Aggregatibacter actinomycetemcomitans and Filifactor alocis as Associated with Periodontal Attachment Loss in a Cohort of Ghanaian Adolescents.
Zeinab RazooqiCarola Höglund ÅbergFrancis KwaminRolf ClaessonDorte HaubekJan OscarssonAnders JohanssonPublished in: Microorganisms (2022)
The aims of the present study were to document the presence of Aggregatibacter actinomyctemcomitans and the emerging oral pathogen Filifactor alocis , as well as to identify genotypes of these bacterial species with enhanced virulence. In addition, these data were analyzed in relation to periodontal pocket depth (PPD) and the progression of PPD from the sampled periodontal sites during a two-year period. Subgingival plaque samples were collected from 172 periodontal pockets of 68 Ghanaian adolescents. PPD at sampling varied from 3-14 mm and the progression from baseline, i.e., two years earlier up to 8 mm. The levels of A. actinomycetemcomitans and F. alocis were determined with quantitative PCR. The highly leukotoxic JP2-genotype of A. actinomycetemcomitans and the ftxA a gene of F. alocis , encoding a putative Repeats-in-Toxin (RTX) protein, were detected with conventional PCR. The prevalence of A. actinomycetemcomitans was 57%, and 14% of the samples contained the JP2 genotype. F. alocis was detected in 92% of the samples and the ftxA gene in 52%. The levels of these bacterial species were significantly associated with enhanced PPD and progression, with a more pronounced impact in sites positive for the JP2 genotype or the ftxA gene. Taken together, the results indicate that the presence of both A. actinomycetemcomitans and F. alocis with their RTX proteins are linked to increased PPD and progression of disease.
Keyphrases
- escherichia coli
- young adults
- genome wide
- copy number
- physical activity
- genome wide identification
- coronary artery disease
- pseudomonas aeruginosa
- staphylococcus aureus
- high resolution
- gene expression
- small molecule
- big data
- electronic health record
- optical coherence tomography
- genome wide analysis
- candida albicans
- transcription factor
- deep learning