Abstract
INTRODUCTION: Genetically enhanced microorganisms have wide applications in different fields and the increasing availability of omics data has enabled the development of genome-scale metabolic models (GEMs), which are essential tools in synthetic biology. Bartonella quintana str. Toulouse, a facultative intracellular parasite, presents a small genome and the ability to grow in axenic culture, making it a potential candidate for genome reduction and synthetic biology applications. This study aims to reconstruct and analyze the metabolic network of B. quintana to optimize its growth conditions for laboratory use. METHODS: A metabolic reconstruction of B. quintana was performed using genome annotation tools (RAST and ModelSEED), followed by refinement using multiple databases (KEGG, BioCyc, BRENDA). Flux Balance Analysis (FBA) was conducted to optimize biomass production, and in-silico knockouts were performed to evaluate growth yield under different media conditions. Additionally, experimental validation was carried out by testing modified culture media and performing proteomic analyses to identify metabolic adaptations. RESULTS: FBA simulations identified key metabolic requirements, including 2-oxoglutarate as a crucial compound for optimal growth. In-silico knockouts of transport genes revealed their essentiality in nutrient uptake. Experimental validation confirmed the role of 2-oxoglutarate and other nutrients in improving bacterial growth, though unexpected decreases in viability were observed under certain supplemented conditions. Proteomic analysis highlighted differential expression of proteins associated with cell wall integrity and metabolic regulation. DISCUSSION: This study represents a step toward developing B. quintana as a viable chassis for synthetic biology applications. The reconstructed metabolic model provides a comprehensive understanding of B. quintana's metabolic capabilities, identifying essential pathways and growth limitations. While metabolic predictions align with experimental results in key aspects, further refinements are needed to enhance model accuracy and optimize growth conditions.