
   signaling, chemokine signaling and the JAK-STAT signaling pathway.
   Several cytokines such as interleukins, which are involved in
   periodontits, signal through the JAK-STAT signaling pathway [119][33].
   On the other hand, in the healthy biopsies, pathways were indicative of
   non-inflammatory processes that may be involved in the maintenance of
   the healthy gingival tissue. Future studies should also include
   investigation of genes within these pathways, which may contribute to
   understanding, prevention and treatment of periodontitis.

   Differential gene expression analyses of periodontitis-affected vs.
   healthy gingival tissues showed the majority of differentially
   expressed genes to be upregulated in the periodontitis-affected
   tissues. Furthermore, GO enrichment analysis among these differentially
   expressed genes demonstrated that most of these genes were involved in
   immune and inflammatory processes. This is in line with the increased
   inflammatory response in the tissue, and also in accordance with our
   previous microarray studies on inflammatory-stimulated cell cultures
   reporting that gene expression profiles of TNFα-stimulated cells show
   an induction of inflammatory genes [120][34], [121][35].

   Up to date, RNA-Seq studies aimed to identify new genes involved in the
   pathogenesis of periodontits have not been reported. One ab initio
   study by Covani et al. [122][36] identified genes with potential roles
   in periodontitis, some of which have not previously been associated
   with the disease. However, the protein expression of these genes in
   periodontitis-affected tissues has not been confirmed. In our study we
   aimed to identify genes involved in the pathogenesis of periodontitis.
   Therefore, we further searched through the differentially expressed
   genes, focusing on the top 50 upregulated genes. Two of these 50
   upregulated genes, IRF4 and CCL18, were also detected at the protein
   level in periodontitis affected-tissues, supporting these genes as
   novel finds in the pathogenesis of periodontitis. Furthermore, these
   two selected genes have been reported to be involved in other chronic
   inflammatory diseases such as RA. The transcription factor, IRF4, has
   been demonstrated to be involved in T-cell-dependent chronic
   inflammatory diseases such as IBD [123][37]. Mudter et al. 2011
   reported a correlation between mRNA levels of IRF4 and production of
   cytokines such as IL-6 and IL-17 in the inflamed colon from patients
   with IBD, indicating that IRF4 is involved in the regulation of chronic
   mucosal inflammation [124][37]. In addition, the gene for CCL18 was
   upregulated in periodontitis-affected gingival tissues. This chemokine,
   expressed by macrophages, monocytes and dendritic cells, has been
   demonstrated to be increased in synovial tissue of RA patients
   [125][38]. It has also been suggested that blockage of CCL18 expression
   by anti-TNF-α antibodies identifies CCL18 as an additional target for
   anti-TNF-α therapy in patients with RA [126][39], [127][40]. Studies
   are currently ongoing to investigate the expression of candidate genes
   novel for periodontitis in a larger cohort of patients with
   periodontitis and healthy controls, to be able to evaluate their impact
   and to further explore the possible therapeutic targeting of these
   genes. In addition, future studies will also be performed investigating
   the biological significance of the down-regulated genes in
   periodontitis.

   In conclusion, we demonstrate for the first time, using RNA-seq,
   profile analysis of periodontitis revealing site-specific local
   variation in gene expression profiles of periodontitis-affected and
   healthy tissues obtained from patients diagnosed with periodontitis.
   Furthermore, we have identified differentially expressed novel genes in
   gingival tissue of periodontitis. Our findings provide a first step
   towards a quantitative comprehensive insight into the transcriptome of
   gingival tissue from patients with periodontitis, to enable
   identification of possible diagnostic markers of periodontitis as well
   as potential therapeutic targets.

Supporting Information

   Table S1

   Uniquely expressed genes within enriched pathways in
   periodontitis-affected and healthy gingival tissues.

   (XLSX)
   [128]Click here for additional data file.^ (25.8KB, xlsx)
   Table S2

   Full list of all significantly differentially expressed genes in
   periodontitis-affected and healthy gingival tissues.

   (XLSX)
   [129]Click here for additional data file.^ (88.3KB, xlsx)
   Table S3

   Gene Ontology enrichment analysis of differentially expressed genes.

   (XLSX)
   [130]Click here for additional data file.^ (97.1KB, xlsx)

Acknowledgments