Genomic and Metabolic analysis of a pathogen causing dental disease

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• Zihan Tian
• Wednesday 18 June 2025, 17:00-17:30
• Online.

If you have a question about this talk, please contact Pietro Lio.

Various studies indicate that microbial dysbiosis is associated with oral and systemic diseases (Han, 2015). Fusobacterium nucleatum (F.nucleatum), a Gram-negative anaerobe naturally found in the oral cavity of humans, contributes to periodontal disease, as well as oral and colorectal cancers (Patel et al., 2022; Queen et al., 2025). The association of F. nucleatum with oral squamous cell carcinoma (OSCC) highlights its role via oncogenic signaling pathways and has metabolic adaptability in the tumor microenvironments (Lim et al., 2025).To better understand F. nucleatum metabolic capabilities and environmental factors that promote cancer-associated strain growth in a specific niche, such as the oral cavity, this study constructs a comprehensive metabolic model to predict its growth dynamics by leveraging machine learning-informed genome-scale metabolic modeling (GEM) (Kapatral et al., 2002). This study wished to facilitate oral cancer screening and early diagnosis, thus focusing on Fusobacterium nucleatum strain ATCC 25586 , which naturally appears in the oral cavity.

To systematically investigate F. nucleatum’s metabolic behavior, COBR Apy, a Python-based constraint-based modeling framework, is used for it allows mechanistic interpretation, genome-scale predictions of microbial growth under defined environmental constraints. COBRA -based features (e.g., nutrient flux profiles, biomass predictions) can be used later as inputs to train machine learning models that predict disease associations or therapeutic responses based on a specific oral nutrient environment (Heirendt et al., 2019).

To generate a metabolic model that COBR Apy can analyze, the complete genome of F. nucleatum ATCC 25586 was initially annotated using Prokka, providing a detailed catalog of its genetic components. The annotated genome was then utilized with CarveMe to reconstruct a genome-scale metabolic model, comprising 1914 reactions and 1339 metabolites, with 582 associated genes. Flux Balance Analysis (FBA) was applied to determine the optimal growth rate, which was predicted to be 67.42h⁻¹ under ideal nutrient-rich conditions. The flux through the biomass reaction represents the rate at which the bacterium produces those metabolites necessary for its growth (e.g., amino acids, lipids, cofactors, and proteins). The biomass flux returned by COBRA is the specific growth rate that can be used to calculate the specific biomass of the bacteria at a specific time using an exponential function. Therefore, biomass flux can be used as an indicator of the growth rate of the bacterium.

This talk is part of the Data Science and AI in Medicine series.

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