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CC BY 4.0 · Glob Med Genet 2022; 09(02): 159-165
DOI: 10.1055/s-0042-1743571
Original Article

A Computational Data Mining Strategy to Identify the Common Genetic Markers of Temporomandibular Joint Disorders and Osteoarthritis

Vijayashree Priyadharsini Jayaseelan 
1   Clinical Genetics Lab, Cellular and Molecular Research Centre, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Poonamallee High Road, Chennai, Tamil Nadu, India
,
Paramasivam Arumugam 
2   Molecular Biology Lab, Cellular and Molecular Research Centre, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Poonamallee High Road, Chennai, Tamil Nadu, India
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Abstract

Statement of Problem Prosthodontic planning in patients with temporomandibular joint disorders (TMDs) is a challenge for the clinicians.

Purpose A differential biomarker identification could aid in developing methods for early detection and confirmation of TMD from other related conditions.

Materials and Methods The present study identified candidate genes with possible association with TMDs. The observational study delineates genes from three datasets retrieved from DisGeNET database. The convergence of datasets identifies potential genes related to TMDs with associated complication such as osteoarthritis. Gene ontology analysis was also performed to identify the potential pathways associated with the genes belonging to each of the datasets.

Results The preliminary analysis revealed vascular endothelial growth factor A (VEGFA), interleukin 1 β (IL1B, and estrogen receptor 1 (ESR1) as the common genes associated with all three phenotypes assessed. The gene ontology analysis revealed functional pathways in which the genes of each dataset were clustered. The chemokine and cytokine signaling pathway, gonadotropin-releasing hormone receptor pathway, cholecystokinin receptors (CCKR) signaling, and tumor growth factor (TGF)-β signaling pathway were the pathways most commonly associated with the phenotypes. The genes CCL2, IL6, and IL1B were found to be the common genes across temporomandibular joint (TMJ) and TMJ + osteoarthritis (TMJ-OA) datasets.

Conclusion Analysis through computational approach has revealed IL1B as the crucial candidate gene which could have a strong association with bone disorders. Nevertheless, several immunological pathways have also identified numerous genes showing putative association with TMJ and other related diseases. These genes have to be further validated using experimental approaches to acquire clarity on the mechanisms related to the pathogenesis.

Keywords

computational approach - osteoarthritis - temporomandibular joint disorders - inflammation - genetic markers

Authors' Contributions

V.P.J.: conceptualization, data analysis, interpretation, and manuscript draft preparation. P.A.: manuscript preparation and editing. All authors read and approved their manuscript.




Publication History

Received: 27 December 2021

Accepted: 27 January 2022

Article published online:
09 March 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Lai YC, Yap AU, Türp JC. Prevalence of temporomandibular disorders in patients seeking orthodontic treatment: A systematic review. J Oral Rehabil 2020; 47 (02) 270-280
    CrossrefPubMedGoogle Scholar
  • 2 Athanasiou AE. Orthodontics and craniomandibular disorders. In: Bishara SE. ed. Textbook of Orthodontics. 2nd ed.. Philadelphia, PA: W.B. Saunders; 2003. -478 93
    Google Scholar
  • 3 Sangani D, Suzuki A, VonVille H, Hixson JE, Iwata J. Gene mutations associated with temporomandibular joint disorders: a systematic review. OAlib 2015; 2 (06) e1583
    CrossrefPubMedGoogle Scholar
  • 4 Kang JH. Transcriptomes in peripheral blood of young females with temporomandibular joint osteoarthritis. Sci Rep 2021; 11 (01) 8872
    CrossrefPubMedGoogle Scholar
  • 5 Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J. et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res 2020; 48 (D1): D845-D855
    PubMedGoogle Scholar
  • 6 Piñero J, Bravo À, Queralt-Rosinach N. et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res 2017; 45 (D1): D833-D839
    CrossrefPubMedGoogle Scholar
  • 7 Piñero J, Queralt-Rosinach N, Bravo À. et al. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxford) 2015; 2015: bav028
    CrossrefPubMedGoogle Scholar
  • 8 Stelzer G, Rosen N, Plaschkes I. et al. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics 2016; 54: 30.1 , 33
    CrossrefPubMedGoogle Scholar
  • 9 Szklarczyk D, Gable AL, Nastou KC. et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 2021; 49 (D1): D605-D612
    CrossrefPubMedGoogle Scholar
  • 10 Mi H, Thomas P. PANTHER pathway: an ontology-based pathway database coupled with data analysis tools. Methods Mol Biol 2009; 563: 123-140
    CrossrefPubMedGoogle Scholar
  • 11 Mi H, Ebert D, Muruganujan A. et al. PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API. Nucleic Acids Res 2021; 49 (D1): D394-D403
    CrossrefPubMedGoogle Scholar
  • 12 Brancher JA, Bertoli FMP, Michels B. et al. Is catechol-O-methyltransferase gene associated with temporomandibular disorders? A systematic review and meta-analysis. Int J Paediatr Dent 2021; 31 (01) 152-163
    CrossrefPubMedGoogle Scholar
  • 13 Nenna R, Turchetti A, Mastrogiorgio G, Midulla F. COL2A1 gene mutations: mechanisms of spondyloepiphyseal dysplasia congenita. Appl Clin Genet 2019; 12: 235-238
    CrossrefPubMedGoogle Scholar
  • 14 Lian C, Wang X, Qiu X. et al. Collagen type II suppresses articular chondrocyte hypertrophy and osteoarthritis progression by promoting integrin β1-SMAD1 interaction. Bone Res 2019; 7: 8
    CrossrefPubMedGoogle Scholar
  • 15 Wang XW, Fang W, Li YJ, Long X, Cai HX. Synovial fluid levels of VEGF and FGF-2 before and after intra-articular injection of hyaluronic acid in patients with temporomandibular disorders: a short-term study. Br J Oral Maxillofac Surg 2021; 59 (01) 64-69
    CrossrefPubMedGoogle Scholar
  • 16 Berger M, Szalewski L, Bakalczuk M, Bakalczuk G, Bakalczuk S, Szkutnik J. Association between estrogen levels and temporomandibular disorders: a systematic literature review. Przegl Menopauz 2015; 14 (04) 260-270
    Google Scholar
  • 17 Küchler EC, Meger MN, Ayumi Omori M. et al. Association between oestrogen receptors and female temporomandibular disorders. Acta Odontol Scand 2020; 78 (03) 181-188
    CrossrefPubMedGoogle Scholar
  • 18 Robinson JL, Cass K, Aronson R. et al. Sex differences in the estrogen-dependent regulation of temporomandibular joint remodeling in altered loading. Osteoarthritis Cartilage 2017; 25 (04) 533-543
    CrossrefPubMedGoogle Scholar
  • 19 Robinson JL, Gupta V, Soria P. et al. Estrogen receptor alpha mediates mandibular condylar cartilage growth in male mice. Orthod Craniofac Res 2017; 20 (1, suppl 1): 167-171
    CrossrefPubMedGoogle Scholar
  • 20 Dalewski B, Kamińska A, Białkowska K, Jakubowska A, Sobolewska E. Association of estrogen receptor 1 and tumor necrosis factor α polymorphisms with temporomandibular joint anterior disc displacement without reduction. Dis Markers 2020; 2020: 6351817
    CrossrefPubMedGoogle Scholar
  • 21 Pinto Fiamengui LMS, Furquim BD, De la Torre Canales G. et al. Role of inflammatory and pain genes polymorphisms in temporomandibular disorder and pressure pain sensitivity. Arch Oral Biol 2020; 118: 104854
    CrossrefPubMedGoogle Scholar
  • 22 Almeida LE, Pierce S, Zacharias J. et al. Immunohistochemical analysis of IL-1 beta in the discs of patients with temporomandibular joint dysfunction. Cranio 2017; 35 (04) 233-237
    CrossrefPubMedGoogle Scholar
  • 23 Ballaz S. The unappreciated roles of the cholecystokinin receptor CCK(1) in brain functioning. Rev Neurosci 2017; 28 (06) 573-585
    CrossrefPubMedGoogle Scholar
  • 24 Florjański W, Orzeszek S. Role of mental state in temporomandibular disorders: a review of the literature. Dent Med Probl 2021; 58 (01) 127-133
    CrossrefPubMedGoogle Scholar
  • 25 Giacobini P, Wray S. Cholecystokinin directly inhibits neuronal activity of primary gonadotropin-releasing hormone cells through cholecystokinin-1 receptor. Endocrinology 2007; 148 (01) 63-71
    CrossrefPubMedGoogle Scholar
  • 26 Jayaseelan VP, Arumugam P. Exosomal microRNAs as a promising theragnostic tool for essential hypertension. Hypertens Res 2020; 43 (01) 74-75
    CrossrefPubMedGoogle Scholar
  • 27 Jayaseelan VP, Arumugam P. Dissecting the theranostic potential of exosomes in autoimmune disorders. Cell Mol Immunol 2019; 16 (12) 935-936
    CrossrefPubMedGoogle Scholar
  • 28 Jayaseelan VP. Emerging role of exosomes as promising diagnostic tool for cancer. Cancer Gene Ther 2020; 27 (06) 395-398
    CrossrefPubMedGoogle Scholar
  • 29 Paramasivam A, Vijayashree Priyadharsini J. Novel insights into m6A modification in circular RNA and implications for immunity. Cell Mol Immunol 2020; 17 (06) 668-669
    CrossrefPubMedGoogle Scholar
  • 30 Paramasivam A, Vijayashree Priyadharsini J, Raghunandhakumar S. N6-adenosine methylation (m6A): a promising new molecular target in hypertension and cardiovascular diseases. Hypertens Res 2020; 43 (02) 153-154
    CrossrefPubMedGoogle Scholar