Molecular-level evaluation of selected periodontal pathogens from subgingival regions in canines and humans with periodontal disease

Abstract

Dogs commonly serve as a model for various human conditions, including periodontal diseases. The aim of this study was to identify the anaerobic bacteria that colonize the subgingival areas in dogs and humans by using rapid real-time polymerase chain reaction (RT-PCR)-based tests and to compare the results obtained in each species. Bacterial microflora evaluations, both quantitative and qualitative, were performed by applying ready-made tests on twelve dogs and twelve humans. Five samples were collected from each subject's deepest gingival pockets and joined to form a collective sample. The results of the study revealed interspecies similarities in the prevalences of Porphyromonas (P.) gingivalis, Treponema denticola, Tannerella forsythia, and Fusobacterium nucleatum. Red complex bacteria comprised the largest portion of the studied bacterial complexes in all study groups, with P. gingivalis being the most commonly isolated bacterium. The results show similarities in the prevalence of bacterial microflora in dogs and humans. Microbiological analysis of gingival pockets by using rapid real-time PCR-based tests in clinical practice, both veterinary and human, can facilitate the choice of appropriate pharmacological treatment and can provide a basis for subsequent verification of the treatment's effectiveness.

Dental Biofilm as Etiological Agent of Canine Periodontal Disease

“…As transmission of oral bacteria during normal contacts between dogs and humans is also feasible one might expect correlations between the oral microbiota of dogs and humans [17]. Oral-to-oral transfer of Neisseria shayeganii, Porphyromonas canigingivalis, Tannerella forsythia and Streptococcus minor from dogs to humans is suspected.…”

Effect of Hydrogen Peroxide Photoactivated Decontamination Using 940 nm Diode Laser in Periodontal Treatment: A Pilot Study

“…Group 3 was the only one capable of successfully eliminating ( p < 0.001) the orange complex bacteria (P. intermedia, P. micros, and F. nucleatum) responsible for creating the living conditions for the strictly anaerobic bacteria of the red complex and their colonization of the periodontal pocket. 24 All investigated groups demonstrate a statistical improvement ( p < 0.001) of all investigated clinical parameters like PPD, CAL (Table 7), and BoP (Table 8).…”

Quantification by qPCR of Pathobionts in Chronic Periodontitis: Development of Predictive Models of Disease Severity at Site-Specific Level

“…From an analytical point of view, the majority of authors in their respective qPCR studies applied a ‘simple approach.’ As a result, the subgingival levels of pathobionts in control volunteers and periodontal patients were compared for testing using q-PCR, whether or not a particular bacterium is absent/present and/or diminished/elevated at the individual level ( Braga et al, 2010 ; Polonyi et al, 2013 ; Kotsilkov et al, 2015 ; Milne et al, 2015 ; Scapoli et al, 2015 ; Golynska et al, 2016 ). In parallel with current trends in big data analytics for genomic medicine ( He et al, 2017 ), further evidence is required about applying multivariate analytical approaches to qPCR data.…”

“…Until now, research on periodontal microbiology using qPCR has focused on the study of periopathogens such as: A. actinomycetemcomitans , P. gingivalis , T. forsythia , P. intermedia , T. denticola , F. nucleatum , and P. micra ( Braga et al, 2010 ; Saygun et al, 2011 ; Decat et al, 2012 ; Polonyi et al, 2013 ; Gatto et al, 2014 ; Kinney et al, 2014 ; Al-hebshi et al, 2015 ; Kotsilkov et al, 2015 ; Milne et al, 2015 ; Scapoli et al, 2015 ; Torrungruang et al, 2015 ; Golynska et al, 2016 ). In most of these q-PCR studies, the authors applied a ‘simple analytical approach’ consisting of univariate comparisons of the periopathogen levels quantified in different periodontal conditions ( Braga et al, 2010 ; Polonyi et al, 2013 ; Kotsilkov et al, 2015 ; Milne et al, 2015 ; Scapoli et al, 2015 ; Golynska et al, 2016 ). As a result, there are very few studies based on a multivariate modeling approach ( Saygun et al, 2011 ; Gatto et al, 2014 ; Kinney et al, 2014 ; Al-hebshi et al, 2015 ; Torrungruang et al, 2015 ).…”