The long-term goal of the proposed research is to be able to diagnose and predict periodontal disease before there are clinical symptoms of the disease. This addresses a significant health-related problem in that periodontal diseases are bacterial infections that affect over half of the US adult population and are responsible for half of all tooth loss in adults. Although it is known that there are marked differences in the microbial composition of subgingival plaque from healthy sites as compared to those sites with periodontal disease, the microbial profiles of healthy, pre-clinical sites that will progress to diseased sites are not known. The proposal is a case-controlled, longitudinal study in which progressing sites of new disease will be identified on the basis of microbial compositions or profiles. To accomplish this goal, microbial profiles will be determined by using two high-throughput technologies, namely 1) the Human Oral Microbe Identification Microarray (HOMIM) to rapidly identify the predominant oral microflora, and 2) 454 pyrosequencing to provide deep sequencing of a select subset of samples. In this grant, a biological model based on microbial compositions will be developed in order to predict periodontal disease progression. This model can be used to test new subject populations. Ultimately, the outcome of this research has great promise for bench-to-chairside applications where clinicians will be able to determine and treat those periodontal sites at risk for disease on the basis of microbial compositions.
Gum diseases are bacterial infections that affect over half of the US adult population and are responsible for half of all tooth loss in adults. The proposed research will determine if the bacterial composition of dental plaque can be used as an early warning signal for gum diseases. Based on this information, dentists can better treat and prevent these diseases.
|Krishnan, K; Chen, T; Paster, B J (2017) A practical guide to the oral microbiome and its relation to health and disease. Oral Dis 23:276-286|
|Tian, Na; Faller, Lina; Leffler, Daniel A et al. (2017) Salivary Gluten Degradation and Oral Microbial Profiles in Healthy Individuals and Celiac Disease Patients. Appl Environ Microbiol 83:|
|Mougeot, Jean-Luc C; Stevens, Craig B; Cotton, Sean L et al. (2016) Concordance of HOMIM and HOMINGS technologies in the microbiome analysis of clinical samples. J Oral Microbiol 8:30379|
|Belstrøm, Daniel; Holmstrup, Palle; Bardow, Allan et al. (2016) Temporal Stability of the Salivary Microbiota in Oral Health. PLoS One 11:e0147472|
|Heller, D; Helmerhorst, E J; Gower, A C et al. (2016) Microbial Diversity in the Early In Vivo-Formed Dental Biofilm. Appl Environ Microbiol 82:1881-8|
|Lourenço, Talita Gomes Baêta; Heller, Débora; Silva-Boghossian, Carina Maciel et al. (2014) Microbial signature profiles of periodontally healthy and diseased patients. J Clin Periodontol 41:1027-36|
|Teles, Ricardo; Teles, Flavia; Frias-Lopez, Jorge et al. (2013) Lessons learned and unlearned in periodontal microbiology. Periodontol 2000 62:95-162|
|Docktor, Michael J; Paster, Bruce J; Abramowicz, Shelly et al. (2012) Alterations in diversity of the oral microbiome in pediatric inflammatory bowel disease. Inflamm Bowel Dis 18:935-42|