Transcriptome sequencing of gingival biopsies from chronic periodontitis patients reveals novel gene expression and splicing patterns
Kim et al. Human Genomics
Transcriptome sequencing of gingival biopsies from chronic periodontitis patients reveals novel gene expression and splicing patterns
Yong-Gun Kim 1 2 3
Minjung Kim 0 1
Ji Hyun Kang 5
Hyo Jeong Kim 5
Jin-Woo Park 3
Jae-Mok Lee 3
Jo-Young Suh 3
Jae-Young Kim 2 5
Jae-Hyung Lee 0 4
Youngkyun Lee 2 5
0 Department of Life and Nanopharmaceutical Sciences, Kyung Hee University , Seoul 02447 , Korea
1 Equal contributors
2 Institute for Hard Tissue and Bone Regeneration, Kyungpook National University , Daegu 41940 , Korea
3 Department of Periodontology, School of Dentistry, Kyungpook National University , Daegu 41940 , Korea
4 Department of Maxillofacial Biomedical Engineering, School of Dentistry, Kyung Hee University , 26 Kyunghee-daero, Dongdaemun-gu, Seoul 02447 , Korea
5 Department of Biochemistry, School of Dentistry, Kyungpook National University , 2177 Dalgubeol-daero, Joong-gu, Daegu 41940 , Korea
Background: Periodontitis is the most common chronic inflammatory disease caused by complex interaction between the microbial biofilm and host immune responses. In the present study, high-throughput RNA sequencing was utilized to systemically and precisely identify gene expression profiles and alternative splicing. Methods: The pooled RNAs of 10 gingival tissues from both healthy and periodontitis patients were analyzed by deep sequencing followed by computational annotation and quantification of mRNA structures. Results: The differential expression analysis designated 400 up-regulated genes in periodontitis tissues especially in the pathways of defense/immunity protein, receptor, protease, and signaling molecules. The top 10 most upregulated genes were CSF3, MAFA, CR2, GLDC, SAA1, LBP, MME, MMP3, MME-AS1, and SAA4. The 62 down-regulated genes in periodontitis were mainly cytoskeletal and structural proteins. The top 10 most down-regulated genes were SERPINA12, MT4, H19, KRT2, DSC1, PSORS1C2, KRT27, LCE3C, AQ5, and LCE6A. The differential alternative splicing analysis revealed unique transcription variants in periodontitis tissues. The EDB exon was predominantly included in FN1, while exon 2 was mostly skipped in BCL2A1. Conclusions: These findings using RNA sequencing provide novel insights into the pathogenesis mechanism of periodontitis in terms of gene expression and alternative splicing.
Periodontitis; Transcriptome sequencing; Alternative splicing; Gene expression profile
Periodontitis is a chronic inflammatory disease of
periodontium, characterized by massive destruction of both
soft and hard tissues surrounding the teeth [
current concept for the periodontal diseases involve
complex interaction between the microbial biofilm and host
immune responses that leads to the alteration of bone and
connective tissue homeostasis [
]. Understanding the
molecular mechanisms underlying the pathogenesis as
well as development of efficient therapeutics is
furthermore important since periodontitis is linked to other
metabolic and/or systemic diseases including diabetes,
cardiovascular diseases, and rheumatoid arthritis [
The analysis of transcriptome by microarrays has been a
valuable tool to study the changes in gene expression
profiles in gingival tissues of periodontitis patients [
However, recent advances in the high-throughput RNA
sequencing technology revolutionarily enhanced our
understanding on the complexity of eukaryotic transcriptome
]. RNA sequencing has several key advantages over
the hybridization-based microarray techniques. First of all,
direct sequencing enables an unbiased approach compared
with the microarrays that depends on the predetermined
genome sequences. Secondly, RNA sequencing is highly
accurate in detecting gene expression with very wide
dynamic detection ranges with low background. Thus, RNA
sequencing is not only useful to precisely determine gene
expression profiles but also particularly powerful to detect
novel transcription variants via alternative splicing .
In the present study, we analyzed the pooled
transcriptome from gingival tissues of periodontitis patients and
compared with that of healthy patients. The large sum
of novel information on the gene expression profiles as
well as novel transcripts through alternative splicing
would provide not only insights into the pathogenesis of
periodontitis but also basis for the development of
biomarkers and therapeutic targets.
Materials and methods
Periodontitis patient characteristics and gingival tissue samples
Gingival tissue samples were collected from chronic
periodontitis patients or healthy individuals. On the basis of
clinical and radiographic criteria, the periodontitis-affected
site had a probing depth of ≥4 mm, clinical attachment
level of ≥4 mm, and bleeding on probing. A total of 10
gingival samples were collected from 9 periodontal healthy
patients who visited Kyungpook National University
Hospital. Similarly, a total of 10 periodontitis tissue samples
were obtained from 4 periodontitis patients with pocket
depth of 4~6 mm and 3 severe periodontitis patients with
pocket depth of 7 mm or deeper. The patient
characteristics are given in Additional file 1: Table S1. All patients
were non-smoking and did not have untreated metabolic/
systemic diseases nor associated with
infection/autoimmune diseases at the time of tissue collection. The size
of 3-mm2 gingival biopsies were obtained from the
marginal gingiva during periodontal flap surgery and
immediately stored in RNAlater solution (Thermo Fisher Scientific,
Waltham, MA) at −70 °C after removal of blood by brief
washing in phosphate-buffered saline. The study was
approved by the institutional review board of the Kyungpook
National University Hospital with informed consent from
Isolation of RNA and RNA sequencing
Frozen tissues were disrupted in the lysis solution of
mirVana RNA isolation kit (Thermo Fisher Scientific)
using disposable pestle grinder system (Thermo Fisher
Scientific). After RNA extraction, the same amount of
total RNA isolated from each individual sample (1 μg)
was pooled into 2 groups (healthy and periodontitis) and
used for further analysis. The integrity of pooled total
RNA was analyzed by Agilent 2100 Bioanalyzer (Agilent
Technologies, Santa Clara, CA). After purification of
mRNA molecules by poly-T oligo-attached magnetic
beads followed by fragmentation, the RNA of
approximately 300-bp size was isolated using gel
electrophoresis. The cDNA synthesis and library construction was
performed using the Illumina Truseq RNA sample
preparation kit (Illumina, San Diego, CA), following the
manufacturer’s protocol. The PCR-amplified cDNA
templates on a flow cell was loaded and sequenced in the
HiSeq 2000 sequencing system (Illumina) in the
pairedend sequencing mode (2 × 101 bp reads).
Sequencing data analysis
All sequencing raw reads were aligned to the human
genome reference hg19 using the GSNAP alignment tool
]. Only uniquely and properly mapped
read pairs were used for further analysis. The differentially
expressed genes between gingival tissues from periodontal
healthy patients and periodontitis patients were identified
using the DESeq R package [
]. Differentially expressed
genes were defined as those with changes of at least 2-fold
between samples and at a false discovery rate (FDR) cutoff
of 5 % based on DESeq adjusted p values. The analysis of
alternative splicing events was performed using MATS
]. The differences in the alternative splicing in
genes were considered significant when the inclusion
difference between samples was equal or greater than 5 % at
a 10 % FDR. Each alternative splicing change of the
skipped exon vent was manually inspected in UCSC genome
browser using the sequencing data. The functional
classification analysis of differentially expressed genes was performed
using the PANTHER tools (http://www.pantherdb.org). The
GO term and KEGG pathway enrichment analysis was
performed as described previously [
]. Briefly, the fraction of
genes in a test set associated with each GO category was
calculated and compared with that of control set comprised of
randomly chosen genes of the same number and length of
the test genes. The random sampling was repeated 100,000
times for the calculation of empirical p value. The
significance of enriched GO terms or KEGG pathways were
determined by the p value cutoff, which was 1/total number of
GO terms considered.
Validation of differentially expressed genes and alternative splicing events
From the pooled RNA samples, 1 μg of RNA was reversed
transcribed using the Superscript II Reverse Transcriptase
(Thermo Fisher Scientific). Quantitative real-time PCR
analysis was performed by the addition of 1 μg of cDNA
and SYBR green master mix in MicroAMP optical tubes
using the AB 7500 system (Thermo Fisher Scientific). The
expression of genes relative to that of HPRT1 was
determined by the 2–ΔΔCT method [
]. The differential
alternative splicing events were confirmed via RT-PCR analysis
with the addition of 1 μg of cDNA and Takara premix Taq
polymerase (Takara Bio Inc, Shiga, Japan) for 33 cycles of
10 s at 98 °C, 30 s at 60 °C, and 1 min at 72 °C. The
primers for the detection of alternative splicing were
designed by the PrimerSeq software [
] in order that the
PCR product to span the region of exon
inclusion/skipping, enabling the differentiation of alternative splicing
events by product size. The primer sequences for the
realtime RT-PCR analysis of selected genes and those for the
RT-PCR detection of alternative splicing events of FN1
and BCL2A1 gene were provided in the supplemental
tables (Additional file 2: Table S2 and Additional file 3:
RNA sequencing results
Total RNA was extracted from 10 healthy gingival tissue
samples and 10 chronic periodontitis-affected gingival
tissues as described above. Then, cDNAs synthesized
from the pooled RNA samples of both groups were
sequenced using the Illumina HiSeq 2000 system, which
generated approximately 80 million pairs of reads of 101 bp
in size. When compared with the reference sequence of
Genome Reference Consortium GRCh37 (hg19), more than
90 % of read pairs were uniquely mapped on the human
genome (Table 1). Gene annotation using the Ensembl
(release 75) identified that a total of 36,814 genes have at least
1 read mapped on the exonic regions. Among these, 4800
genes were unique to the periodontitis tissue sample, while
2811 transcripts were detected only in healthy gingival
Identification and classification of differentially expressed genes between periodontitis and healthy gingiva
The differential expression of genes between
periodontitis and healthy gingival samples was analyzed by DESeq
]. By applying the cutoff of at least twofold
change in the number of reads with 5 % FDR, we found
a total of 462 genes differentially expressed between the
samples (Fig. 1a, volcano plot). While 400 genes were
up-regulated in the periodontitis tissue sample, 62 genes
were down-regulated compared with the healthy control
(Additional file 4: Table S4). Previously, Davanian et al.
reported the discovery of 381 genes up-regulated in the
periodontitis-affected gingival tissues by RNA sequencing
]. Notably, 182 genes among them were also found to
be up-regulated in the present study (Additional file 5:
Figure S1), demonstrating an overlap between the two
sets of gene lists when analyzed by a hypergeometric
test (p < 2.2e−16) [
The top 20 up-regulated genes listed in Table 2 included
cytokines and immune response-related genes (CSF3,
CR2, LBP, CXCL1, and IL19), serum amyloid proteins
(SAA1, SAA4, and SAA2), and proteases (MME, MMP3,
MME-AS1, and MMP7). The 20 most down-regulated
genes (Table 3) included peptidase inhibitors (SERPINA12
and SPINK9) and structural proteins (KRT2, KRT27,
LCE3C, LCE6A, LCE1B, LCE2D, and KRT1).
To classify the differentially expressed genes into
functionally related subgroups, we utilized the PANTHER
classification system (http://pantherdb.org). As a result,
the 462 differentially expressed genes between
periodontitis and healthy gingival tissues were segregated into 20
different classes of proteins. When we compared the
composition of these protein classes, there was a significant
difference in the number of genes between periodontitis
and healthy gingival samples in 6 protein classes. In the
periodontitis tissue, genes classified as defense/immunity
protein, receptor, protease, and signaling molecules were
significantly enriched (Fig. 1b). On the other hand, genes
in the categories of cytoskeletal protein and structural
protein were predominant in healthy tissue sample
compared with periodontitis. Furthermore, functional
annotation of GO and KEGG pathway enrichment analyses as
previously described [
] revealed enhanced immune
responses in the periodontal tissues, including NOD-like
receptor signaling, cytokine and chemokine activities,
response to lipopolysaccharide, Jak-STAT signaling pathway,
and B cell receptor signaling pathway (Additional file 6:
Table S5 and Additional file 7: Table S6).
Validation of differentially expressed genes between periodontitis and healthy gingiva by quantitative realtime PCR analysis
To validate the differential gene expression results by
RNA sequencing analysis, we selected 10 up-regulated or
down-regulated genes in periodontal tissue and assessed
their expression by quantitative real-time RT-PCR
analysis. Figure 1c shows that the examination of differential
gene expression by both methods is significantly
concordant, with the Pearson’s correlation coefficient (R) value of
0.81 (p = 0.005). Since the current study design employed
pooling of samples, we further validated the variations in
gene expression in individual samples of healthy and
periodontitis patients. The real-time RT-PCR analyses for
selected genes (Additional file 8: Figure S2) mostly repeated
the RNA sequencing results, showing significant reduction
in NOS1, CHP2, CDON, and MT4. Similarly, significant
elevation was observed in ICAM1, MMP13, LYN,
CSF3, MMP3, LBP, and CXCL2 while the expression
of IL6 and IL19 only slightly increased. However, a
large individual variation was observed in SERP1 and
Alternative splicing events in periodontitis and healthy gingival tissues
More than 90 % of human genes are alternatively spliced
through different types of splicing [
]. To identify the
differential splicing events between the healthy and
periodontitis gingival tissues, the inclusion level of
alternative spliced exons was compared using the MATS tool
 based on a statistical model that calculates the
difference in the isoform ratio of a gene. The MATS analysis of
RNA sequencing data revealed 183 significantly
differential alternative splicing events in 155 genes with a cutoff of
5 % inclusion difference and 10 % FDR (Table 4 and
Additional file 9: Table S7). The GO and KEGG pathway
enrichment analyses for the determination of the
biological relevance of those differentially spliced genes
showed significant difference in the pathways including
RNA splicing regulation, substrate adhesion-dependent cell
spreading, response to wound healing, and positive
regulation of cell migration (Additional file 10: Table S8 and
Additional file 11: Table S9).
Among the genes that exhibited prominently novel
included exons was FN1 that encodes one of the major
extracellular matrix protein fibronectin [
structure consists of 2 nearly identical ~250-kDa
glycoprotein subunits with each monomer composed of
repetitive units of type I, II, and III domains [
type III domains contain 2 exons called extra domain A
(EDA) and extra domain B (EDB), the latter showed
significantly increased inclusion in periodontitis gingival
tissues compared with healthy samples (Fig. 2a; left
panel). The preferential formation of EDB-containing
isoform in periodontitis was further corroborated by the
RT-PCR analysis designed to amplify the included EDB
exon regions (Fig. 2a; right panel). The analysis of
alternative splicing events also indicated that BCL2A1
(BCL2-related protein A1) exhibited prominently skipped exon 2
(Fig. 2b; left panel). RT-PCR analysis designed to amplify
the skipped region revealed significantly increased shorter
isoform (Fig. 2b; right panel). The individual variation
between healthy and periodontitis tissues for these
differences in the alternative slicing events was
further confirmed by RT-PCR analyses (Additional file 12:
Figure S3). For FN1, the inclusion of EDB exon was
% of total differential alternative splicing events (183)
preferentially observed in periodontitis tissues (7/10)
compared with healthy tissues (3/8) tested. Similarly, the
skipping of exon 2 in BCL2A1 was predominant in
periodontitis tissues (9/10), compared with healthy tissues
Recent developments in the RNA sequencing technology
and bioinformatics tools enabled elaborate analysis of
gene expression in numerous human diseases. However
in periodontitis research, most RNA sequencing studies
have focused on the identification of microbiome that
constitutes periodontal biofilm, with little attention to
the host responses against such microbial challenge. The
current study provides extensive information on gene
expression as well as alternative splicing in periodontitis
gingival tissues, which is crucial for the understanding
the pathogenesis and development of biomarkers and
therapeutic targets. The gene expression analysis
revealed 62 down-regulated and 400 up-regulated genes in
periodontitis tissues, suggesting the effectiveness of mRNA
sequencing as a tool to scrutinize the differential gene
expression during the development of periodontitis. Davanian
et al. previously reported a series of up-regulated genes as
well as enriched biological pathways in periodontitis [
When we compared these results with ours, the current
results only partially overlap in terms of differential gene
expression, possibly originated from the difference in the
ethnic group of the subjects as well as in the methods to
eliminate individual fluctuations in the gene expression. For
example, Davanian et al. used healthy gingival tissue of the
same periodontitis-affected individual as healthy control
tissue. However, in the current study, the healthy and
periodontitis tissues were pooled, allowing the dilution of
individual differences in the gene expression. Indeed, the
RNA sequencing analysis of pooled samples proved
effective, since the expression levels of genes (except IL6 and
IL19) identified as differentially expressed by RNA
sequencing were also significantly different between healthy and
periodontitis samples, when we confirmed by real-time
PCR analysis of individual samples (Additional file 8: Figure
S2). Most of the top 20 up-regulated genes in periodontitis
tissue (Table 2) were associated with inflammation and
tissue degradation. Notably, serum amyloid A isoforms
consisted 3 of 20 most up-regulated mRNAs, supporting the
notion that these can serve as biomarkers for
periodontitisassociated acute as well as chronic inflammation [
Until recently, gene expression analyses mostly focused
on the genes whose expression was significantly increased
in periodontitis. In line with this, 18 of top 20
upregulated genes were associated with periodontal disease
at least once by previous studies. The current study
revealed 2 novel genes highly overexpressed in periodontitis
tissues compared with healthy control. MAFA is a
subgroup member of the basic leucine-zipper family
transcription factor prominently known for its role in
glucoseresponsive insulin secretion [
]. CLDN10 is an ion
channel-forming member of claudin family, which is a
constituent of tight junction [
]. The role of these genes
in periodontitis is of great interest and requires further
In contrast to the highly expressed genes in
periodontitis, fewer highlights have been drawn on the genes
down-regulated in periodontal diseases. In accordance,
most of the top 20 down-regulated genes (Table 3) have
not been studied with regard to periodontitis, although
investigating the role of those genes in periodontitis
compared with that in normal tissues would greatly
enhance our knowledge regarding the pathogenesis of
periodontal diseases. Notably, keratin (KRT2, KRT27,
and KRT1) and late cornified envelope (LCE3C, LCE6A,
LCE1B, and LCE2D) genes constituted significant part of
the down-regulated genes, suggesting the loss of
epithelial barrier [
]. The causal relationship between the loss
of these genes and the development of periodontal
diseases requires further investigation.
It has long been suggested that different sites in the
same individual exhibit different patterns of disease
progression, morphology, and often response to therapy
]. In addition, the oral microbiota responsible for the
induction of periodontal diseases is distinct from
site-tosite in the same individual [
]. Accordingly, it is
recommended to design clinical studies based on
individual sites rather than individual person . In
agreement of this notion, the analysis of gene expression in
individual sites by real-time RT-PCR (Additional file 8:
Figure S2) revealed site-specific variation. In different
sites from the same periodontitis patients (P2: P3, P7:
P8, and P9: P10), it was clearly noticeable that MMP3,
MMP13, and LBP expressions differ in a site-specific
manner. An individual RNA sequencing study with
larger number of patients is ongoing, which will further
provide detailed information on the site specificity of
The gene ontology and KEGG pathway enrichment
analyses revealed both innate and adaptive immune responses
in the periodontal tissues, including NOD-like receptor
signaling, response to lipopolysaccharide, cytokine and
chemokine activities, and B cell receptor signaling
pathways (Additional file 6: Table S5 and Additional file 7:
Table S6). The NOD1 and NOD2 have been suggested to
mediate the sensing of periodontal bacteria [
addition, NOD2 has been linked to the P.
gingivalis-induced bone resorption, since NOD2 knockout mice were
protected from bone loss in a periodontitis model [
Bellibasakis and Johansson showed that a periodontal
pathogen A. actinomyceptemcomitans regulated NLRP3
and NLRP6 expression in human mononuclear cells [
Considering the existence of 22 human NOD-like
receptor protein members and their crucial functions in
immune diseases, it will be of great interest and importance
to elucidate the involvement of these receptors in the
pathogenesis of periodontitis.
In the periodontitis lesions, it has been estimated that
more than 75 % of infiltrating immune cells are plasma
cells and B cells, suggesting the importance of these cells
in adaptive immunity during the development of
]. In accordance, molecules involved in B cell
activation including CD79, CD19, Lyn, and CR2 were
significantly increased in periodontitis tissue. An increasing
body of evidence indicates that B cells with autoreactive
propensities might be linked to tissue destruction in
]. Indeed, recent reports demonstrated
that B cell-deficient mice were protected from alveolar
bone loss in experimental periodontitis [
Numerous studies attempted to delineate the role of T
helper (Th) cell subsets in human periodontitis by
examining the cytokine mRNA levels by RT-PCR, flow
cytometry, and immunohistochemistry. However, those studies
are incoherent in terms of Th1 and Th2 cytokine
expression, although the Th17 cytokines are consistently
]. The current study revealed that the levels
of Th1 cytokines IFNG and IL12 did not change between
healthy and periodontitis-affected gingival samples while
that of TNF slightly increased in periodontitis (Additional
file 13: Table S10). The Th2 cytokines IL10 and IL33
remained unaltered in periodontitis patients. Interestingly,
Th17 cytokines IL6, IL23A, and IL17C significantly
increased in gingival tissues from periodontitis patients
compared with those of healthy control, supporting the concept
of Th17 cells as crucial mediators of inflammation,
although it is still controversial whether these cells contribute
to tissue destruction or protection in periodontitis [
Alternative splicing of genes contribute to the diversity of
proteome as well as genome evolution, control of
developmental processes, and physiological regulation of various
biological systems [
]. Not surprisingly, dysregulation of
alternative splicing is often linked to various human
diseases such as cancer, metabolic, neurological, and skeletal
]. However, alternative splicing events in the
context of periodontitis has rarely been investigated. The
current study uncovered significant differential alternative
splicing events in BCL2A1 and FN1. BCL2A1 is a target
gene of NF-kB, implicated in the survival of leukocytes
thereby inflammation [
]. However, the role of alternative
splicing on the activity of the protein has not been
suggested until the present. Interestingly, recent discovery
showed that BCL2A1 was increased not only in
periodontitis but also in systemic diseases such as cardiovascular
diseases and ulcerative colitis [
]. Therefore, research
regarding the multiple layers of regulatory mechanisms
including mRNA expression and alternative splicing of
BCL2A1 are required to fully understand the role of this
gene during the pathogenesis of periodontitis.
Parkar et al. previously suggested that FN1 is
differentially spliced in periodontitis [
]. Interestingly, the
authors reported exon skipping of both EDA and EDB
domain in periodontitis, while the current study showed
conspicuously increased inclusion of EDB domain.
Although whether these differences originated from the use
of periodontal ligament [
] versus gingival tissues (the
present study) yet to be cleared, it would be of great
interest to fully identify the role of fibronectin isoforms in the
pathogenesis of periodontitis considering the suggested
role of EDA- and EDB-containing isoforms of fibronectin
during embryonic development and tissue repair [
In conclusion, the current study presented novel gene
expression profiles as well as alternative splicing in
gingival tissues from periodontitis patients by RNA
sequencing experiments. Considering its effectiveness for whole
transcriptome analysis, the use of RNA sequencing in
periodontitis research would facilitate the elucidation of
Additional file 1: Table S1. The characteristics of patients involved in
the current study. The information on age, gender, and disease severity is
given in this table. (DOCX 54 kb)
Additional file 2: Table S2. Primer sequences used for the real-time
RT-PCR validation of RNA sequencing differential gene expression results.
The primer sequences are given in this table. (DOCX 89 kb)
Additional file 3: Table S3. Primer sequences used for the RT-PCR
validation of RNA sequencing alternative splicing results. The primer
sequences are given in this table. (DOCX 50 kb)
Additional file 4: Table S4. The full list of differentially expressed genes
in healthy and periodontitis tissues. This excel file contains the full list of
deferentially expressed genes. (XLSX 43 kb)
Additional file 5: Figure S1. Comparison of up-regulated genes in
periodontitis with those of the previous study by Davanian et al. The
Venn diagram shows the number of genes unique for each study and
that of commonly detected genes. (PDF 388 kb)
Additional file 6: Table S5. The GO term analysis of genes of up- and
down-regulated genes in periodontitis. This excel file contains the list of
the differentially expressed genes, categorized according to the GO
terms. (XLSX 21 kb)
Additional file 7: Table S6. The KEGG pathway analysis of genes of
upand down-regulated genes in periodontitis. This excel file contains the list
of the differentially expressed genes, categorized according to the KEGG
pathway enrichment analysis. (XLSX 11 kb)
Additional file 8: Figure S2. The expression levels of selected genes in
individual samples. The individual variation in gene expression was examined
by real-time RT-PCR analysis of individual healthy and periodontitis samples.
The p values of the Wilcoxon rank-sum test between healthy and periodontitis
groups are given in each graph. (PDF 566 kb)
Additional file 9: Table S7. The full list of differential alternative splicing
events in healthy and periodontitis tissues. This excel file contains the full list
of exons which were included or excluded in periodontitis. (XLSX 32 kb)
Additional file 10: Table S8. The GO term analysis of genes with
differential alternative splicing in periodontitis. This excel file contains the
list of the genes with differential alternative splicing, categorized
according to the GO terms. (XLSX 32 kb)
Additional file 11: Table S9. The KEGG pathway analysis of genes with
differential alternative splicing in periodontitis. This excel file contains the
list of the genes with differential alternative splicing, categorized
according to the KEGG pathway enrichment analysis. (XLSX 10 kb)
Additional file 12: Figure S3. The alternative splicing events in
individual samples. The individual variation in alternative splicing events
in FN1 and BCL2A1 was examined by RT-PCR analysis of individual healthy
and periodontitis samples. (PDF 419 kb)
Additional file 13: Table S10. The expression of Th1, Th2, and Th17
cytokines in healthy and periodontitis tissues. This excel file contains the
selected list of the Th1, Th2, and Th17 cytokine genes with their
expression levels in healthy and periodontitis tissues. (XLSX 37 kb)
EDA, extra domain A; EDB, extra domain B; FDR, false discovery rate; GO,
gene ontology; KEGG, kyoto encyclopedia of genes and genomes; PANTHER,
protein analysis through evolutionary relationships
This work was supported by grants from the National Research Foundation of
Korea (NRF) funded by the Ministry of Science, ICT, and Future Planning
(NRF-2012M3A9B6055415, NRF-2014R1A2A2A01004161, and NRF-2008-0062282 to
YL). This work was also supported by grants from the Korea Health Technology
R&D Project through the KHIDI, funded by the Ministry of Health & Welfare
(HI14C0175 to J-HL).
Availability of data and materials
The RNA sequencing data were submitted to the Gene Expression Omnibus
with ID GSE80715.
Y-GK designed the experiments, collected the tissue samples, analyzed the
data, and wrote the paper. MK analyzed the bioinformatics data and wrote the
paper. JHK, HJK, J-YK, J-WP, J-ML, and J-YS performed the experiments and
analyzed the data. J-HL designed and performed the experiments, analyzed
the bioinformatics data, and wrote the paper. YL designed and performed the
experiments, analyzed the data, and wrote the paper. All authors read and
approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
The study was approved by the institutional review board of the Kyungpook
National University Hospital with informed consent from all patients.
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