Microbial and Cytokine Signatures of Periodontitis in Dogs

Principal Investigator: Santiago Peralta

Co-PI: Rodrigo Bicalho

Department of Clinical Sciences
Sponsor: American Kennel Club Canine Health Foundation
Grant Number: 02809
Title: Microbial and Cytokine Signatures of Periodontitis in Dogs
Project Amount: $86,532
Project Period: November 2020 to October 2022

DESCRIPTION (provided by applicant): 

Periodontitis is a painful inflammatory disease highly prevalent in dogs. Like in other species, its pathogenesis is believed to involve complex interactions between the host’s defense mechanisms and microbial communities present in subgingival areas. Evidence suggests that the taxonomical and functional profiles of subgingival microbial communities influence the host’s inflammatory response. However, these possible associations have not been investigated in dogs. Herein, we propose to determine whether differences in the subgingival microbial taxonomic and functional profiles contribute to specific inflammatory cytokine tissue expression patterns in dogs affected with periodontitis compared to healthy animals. In total, 100 dogs will be enrolled (n=20/group) according to clinical staging (stages 1 to 4), as well as a healthy group for comparison. Subgingival samples will be collected for shotgun metagenomic sequencing; gingival tissues from surgical sites will be collected for reverse transcription-quantitative PCR (RT-qPCR). The metagenomic sequencing data obtained will be uploaded in the MG-Rast server to analyze the microbial profiles (taxonomic and functional). Using RT-qPCR data, we will calculate gene expression in diseased and healthy tissues. The RT-qPCR individual data points will be presented as well as fold-change in expression. Finally, associations between metagenomic sequencing data and differential gene expression data will be analyzed using response screening analysis correlating each host-related gene expression parameter with metagenomics-related sequencing data. Possible associations will be assessed by calculating the false discovery rate and the R-square of the association. All metagenomic data found to be significantly associated with host-related gene expression will be selected for further multivariate analysis.