Exploring the diagnostic potential of extracellular metabolites produced by oral pathogen Porphyromonas gingivalis
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Open Access
Type
ThesisThesis type
Doctor of PhilosophyAuthor/s
Zhang, ShengAbstract
Periodontitis, one of the most prevalent chronic inflammatory diseases, is induced by periodontal bacteria. Porphyromonas gingivalis, recognized as the "keystone pathogen," is hypothesized to orchestrate the dysbiotic shift of the polymicrobial community, thereby initiating ...
See morePeriodontitis, one of the most prevalent chronic inflammatory diseases, is induced by periodontal bacteria. Porphyromonas gingivalis, recognized as the "keystone pathogen," is hypothesized to orchestrate the dysbiotic shift of the polymicrobial community, thereby initiating periodontitis. The intricate interactions between P. gingivalis and the host could provide insights into the transition between health and disease states. This study aims to examine the extracellular metabolome produced by P. gingivalis under conditions simulating health and disease states, and to explore the potential of P. gingivalis-derived extracellular metabolites as biomarkers for disease diagnosis and prediction. Employing untargeted and targeted metabolomics techniques, we initially characterized distinct extracellular metabolite profiles produced by P. gingivalis, detecting 24,773 annotated metabolites under various haemoglobin levels corresponding to different disease stages. Among the top-ranked annotated metabolites contributing to the variances between groups, kynurenic acid, tryptophan, indole, and protoporphyrin IX (PPIX) were identified. These identified extracellular metabolites are proposed to contribute to disease initiation and progression through several possible mechanisms, including bacterial cross-feeding, biofilm regulation, and immune modulation. To further explore the potential of P. gingivalis-derived extracellular metabolites as diagnostic and predictive biomarkers for periodontitis, a prediction model, based on logistic regression combined with a Receiver Operator Characteristic (ROC) curve, was developed. The model, utilizing tryptophan and PPIX as diagnostic markers, demonstrated high accuracy in distinguishing the active disease cohort from both the healthy control and post-treatment cohorts. The findings of this study suggest a promising periodontitis prediction model based on P. gingivalis-derived extracellular metabolites.
See less
See morePeriodontitis, one of the most prevalent chronic inflammatory diseases, is induced by periodontal bacteria. Porphyromonas gingivalis, recognized as the "keystone pathogen," is hypothesized to orchestrate the dysbiotic shift of the polymicrobial community, thereby initiating periodontitis. The intricate interactions between P. gingivalis and the host could provide insights into the transition between health and disease states. This study aims to examine the extracellular metabolome produced by P. gingivalis under conditions simulating health and disease states, and to explore the potential of P. gingivalis-derived extracellular metabolites as biomarkers for disease diagnosis and prediction. Employing untargeted and targeted metabolomics techniques, we initially characterized distinct extracellular metabolite profiles produced by P. gingivalis, detecting 24,773 annotated metabolites under various haemoglobin levels corresponding to different disease stages. Among the top-ranked annotated metabolites contributing to the variances between groups, kynurenic acid, tryptophan, indole, and protoporphyrin IX (PPIX) were identified. These identified extracellular metabolites are proposed to contribute to disease initiation and progression through several possible mechanisms, including bacterial cross-feeding, biofilm regulation, and immune modulation. To further explore the potential of P. gingivalis-derived extracellular metabolites as diagnostic and predictive biomarkers for periodontitis, a prediction model, based on logistic regression combined with a Receiver Operator Characteristic (ROC) curve, was developed. The model, utilizing tryptophan and PPIX as diagnostic markers, demonstrated high accuracy in distinguishing the active disease cohort from both the healthy control and post-treatment cohorts. The findings of this study suggest a promising periodontitis prediction model based on P. gingivalis-derived extracellular metabolites.
See less
Date
2023Rights statement
The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.Faculty/School
Faculty of Medicine and Health, The University of Sydney School of DentistryDepartment, Discipline or Centre
Discipline of Oral BiosciencesAwarding institution
The University of SydneyShare