Association of NOD2 Mutations with Aggressive Periodontitis

Abstract

Aggressive periodontitis (AgP) is characterized by rapid alveolar bone destruction and tooth loss early in life, and its etiology remains unclear. To explore the genetic risk factors of AgP, we performed genome-wide single-nucleotide polymorphism genotyping for identity-by-descent mapping and identified 32 distinct candidate loci, followed by whole exome sequencing with 2 pedigrees of AgP consisting of 3 cases and 1 control in 1 family and 2 sibling cases in the other. After variant filtering procedures and validation by targeted Sanger sequencing, we identified 2 missense mutations at 16q12 in NOD2 (p.Ala110Thr and p.Arg311Trp), which encodes nucleotide-binding oligomerization domain protein 2. We further examined 94 genetically unrelated AgP patients by targeted sequencing of NOD2 and found that 2 patients among them also carried the p.Arg311Trp variant. Furthermore, we found 3 additional missense mutations in this gene (p.His370Tyr, p.Arg459Cys, and p.Ala868Thr). These mutations either had not been previously observed or are extremely rare (frequency <0.001) in Asian populations. NOD2 plays a crucial role in innate immunity as an intracellular receptor initiating nuclear factor κB-dependent and mitogen-activated protein kinase-dependent gene transcription. These results demonstrated NOD2 as a novel gene involved in AgP.

Genomics of periodontal disease and tooth morbidity

“…Other notable investigations have used genotyping arrays with exome content, whole‐exome sequencing, RNA expression/multi‐omics profiling, and several post hoc bioinformatics and machine learning classification approaches to study the molecular and genomic basis of periodontitis. The study of gene expression and other more functional approaches (as opposed to genetic association studies) is a key complement to genome‐wide association studies.…”

“…In a recent report, Kitagaki and colleagues used whole‐exome sequencing to identify GPR126 (lead marker: rs536714306) as a candidate genetic risk factor for aggressive periodontitis in a Japanese population. In another recent investigation, Sudo and colleagues used a two‐step approach in a family based study and used whole‐exome sequencing to identify novel mutations in the NOD2 gene, which is also associated with aggressive periodontitis.…”

Identification of genetic risk factors of aggressive periodontitis using genomewide association studies in association with those of chronic periodontitis

“…NOD2 SNP rs571102620 was identified as a common genetic variant in the first family and SNP rs104895427 was identified as a common genetic variant in the second family. Target exon sequencing in NOD2 was then performed using 94 genetically unrelated Japanese AgP patients and NOD2 SNPs, rs199858111 and rs765857594, and p.Ala868Thr, were absent or very (MAF ≤0.001) in this Asian population . However, this study only reported a heterozygous rare mutation, which was found in only two families with periodontitis.…”

Biosensor and Lab-on-a-chip Biomarker-identifying Technologies for Oral and Periodontal Diseases

“…More specifically, in patients with chronic periodontal conditions four genes (NIN, ABHD12B, WHAMM, and AP32) have been identified (Rhodin et al, 2014). Sanger sequencing revealed the association of NOD2 mutations in a Japanese population with aggressive periodontal disease (Sudo et al, 2017).…”

“…The complementary advances in information technology and sequencing devices in this decade have enabled analysis of vast amounts of Omics information, contributing to better understanding of disease activity to improve our prediction for disease onset and progression (Eicher et al, 2020). In recent years, various investigations were carried out using preclinical animal and translational Omics analyses to elucidate pathological mechanisms (Rhodin et al, 2014;Offenbacher et al, 2016;Sawle et al, 2016;Sudo et al, 2017;Maekawa et al, 2019;Shin et al, 2019). Since Omics probe different cellular layers, they are categorized at DNA, RNA, protein, or metabolite levels (Yugi et al, 2016).…”