Integration of physiologically relevant photosynthetic energy flows into whole genome models of light‐driven metabolism. (22nd September 2022)

• Record Type: Journal Article
• Authors: Broddrick, Jared T.; Ware, Maxwell A.; Jallet, Denis; Palsson, Bernhard O.; Peers, Graham
• Is Part Of: Plant journal. Volume 112:Number 3(2022)
• Page Start: 603
• Page End: 621
• Publication Date: 2022-09-22
• Subjects: chlorophyll fluorescence -- genome‐scale modeling -- diatom -- metabolism -- electron transport -- quantum yield -- bioproducts -- photosynthesis; Plant molecular biology -- Periodicals; Plant cells and tissues -- Periodicals; Botany -- Periodicals; 580
• DOI: 10.1111/tpj.15965
• Languages: English
• ISSNs: 0960-7412

Abstract

SUMMARY: Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome‐scale metabolic models capture species‐specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non‐photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non‐radiative energy dissipation processes, cross‐compartment electron shuttling, and non‐photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico . Overall, we present a methodology for incorporating a common, informative data type into computational models of light‐driven metabolism and show its utilization within the design–build–test–learn cycle for engineering of photosynthetic organisms. Significance Statement: Whole genome models of metabolism are improved by the addition of parameters associated with measured photo‐physiology. The approach is demonstrated in the photosynthetic alga, Phaeodactylum tricornutum, but this approach could be applied to any plant system.