(353e) Systems Level Prediction of Best Product Conversion from CO2 for the Novel Fast Growing Cyanobacterium Synechococcus Elongatus PCC 11801
AIChE Annual Meeting
2024
2024 AIChE Annual Meeting
Sustainable Engineering Forum
Novel Approaches to CO2 Utilization I
Tuesday, October 29, 2024 - 1:50pm to 2:10pm
Greenhouse gases from chemical manufacturing industries contribute significantly to climate change and global warming, and their sequestration is imperative to reducing the carbon
footprint. Cyanobacteria are photoautotrophic microorganisms capable of fixing CO2 from the environment via photosynthesis, and can be engineered to convert the CO2 to value-added products. Despite progress in synthetic biology and strain engineering technologies, the commercial applicability of cyanobacteria has been limited due to the slow growth rate. However, the freshwater strain Synechococcus elongatus PCC 11801 recently isolated from our lab is one of the fastest growing strain with tolerance to different environmental stresses, making it a potential cell factory. To streamline and guide strain engineering efforts for PCC 11801, we generated a genome scale metabolic model for the strain that can be utilized with flux analysis to generate in silico predictions of hotspots for genetic perturbation that would maximize a specific value-added product in its metabolic network. The model was reconstructed using the standard protocol of draft reconstruction generation, manual curation, model debugging and validation. The genome scale model (‘iJB785’) of a phylogenetically close model strain Synechococcus elongatus PCC 7942 was used as the starting point. The draft reconstruction of PCC 11801 was generated by first mapping the homologous genes with iJB785 through bidirectional best hit analysis of the genomes. The model reactions were manually curated to reduce redundancy, and verified with the BiGG, KEGG and BRENDA database. 287 novel reactions identified pertinent to PCC 11801 were added de novo into the model with the corresponding gene-protein-reaction association. Model debugging was performed through flux balance analysis, flux variability analysis, single gene deletion analysis, and single reaction deletion analysis. Overall, the metabolic model consisted of 1052 genes and 1130 reactions. The model was validated by gene essentiality analysis with a sensitivity and specificity score of 0.9223 and 0.5425 respectively. The metabolic robustness of the model was estimated to be 39% from the model, which validates the amenability of the strain for genetic engineering. The model predicted that the strain would convert CO2 to succinic acid at a higher production rate compared to other value-added products through the in silico predictions.
footprint. Cyanobacteria are photoautotrophic microorganisms capable of fixing CO2 from the environment via photosynthesis, and can be engineered to convert the CO2 to value-added products. Despite progress in synthetic biology and strain engineering technologies, the commercial applicability of cyanobacteria has been limited due to the slow growth rate. However, the freshwater strain Synechococcus elongatus PCC 11801 recently isolated from our lab is one of the fastest growing strain with tolerance to different environmental stresses, making it a potential cell factory. To streamline and guide strain engineering efforts for PCC 11801, we generated a genome scale metabolic model for the strain that can be utilized with flux analysis to generate in silico predictions of hotspots for genetic perturbation that would maximize a specific value-added product in its metabolic network. The model was reconstructed using the standard protocol of draft reconstruction generation, manual curation, model debugging and validation. The genome scale model (‘iJB785’) of a phylogenetically close model strain Synechococcus elongatus PCC 7942 was used as the starting point. The draft reconstruction of PCC 11801 was generated by first mapping the homologous genes with iJB785 through bidirectional best hit analysis of the genomes. The model reactions were manually curated to reduce redundancy, and verified with the BiGG, KEGG and BRENDA database. 287 novel reactions identified pertinent to PCC 11801 were added de novo into the model with the corresponding gene-protein-reaction association. Model debugging was performed through flux balance analysis, flux variability analysis, single gene deletion analysis, and single reaction deletion analysis. Overall, the metabolic model consisted of 1052 genes and 1130 reactions. The model was validated by gene essentiality analysis with a sensitivity and specificity score of 0.9223 and 0.5425 respectively. The metabolic robustness of the model was estimated to be 39% from the model, which validates the amenability of the strain for genetic engineering. The model predicted that the strain would convert CO2 to succinic acid at a higher production rate compared to other value-added products through the in silico predictions.