Genetic Association ofMMP10,MMP14, andMMP16with Dental Caries

Lewis, Deyana D.; Shaffer, John R.; Feingold, Eleanor; Cooper, Meg; Vanyukov, Michael; Maher, Brion S.; Slayton, Rebecca L.; Willing, Marcia; Reis, Steven E.; McNeil, Daniel W.; Crout, Richard J.; Weyant, Robert J.; Levy, Steven M.; Vieira, Alexandre R.; Marazita, Mary L.

doi:10.1155/2017/8465125

Cited by 15 publications

(17 citation statements)

References 51 publications

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“…A similar genome‐wide association study investigating clusters of pit‐and‐fissure and smooth surface dental caries in the primary dentition did not identify any significant signals . Additional loci not meeting genome‐wide significance criteria but with multiple lines of supporting evidence requiring some attention, include MMP16 (rs2046315 and rs10429371), PKD2 (rs17013735, rs11938025, rs2725270) and SIBLING (rs2725233), MPPED2 and ACTN2, and TRAV4 . Although additional insights have been gained by gene set enrichment re‐analyses of a dental caries genome‐wide association study reported by Wang et al, the recent, large‐scale consortium meta‐analysis that included clinical and self‐reported data (including denture use) among half a million individuals, discovered 47 novel loci for adult dental caries …”

Section: Genomics Of Traditional Clinical Definitions Of Oral and Dental Diseasementioning

confidence: 98%

Genomics of periodontal disease and tooth morbidity

Morelli

Agler

Divaris

2019

Periodontology 2000

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In this review we critically summarize the evidence base and the progress to date regarding the genomic basis of periodontal disease and tooth morbidity (ie, dental caries and tooth loss), and discuss future applications and research directions in the context of precision oral health and care. Evidence for these oral/dental traits from genome‐wide association studies first emerged less than a decade ago. Basic and translational research activities in this domain are now under way by multiple groups around the world. Key departure points in the oral health genomics discourse are: (a) some heritable variation exists for periodontal and dental diseases; (b) the environmental component (eg, social determinants of health and behavioral risk factors) has a major influence on the population distribution but probably interacts with factors of innate susceptibility at the person‐level; (c) sizeable, multi‐ethnic, well‐characterized samples or cohorts with high‐quality measures on oral health outcomes and genomics information are required to make decisive discoveries; (d) challenges remain in the measurement of oral health and disease, with current periodontitis and dental caries traits capturing only a part of the health‐disease continuum, and are little or not informed by the underlying biology; (e) the substantial individual heterogeneity that exists in the clinical presentation and lifetime trajectory of oral disease can be identified and leveraged in a precision medicine framework or, if unappreciated, can hamper translational efforts. In this review we discuss how composite or biologically informed traits may offer improvements over clinically defined ones for the genomic interrogation of oral diseases. We demonstrate the utility of the results of genome‐wide association studies for the development and testing of a genetic risk score for severe periodontitis. We conclude that exciting opportunities lie ahead for improvements in the oral health of individual patients and populations via advances in our understanding of the genomic basis of oral health and disease. The pace of new discoveries and their equitable translation to practice will largely depend on investments in the education and training of the oral health care workforce, basic and population research, and sustained collaborative efforts..

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Section: Genomics Of Traditional Clinical Definitions Of Oral and Dental Diseasementioning

confidence: 98%

Genomics of periodontal disease and tooth morbidity

Morelli

Agler

Divaris

2019

Periodontology 2000

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“…The first linkage studies in caries were performed in 2008 [ 32 ] and the first genome-wide association studies for early childhood caries were published in 2011 [ 33 ]. Abbasoğlu et al [ 10 ] were the first to correlate environmental and genetic factors in ECC in Turkish 2–5-year-olds, while Lewis et al [ 34 ] considered several single nucleotide polymorphisms, however they were not included in the final caries prediction meta-analysis. Another technical obstacle in any caries prediction model implementation in practice is an almost total lack of replication and validation studies in independent populations [ 13 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

“…Matrix metalloproteinases (MMPs) play an important role in early tooth development by regulating ameloblast maturation and formation of enamel. Several types of MMPs have been described to be involved in dentin collagen degradation and dental caries lesion progression [ 12 , 34 , 39 , 40 , 41 , 42 ]. MMP16 rs10429371 was previously associated with caries in white adults [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks

Zaorska

Szczapa

Borysewicz−Lewicka

et al. 2021

Genes

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Background: Several genes and single nucleotide polymorphisms (SNPs) have been associated with early childhood caries. However, they are highly age- and population-dependent and the majority of existing caries prediction models are based on environmental and behavioral factors only and are scarce in infants. Methods: We examined 6 novel and previously analyzed 22 SNPs in the cohort of 95 Polish children (48 caries, 47 caries-free) aged 2–3 years. All polymorphisms were genotyped from DNA extracted from oral epithelium samples. We used Fisher’s exact test, receiver operator characteristic (ROC) curve and uni-/multi-variable logistic regression to test the association of SNPs with the disease, followed by the neural network (NN) analysis. Results: The logistic regression (LogReg) model showed 90% sensitivity and 96% specificity, overall accuracy of 93% (p < 0.0001), and the area under the curve (AUC) was 0.970 (95% CI: 0.912–0.994; p < 0.0001). We found 90.9–98.4% and 73.6–87.2% prediction accuracy in the test and validation predictions, respectively. The strongest predictors were: AMELX_rs17878486 and TUFT1_rs2337360 (in both LogReg and NN), MMP16_rs1042937 (in NN) and ENAM_rs12640848 (in LogReg). Conclusions: Neural network prediction model might be a substantial tool for screening/early preventive treatment of patients at high risk of caries development in the early childhood. The knowledge of potential risk status could allow early targeted training in oral hygiene and modifications of eating habits.

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“…However, in the case of SNP MMP16 rs10429371, C allele was more frequent in RAS patients than in controls and CC + CT genotype carriers of this studied polymorphism had marginally significantly associated higher risk of developing RAS in comparison with TT homozygotes in our population. Intragenic polymorphisms rs10429371 is located upstream of MMP16 and receptor‐interacting serine‐threonine kinase 2 ( RIKP2 or RIP2 ) genes and it has not been elucidated yet, whether this variant has an impact on the expression of either MMP16 or RIPK2 . Regarding the development of RAS, the membrane‐type MMP16 (or MT3‐MMP) is involved in extracellular matrix remodelation, since its substrates include type III collagen, gelatin, or fibronectin .…”

Section: Discussionmentioning

confidence: 99%

Gene variability in matrix metalloproteinases in patients with recurrent aphthous stomatitis

Slezaková

Linhartová

Bártová

et al. 2020

J Oral Pathology Medicine

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Background The development of recurrent aphthous stomatitis (RAS), inflammatory disease of oral mucosa, is influenced by both environmental and genetic factors. The aim of this study was to investigate polymorphisms located in seven genes coding different types of matrix metalloproteinases (MMPs)—collagenases (MMP1, MMP8, and MMP13), gelatinases (MMP2 and MMP9), stromelysin (MMP3), and membrane‐type metalloproteinase (MMP16) in patients with RAS and healthy controls. Methods Totally, 223 subjects were included in this case‐control study and their detailed anamnestic, clinical, and laboratory parameters were recorded. Seventy‐seven patients with RAS and 146 controls were genotyped for seventeen polymorphisms in the MMPs genes using the real‐time polymerase chain reaction (PCR) or PCR with restriction analysis. Results Allele, genotype, and haplotype frequencies of the studied polymorphisms between RAS patients and controls were similar, except for allele distributions of MMP1 rs1144393, MMP9 rs3918242, and MMP16 rs10429371, which were different between patients with RAS and healthy controls (P = .023, P = .049 and P = .025, all Pcorr > 0.05, respectively). Moreover, the comparison of genotype frequencies (TT vs CC + CT) of the MMP16 rs10429371 variant showed a marginally significant difference between RAS patients and controls (P = .05, Pcorr > 0.05, OR = 1.68, 95% CI = 0.95‐2.98). Conclusions No significant relationship between investigated polymorphisms in seven MMPs genes and RAS development in the Czech population was observed in this study.

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Genetic Association ofMMP10,MMP14, andMMP16with Dental Caries

Cited by 15 publications

References 51 publications

Genomics of periodontal disease and tooth morbidity

Genomics of periodontal disease and tooth morbidity

Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks

Gene variability in matrix metalloproteinases in patients with recurrent aphthous stomatitis

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