Employing Systems Biology for Discovery and Engineering in Phototrophic Microorganisms
- Author(s): Broddrick, Jared Thomas
- Advisor(s): Palsson, Bernhard O
- et al.
Global respiratory balance is maintained by photosynthetic organisms, yet the importance of this contribution is not commensurate with our understanding of light-driven metabolic processes. Meanwhile, there has been a increase in the desire to engineer phototrophic microorganisms. Excessive demands by modern society have been depleting nature's resources over the past centuries. Exploring and developing new sustainable resources to counter increasing consumption has therefore been the focus of research efforts in the academic and private sectors. The emphasis has partly been on using phototrophic organisms that fix carbon dioxide by utilizing light energy to produce energy-dense products. Recent efforts to characterize metabolic capabilities of photosynthetic species as well as engineer attractive candidates require a framework for discovery, data analysis and reconfiguring of existing metabolic networks. The systems biology approach of constraint-based reconstruction and analysis coupled with flux balance analysis has a proven record of contextualizing organism specific information and characterizing cellular metabolism. However, a persistent challenge in the modeling of photoautotrophy has been a mechanistic incorporation of light uptake. As the light environment dictates cell physiology, such as growth rate, biomass composition and metabolic pathway usage, constraining photon flux is a prerequisite for biologically accurate results. Here we describe efforts to address this challenge and the resulting insights into photoautotrphic biology. First, a proper accounting of light uptake and shading coupled with a high-quality genome-scale model of the cyanobacteria
Synechococcus elongatus sp. PCC7942 resulted in accurate growth and flux predictions. Additionally,
we concluded despite an incomplete TCA cycle in the organism studied, there was no impact to fitness due to the metabolic network configuration. Next, in an attempt to extend the methodology to eukaryotic microalgae, we generated a genome-scale model of the diatom Phaeodactylum tricornutum. The systems biology perspective elucidated metabolic capabilities in this organism conferred by its unique phylogeny. Applying an improved set of photophysiology constraints, we simulated circadian dynamics and characterized photoprotective mechanisms resulting from the previously elucidated metabolic capabilities; highlighting the importance of the broader metabolic network in dissipating excess light energy. Finally, photophysiology constraints coupled to chlorophyll fluorescence measurements and genome-scale modeling enabled a comparative analysis of light environment acclimation across the phototrophic clades cyanobacteria, green algae and diatoms and quantified the fraction of excess light energy absorbed by the cells and its metabolic fate. Overall, coupling mechanistic constraints on photophysiology with genome-scale modeling accurately characterized the cellular response to the light environment. This work is relevant to understanding species-specific metabolic capabilities and adaptations of interest to biological and bioengineering communities.