Periodontitis is a severe inflammatory disease that damages the gingiva and has been linked to systemic diseases, such as diabetes, heart disease, and respiratory disease. These serious health complications express the importance of studying oral microbiota and their interactions. This research investigates the growth dynamics of Fusobacterium nucleatum and Porphyromonas gingivalis, two gram-negative anaerobic bacteria that play a significant role in inducing the progression of periodontitis, under mono- and co-culture conditions. By collecting monoculture and co-culture growth curves along with cell counts, and LIVE/DEAD BacLight staining we are identifying their optimal growth phases and interactions. With this information, we seek to optimize these cultures for downstream experiments, including bulk and single-cell RNA sequencing, to identify unique genes signatures from cell-cell interactions implicated in periodontal disease progression. Understanding these dynamics will contribute to future studies on the persistence of periodontal infections and broader research on gene expression.

Individuals with Stage II/III severe periodontal disease were recruited from the Graduate Periodontics Clinic at the University of Washington School of Dentistry using an approved IRB (STUDY00016871). Subgingival plaque samples were collected from four tooth sites: an active inflamed site, two adjacent tooth sites, and a distant healthy tooth site. Sterile paper points were inserted into the gingival sulcus for 30 seconds. DNA was isolated and extracted using the Qiagen AllPrep DNA/RNA Mini kit (Cat. #80204), then purified and concentrated using the Zymo Clean & Concentrator (Cat. #D4014) kit. 16S rRNA libraries were generated and sequencing was performed on the MiSeq platform (Illumina, San Deigo, CA, USA) using 300bp paired-end chemistry. Raw reads were processed and analyzed using Qiime2 and the DADA2 algorithm to generate amplicon sequence variants (ASVs), which were then classified using the expanded Human Oral Microbiome Database (eHOMD). Differences between tooth sites were assessed within and across individuals. Correlation between taxonomic levels and clinical data was also assessed. Data analysis is still being performed at this time. Based on previous literature (Pawolski et al, 2005, Kerns et al. 2023), we expect that a subgingival community gradient radiates from tooth sites affected with periodontal disease toward distant healthy sites. We aim to resolve this within individuals using ASVs for the first time. Additionally, we anticipate an increase in disease-associated bacteria within actively inflamed tooth sites, such species within Porphyromonas, Tannerella, and Treponema genera. Furthermore, we anticipate a gradient of perio-pathic disease-associated bacteria will decrease in relative abundance the further away from active diseased sites. We expect that results from our study will highlight the presence of a subgingival microbiome composition and enrichment of specific gram negative perio-pathic disease-associated species within clinically healthy tooth sites in patients with active periodontal disease despite the absence of clinically observed inflammation.

Individuals with Stage II/III severe periodontal disease were recruited from the Graduate Periodontics Clinic at the University of Washington School of Dentistry using an approved IRB (STUDY00016871). Subgingival plaque samples were collected from four tooth sites: an active inflamed site, two adjacent tooth sites, and a distant healthy tooth site. Sterile paper points were inserted into the gingival sulcus for 30 seconds. DNA was isolated and extracted using the Qiagen AllPrep DNA/RNA Mini kit (Cat. #80204), then purified and concentrated using the Zymo Clean & Concentrator (Cat. #D4014) kit. Whole genome libraries were generated and whole genome sequencing was performed on a NovaSeq X (Illumina, San Diego, CA, USA). Relative abundance of species-level assignments represented by clusters were determined, and best matched genomes were then used to generate species pangenomes for comprehensive multi-genome wide read mapping and gene-level analysis. Taxonomic and gene level functional analysis was also performed. Data analysis is still being performed at this time. Based on previous literature (Basic and Dahlén, 2023), we anticipate that the activity of certain microbial metabolic pathways associated with oral disease and their functions will be heightened in the active disease site and lower on the non-affected site within strain-level disease-associated bacteria, such as those of the Porphyromonas, Tannerella, and Treponema genera. Specifically, we expect amino acid fermentation and lipid metabolism activity to be increased in the active disease site. We expect that results from this study will highlight the presence of heightened activity of bacterial metabolic pathways and functions at the strain level associated with specific gram negative perio-pathic disease-associated species within clinically healthy tooth sites in patients with active periodontal disease despite the absence of clinical observed inflammation.

Periodontitis, the most severe form of periodontal disease, affects ~50% of Americans and is expected to continue increasing as a major public health concern globally. Fusobacterium nucleatum (Fn) and Porphyromonas gingivalis (Pg) have been identified as synergistic oral pathogens that play a key role in advancing periodontitis via immune subversion; however, bulk RNA sequencing fails to elucidate the genuine synergistic interactions among these populations due to culture heterogeneity. To capture the true cell-cell interactions within complex polymicrobial communities we are utilizing microbial split-pool ligation transcriptomics (microSPLiT), a cutting-edge high-resolution single-cell RNA sequencing approach to illuminate novel interactions between Fn and Pg. To optimize microSPLiT for oral bacteria, this study explores quantifying RNA and DNA within Fn and Pg mono-cultures to pinpoint ideal sample populations and library preparation conditions needed for accurate single-cell gene expression. Qubit fluorometric quantification was used to quantify RNA and DNA. Pg possess known mechanisms that confer resistance to assorted antimicrobial agents; therefore, an increased concentration of enzymatic reagent may be necessary for permeabilization steps within microSPLiT. These findings are expected to help optimize microSPLiT for Fn and Pg while directly advancing our understanding of in vitro interactions between two pervasive oral pathogens.