Towards a Metabolic and Expression Model of the Metabolically Versatile Bacterium Rhodopseudomonas Palustris

Rhodopseudomonas palustris is a metabolically versatile Purple Non-Sulfur Bacterium (PNSB). Depending on growth conditions, R. palustris can operate in either one of four different forms of metabolism: photoautotrophic, photoheterotrophic, chemoautotrophic, and chemoheterotrophic. R. palustris is also a facultative anaerobe and is capable of fixing nitrogen. This metabolic flexibility suggests that R. palustris would be an ideal model organism for studying the network of interacting reactions, and how it’s altered in response to changing conditions. Moreover, exploration into R. palustris’ industrial applications has just begun with research being generated on the production of hydrogen, methane, and polyhydroxybutyrate (PHB). This study aims to combine systems and synthetic biology by reconstructing R. palustris’ metabolic and expression (ME-) network and establishing a constitutive promoter library to gain a mechanistic understanding of the organism’s functional capabilities under different conditions.

In this study, a genome-scale metabolic model was first constructed following a well-developed framework established in the lab. The metabolic model currently contains 925 reactions, 1220 metabolites, and 1134 genes. An expression matrix (E-matix), containing transcription, translation, and post-transcriptional modifications, is currently being reconstructed. After the reconstruction of this matrix, the two networks will be integrated and previously established ‘coupling constraints’ will be implemented to link the synthesis of macromolecular molecules to catalysis of metabolic components. Moreover, preliminary experimental results showed that the bacterium is able to utilize a host of different carbon sources, including a host of lignin-derived compounds. An IPTG induced promoter was also established to express the CRISPRi components. This system will be used to verify and iteratively improve model predictions, resulting in a well-established mechanistic understanding of the organism’s cellular functions. The predictive power of this work will expand as more omics datasets become available, facilitating the establishment of this metabolically versatile, non-model microorganism as a biotechnology platform.