Keywords computational approach - osteoarthritis - temporomandibular joint disorders - inflammation
- genetic markers
Introduction
Clinical Implications
Identification of molecular biomarkers and the pathway associated with the same is
an important strategy to design and develop prognostic tools for complex disorders.
Assessing markers could be a herculean task when it comes to human disorders due to
heterogeneity and complexity of the disorders. Computational analysis could aid in
delineating the markers associated with a disease by predicting interactions, providing
a clue toward understanding the pathogenesis of the disease.
Temporomandibular disorders (TMDs) include a heterogeneous group of conditions emanating
from the masticatory muscles and mandibular joints resulting in dysfunction and pain.
The prevalence of TMDs in the general population is recorded as 21 to 73%, with painful
TMD exhibiting a greater prevalence of 65.7% over nonpainful forms reported to be
40.8%. The gender-wise assessment of prevalence of TMD has been recorded as high as
72.4% in females than in male (10.6–68.1%) population. The incidence of the disorder
was observed in adults over 18 years of age than the younger ones.[1 ] Although reports did not provide suggestive evidence on the association between
TMDs and malocclusion, a few occlusal traits have been implicated in TMDs. The etiology
of factors associated with TMDs can be classified as initiating, predisposing, and
perpetuating factors which cause onset of TMDs, increase the risk of TMDs, and enhance
progression of TMDs by interfering with the healing process. Although the etiology
of TMDs is multifactorial with the involvement of physiological, psychosocial, neurological
and cognitive factors, heredity, or genetic factors play a very important role in
the predisposition of the disorder. Despite the fact that there are numerous reports
related to TMDs based on epidemiology, gender, clinical presentations, and others,
only a very few records are available to support this hereditary predisposition to
TMDs.
To bridge the gaps and address the lacunae that lies in the temporomandibular joint
(TMJ) research related to genetic predisposition, the authors have embarked on an
exploration employing computational tools to identify the “hub of genes”' which are
candidate genes whose deregulation precipitates into TMD phenotypes. Additionally,
osteoarthritis (OA) is considered to be one of the major risk factors often related
to TMDs. Orthodontists report a higher incidence of TMDs in their patients suffering
from OA.[2 ] OA is a progressive disease commonly found in women who lead to disturbances in
health and day-to-day performances due to severe functional decline. A very recent
study by Kang demonstrated the immunological pathway related to TMJ + OA (TMJ-OA)
in young females. The RNA-sequencing analysis conducted by recruiting 24 females with
TMJ-OA and 11 age- and gender-matched control group revealed nearly 41 differentially
expressed genes. These genes were further clustered into 16 ontology terms. A hub
of six candidate genes, that is, HLA-C, HLA-F, CXCL8, IL11RA , IL13RA1 , and FCGR3B were identified. They were shown to be regulating pathways related to inflammation,
autoimmunity, and dysregulation of T-cell functions.[3 ]
[4 ] Thus, clinical experimental procedures coupled with in silico analysis pave way
in selecting the most appropriate gene clusters associated with the disease phenotype,
thereby enabling the development of therapeutic leads targeting these gene products.
The authors hypothesize that TMDs and OA could have common pathways culminating in
the degenerative process. Hence a comprehensive assessment of genes related to the
phenotypes TMJ, TMJ-OA, and generalized OA (G-OA) was performed for deriving a better
understanding of the converging molecular mechanisms underlying these closely and
commonly occurring disorders. Further, the putative factors behind increased prevalence
of OA and TMJ-related OA have been discussed in terms of genetic predisposition and
underlying pathways. The present study is the first of its kind, wherein computational
tools were used to assess data to identify those hub of genes and their encoded protein
products which could be considered for targeted therapy. The preliminary data have
been analyzed in different dimensions to tag high priority genes associated with susceptibility
to TMJs.
Methods
Collection of Data
DisGeNET is a user friendly platform which is a collection of publicly available information
on genes and variants associated with human diseases.[5 ]
[6 ]
[7 ] It is an integrated domain with data curated from the Genome Wide Association Studies
(GWAS), animal models, and text-mining approaches. Also, additional metrics are provided
to enable prioritization of genotype and phenotype associations. The present version
(v7.0) comprises 1,134,942 gene-disease associations (GDAs) which include 21,671 genes
encompassing 30,170 diseases or traits and 3,69,554 variant associations (VDAs).
Cytogenetic Location of Genes
GeneCards is an integrative platform which provides information on all predicted genes
of the human genome. The database integrates the information from approximately 150
web sources, hosting genomic, transcriptomic, proteomic, genetic functional, and clinical
information about the genes. The frequency of genes in each of the dataset across
the genome was presented as a graph to denote the involvement of a specific chromosome
with the type of disorder.[8 ]
Protein Interaction Analysis
The STRING (v11.0) database hosts a range of known and predicted protein–protein interactions.
The interactions may be of two main types: direct (physical) and indirect (functional)
associations. The information gathered in the database is derived from predictions
performed using computational tools, text mining, and data mining from other primary
databases.[9 ]
Gene Ontology Analysis
Gene ontology analysis was performed by using the PANTHER database (v16.0; Protein
ANalysis THrough Evolutionary Relationships). User-defined query lists of genes from
each of the datasets were fed as a batch to identify the functional classification
of the genes. Classification based on pathway was conducted to identify the potential
pathways associated with the genes.[10 ]
[11 ]
Results
DisGeNET Analysis
The three different datasets unique for each phenotype, that is, (1) TMJ (C0039494),
(2) TMJ-OA (C4552513), and (3) G-OA (C1384584) datasets with 83, 32, and 63 genes,
respectively, were selected for the present analysis. The catechol-O-methyltransferase
(COMT; TMJ), interleukin 1 β (IL1B; TMJ-OA), and collagen type-II α 1 chain (COL2A1;
G-OA) were found to exhibit high-gene-disease-association score for the corresponding
dataset analyzed. The preliminary analysis revealed common genes associated with all
three datasets were vascular endothelial growth factor A (VEGFA ), IL1B , and estrogen receptor 1 (ESR1 ; [Table 1 ]; [Fig. 1 ]). Similarly, unique collections of genes specific for each of the phenotypes were
also identified ([Table 1 ]).
Table 1
The number of genes implicated in each of the category studied and common genes identified
among different combinations of datasets
Names
Total number of genes
Genes
G-OA, TMJ, TMJ-OA
3
VEGFA, IL1B, ESR1
G-OA, TMJ
2
NOTCH1, CALCA
TMJ, TMJ-OA
6
TNF, TGFB1, LEP, CCL2, HIF1A, IL6
G-OA, TMJ-OA
5
DNMT3B, MMP1, SMAD3, GDF5, BMP2
TMJ
72
EREG, SHOX2, MTOR, BECN1, GRHL3, HTR2A, DCTN4, TXNRD2, PITX2, ESRRB, TNFRSF11B, CCL5
MAP1LC3A, KHDRBS1, LTA, ADRB2, CMYA5, TTN, CAPN3, SLC6A4, GLI3, MRAS, AMY1C, TAL1,
DRD2, NGF, CT, AMY1A, TRIP12, ELN, TIMP1, CTRL, CALCR, GTF2H1, NUP62, MAP1LC3B, ENPP1,
ESR2, VCP, SCPEP1, BAHCC1, GSTM1, ANKK1, DMD, LTBP3, DRD4, CRP, RARRES2, SQSTM1, GSTT1,
ANKH, MTHFR, COL4A3, PTH, TNFRSF11A, ITGAL, CXCL8, EGR1, COMT, MAP4K3, HPGDS, EGFR,
NFKB1, DKK1 IL1RN, BTF3P11, CCDC88A, KRT7, SCLY, MMP9, AMY1B, IL11
G-OA
53
VDR, BMP4, POLDIP2, HCK, SFRP4, RNF19A, GNL3, GRAP2, MEFV, CCL8, DNASE1L3, TNFAIP6,
HFE BID, COL9A1, PART1, IGF1, MIR146A, MAPK14, ROM1, PHB, CLU, PITX1, CNGB1, COL2A1,
PTH2R, MMP13, TPSG1, HLA-DRB1, IL1A, LRRC32, MMP3, SERPINA3 PTK2B, HLA-B, CRK, MICAL3,
NAMPT, MAPKAPK2, HLA-A, AIMP2, AHSA1, BMPR1A, MAPK1, TLR10, SPP1, DNMT1, SERPINA1,
GHR, CTNNB1, GH1, NODAL, MGP
TMJ-OA
18
IL17A, NPAS2, SSRP1, DMP1, PER2, SETD2, MIR140, CHST11, HOTAIR, MAPK3, HES5, CCR5,
CCL20, IL22, TNFSF11, ESRRG, CD68, RUNX2
Abbreviations: G-OA, generalized-osteoarthritis; TMJ, temporomandibular joint; TMJ-OA,
temporomandibular joint-osteoarthritis.
Fig. 1 The Venn diagram showing common genes associated with both TMJ, G-OA, and TMJ-OA.
G-OA, generalized-osteoarthritis; TMJ, temporomandibular joint; TMJ-OA, temporomandibular
joint-osteoarthritis.
Frequency Distribution of Genes on the Chromosomes
The frequency distribution of genes from different datasets was identified using GeneCards
and clustered based on their location on each of the 23 chromosomes ([Figs. 2A–C ]). Eleven genes on chromosome 1 (TMJ), 5 genes on chromosome 6 (TMJ-OA), and 8 genes
on chromosome 6 (G-OA) were found to be present in the respective datasets. In line
with the observation made, chromosome 6 was found to harbor more genes associated
with generalized OA and TMJ. Cumulative effect of variations in these genes might
increase the susceptibility to TMDs.
Fig. 2 (A ) The distribution of genes across the genome in the TMJ dataset (C0039494), (B ) TMJ-OA (C4552513), and (C ) G-OA (C1384584). G-OA, generalized-osteoarthritis; TMJ, temporomandibular joint;
TMJ-OA, temporomandibular joint-osteoarthritis.
Protein Interaction Analysis
The analysis using STRING database returned protein interaction network for each of
the dataset investigated. The TMJ dataset had two gene clusters with the majority
of genes interacting in one cluster and amylase α 1A (AMY1A ), 1B (AMY1B ), and 1C (AMY1C ) forming a second cluster interacting with each other. Seven other genes, such as
GRHL3, MRAS, SHOX2, DCTN4, SCPEP1, CTRL , and BAHCC1 , were found to remain as independent entities without showing any interactions with
either of the clusters ([Fig. 3A ]). Similarly TMJ-OA had two gene clusters with SETD2 (SET domain-containing 2, histone lysine methyltransferas) and SSRP1 (structure-specific recognition protein 1), forming a secondary cluster ([Fig. 3B ]). The G-OA dataset showed a single cluster with majority of genes and a few genes
which were found to act independently, that is, POLDIP2, BID, DNASE1L3, LRRC32, TPSG1, GNL3, AIMP2, MICAL3 , and RNF19A ([Fig. 3C ]).
Fig. 3 (A ) The protein network interaction of genes in the TMJ dataset (C0039494), (B ) TMJ-OA (C4552513), and (C ) G-OA (C1384584). G-OA, generalized-osteoarthritis; TMJ, temporomandibular joint;
TMJ-OA, temporomandibular joint-osteoarthritis.
Gene Ontology Analysis
The gene ontology analysis identified 36 pathways in the TMJ set of genes with the
highest cluster being found in the inflammation mediated by chemokine and cytokine
signaling pathway with eight genes, that is, CCL2, ITGAL, CXCL8, CCL5, NFKB1, LTA, IL6 , and IL1B . The second highest cluster was that of gonadotropin-releasing hormone receptor pathway,
comprising six genes, that is, MAP4K3, EGFR, EGR1, PITX2, TGFB1 , and DRD2. Similarly, on analysis of TMJ-OA dataset, a total of 51 pathways were revealed of
which the highest cluster was observed as possessing six genes of the inflammation
mediated by chemokine and cytokine signaling pathway, CCR5, CCL2, CCL20, MAPK3, IL6 , and IL1B , and the second highest being tumor growth factor (TGF)-β signaling pathway with
five genes, that is, BMP2, SMAD3, GDF5, TGFB1 , and MAPK3. Subsequently, the observation derived from G-OA dataset revealed 35 pathways with
the highest cluster with 10 genes (BMP2, BMP4, SMAD3, BMPR1A, PITX1, CTNNB1, MAPK1, IGF1, MAPK14, and PTK2B ) belonging to gonadotropin-releasing hormone receptor pathway. The second highest
cluster included two pathways, the cholecystokinin receptors (CCKR) signaling (MMP3, CLU, CRK, CTNNB1, CALCA, MAPK1, MAPK14, and PTK2B ) and TGF-β signaling pathway (BMP2, BMP4, SMAD3, GDF5, BMPR1A, MAPK1, MAPK14, and NODAL ) with eight genes each ([Fig. 4A–C ]).
Fig. 4 The gene ontology results depicting the cluster of genes contained in each of the
pathway in the (A ) TMJ (C0039494), (B ) TMJ-OA (C4552513), and (C ) G-OA (C1384584) datasets. G-OA, generalized-osteoarthritis; TMJ, temporomandibular
joint; TMJ-OA, temporomandibular joint-osteoarthritis.
Discussion
The etiology of TMD is multifactorial. Several factors, such as trauma, osteoarthritis,
neuromuscular, mechanical displacement, psychophysiological, psychosocial, and hereditary
components, have been identified as causes of TMD.[1 ] Among the predisposing, initiating, and perpetuating factors, a predisposing factor
with a special emphasis on the genetic component influencing the susceptibility of
TMD has been considered in the present study. Computational analysis is a growing
field where even a small piece of information can contribute immensely to the research
intended to alleviate the discomforts experienced by patients suffering from TMDs.
The present study is one such attempt conducted to identify high priority genes in
close association with TMD. The DisGeNET analysis showed COMT (TMJ), IL1B (TMJ-OA), and COL2A1 (G-OA) to exhibit high gene-disease association scores in each of the dataset investigated.
Meta-analysis conducted by Brancher and colleagues identified COMT to be significantly associated with TMD. They reported two polymorphisms rs6269 and rs9332377 to be related to myofascial pain or myofascial pain with painful TMD respectively.[12 ] COMT is an enzyme which is involved in the catalysis of catecholamines, such as
dopamine, epinephrine, and norepinephrine. The forms of COMTs, that is, soluble COMT
and membrane bound COMT, are encoded by the gene located at the cytogenetic loci 22q11.21.
Numerous polymorphisms in the coding, intergenic, and promoter regions of the COMT
gene have been found to be associated with stress and painful conditions. The distribution
of genes across different datasets revealed chromosomes 1 and 6 to harbor the highest
frequency of genes associated with the disease phenotypes. The high scoring gene in
TMJ-OA group was IL1B which encodes cytokines that participate in immunoregulation and inflammatory processes.
The COL2A1 gene encoding type-II collagen is located at 12q13.1-q13.2.[13 ] Although once considered to be encoding for structural component, it was recently
established that COL2A1 can act as an extracellular signaling molecule capable of
suppressing chondrocyte hypertrophy.[14 ]
The intersection of 3 datasets (
http://bioinformatics.psb.ugent.be/cgi-bin/liste/Venn/calculate_venn.htpl
) revealed common genes associated with all three datasets which were VEGFA , IL1B , and ESR1 ([Fig. 1 ]). The formation of small blood vesicles within the joint cavity is most frequently
observed in case of synovitis. A recent study by Wang and colleagues provided evidence
about the involvement of VEGF and FGF-2 in inducing angiogenesis in joints. The study
findings also suggested that administration of intra-articular dose of hyaluronic
acid (HA) might alleviate synovotis by targeting VEGF, whose level was found to be
high before treatment and reduced significantly after treatment with HA.[15 ]
Cytogenetic location of the genes of TMJ dataset, that is, CRP, AMY1A, AMY1B, AMY1C, GSTM1, GRHL3, MTOR, MTHFR, NGF, TAL1 , and KHDRBS1 were found on chromosome 1 ([Fig. 2A ]). On the other hand, IL17A, RUNX2 , and TNF were all clustered on chromosome 6 in TMJ-OA ([Fig. 2B ]). The genes COL9A1, HFE, HLA-A, HLA-B, DRB1 , and MAPK14 were found to be on chromosome 6 ([Fig. 2C ]). Interestingly, ESR1 and VEGFA were found to be the overlapping genes in TMJ-OA and G-OA sets. Although the heritability
data for TMJ is not well established in different populations, a consolidated data
on the involvement of chromosomes and genes have been addressed in the present study.
As with TMJ and associated disorders being multifactorial in nature, cumulative effects
of all the variants occurring in the genes mentioned above can contribute toward the
magnitude of the disorder as TMJ with mild-to-moderate symptoms or more severe forms
of TMJ eventually leading to serious disabilities.
Finally, the gene ontology analysis revealed one pathway which was common for TMJ
and TMJ-OA dataset and one pathway common to TMJ-OA and G-OA which was the TGF-β signaling
pathway. Gonadotropin-releasing hormone receptor pathway was found to be common between
TMJ and G-OA. The other pathway of significant importance was the CCKR signaling pathway.
The protein interaction network analysis provided information on gene clusters in
two datasets, TMJ and TMJ-OA, whereas the G-OA showed a single cluster with the highest
number of independent genes alongside the cluster as depicted in [Fig. 3C ]. Gender predilection is a common observation in OA, where females show a greater
prevalence than males. The possible reason for greater prevalence can be attributed
to hormone estrogens. Estrogens are known to regulate several biological processes
including reproduction, differentiation, development, and cell growth.[16 ] Cell signaling mediated by estrogens requires two types of receptors, (1) ERa and
(2) ERb, encoded by ESR1 and ESR2 genes.[17 ] The ERa is expressed in mandibular condylar cartilage which describes their possible
role in the development of TMDs.[18 ]
[19 ] A very recent study by Dalewski demonstrated the association of rs1643821 ESR1 gene polymorphism and rs1800629 TNF-α gene polymorphism with disc displacement in TMD. The study recruited 124 Caucasian
patients with TMDs and 126 control patients free of TMDs. Among the two polymorphisms,
rs1643821 (ESR1) exhibited a significant association conferring susceptibility to anterior disc displacement
observed in TMDs.[20 ] Pinto Fiamengui and team assessed the role of inflammatory and pain-related gene
polymorphisms in Brazilian patients who presented with TMDs. About 131 TMDs patients
and 1347 normal patients were included in the cross-sectional study. Allelic discrimination
among variants of different genes COMT (rs4680 ), IL-1β (rs1143634 ), IL6 (rs1800795 ), and TNFA (rs1800629 ) were assessed. Of the four Single nucleotide polymorphisms (SNPs), TNF- α and IL6
showed a significant association with TMD and sensitivity to pain, respectively.[21 ]
About 1.2, 6.25, and 14.28% of the total genes in TMJ, TMJ-OA, and G-OA datasets,
respectively, were found to act independently and were not involved in the interactions.
Genes of the G-OA lying out of the cluster could make the phenotype more diverse involving
pathways which are yet to be explored. As reported by researchers worldwide, inflammatory
cytokine and chemokine pathways serve as a converging point between TMJ and TMJ-OA
phenotypes. As anticipated, the gonadotropin-releasing pathway was common between
TMJ and G-OA which could explain the risk of TMJ and arthritis in females rather than
in male population. Induction of inflammatory pathways is a common observation in
TMDs. Chronic inflammatory process driven by chemokines and cytokines are the well-known
factors implicated in the resorption of bones and damage to adjoining tissues. Among
all the cytokines involved, IL1B has been found to show a greater frequency of occurrence
in each of the analyses made. Almeida and colleagues determined the association of
IL1B expression with TMJ employing immunohistochemical analysis. A total of 39 samples
including eight control and 31 obtained from patients with anterior disc displacement
with and without reduction. A statistically significant association was observed between
the two groups implying the fact that IL1B possesses a major role as a precipitating
factor in TMJ.[22 ]
Amidst all the observations made in the present study, the authors demonstrate an
interesting network of genes involved in the CCKR signaling pathway. The pathway included
MMP3, CLU, CRK, CTNNB1, CALCA, MAPK1, MAPK14 , and PTK2B . When most of the candidate genes were thought to be involved in physiological, immunological,
and metabolic functions, the CCKR pathway has been involved in a psychological process
that is mostly implicated as one of the risk factors in TMDs. Stress, fear, and anxiety
are known to be controlled by CCK. The CCK1 receptor binds to sulphated CCK peptide
hormone, widely distributed in the gastrointestinal tract and brain of mammals.[23 ] A recent review compiled by Florjański and Orzeszek provided substantial evidence
supporting the fact that psychological derangements play an inherent role in the development
and progression of TMDs.[24 ] Earlier studies on the CCK showed that it inhibits gonadotropin releasing cell's
neuronal activity through CCKRs. Thus, it is clear that a trio of psychological, hormonal,
and genetic factors makes a female more vulnerable to G-OA phenotype.[25 ]
Despite the fact that the present study is designed based on data generated from experimental
procedures, datamining, and text mining, there could be genes or pathways which have
not been explored to a greater extent. Second, since the results are not based on
a specific population or ethnic group, the observation made on potential candidate
genes can differ across geographical locations, as other extraneous factors, such
as gender, habits, dwelling place, income, level of education, food, life style, and
others, might act as potential modifiers of the hereditary factors. Third, information
on more intricate mechanisms, such as exosomes,[26 ]
[27 ]
[28 ] RNA modifiers, epigenetic, and epitranscriptome marks[29 ]
[30 ] implicated in other inflammatory diseases are not dealt with in a comprehensive
manner. Further, the phenotype TMD includes a heterogeneous group of symptoms. TMD's
classifications are intra-articular, extra-articular, musculoskeletal, and psychological
problems, and the etiology of each classification is different; hence, more specific
experimental approach should be designed to identify a panel of genes to be analyzed
in a specific type to recognize vital genetic markers contributing to susceptibility
of such disorders.
Conclusion
Genetic basis of TMDs still remains to be a debatable topic. Taken together, the present
study reveals a hub of genes based on cluster and pathway analysis to be associated
with TMDs. These potential genes include, COMT, IL1B, COL2A1, VEGFA , and ESR1. Additionally, genes related to inflammatory pathways do have a vital role to play
in the development of the disorder. Among all the data analyzed, it is of interest
to know that IL1B can be regarded as one of the major precipitating factors for TMJ and OA. Having
addressed all the pros and cons of the present study, the authors suggest data generated
through experimental evidence to prove the association IL1B with this disease phenotype. Advancements in next generation sequencing analysis,
whole genome and exome sequencing, RNA sequencing, transcriptomics, and epitranscriptomics
studies are sure to provide evidence-based reports on the heritability index of TMJs
and related disorders.
