Cross-compartment metabolic coupling enables flexible photoprotective mechanisms in the diatom Phaeodactylum tricornutum.

Photoacclimation consists of short- and long-term strategies used by photosynthetic organisms to adapt to dynamic light environments. Observable photophysiology changes resulting from these strategies have been used in coarse-grained models to predict light-dependent growth and photosynthetic rates. However, the contribution of the broader metabolic network, relevant to species-specific strategies and fitness, is not accounted for in these simple models. We incorporated photophysiology experimental data with genome-scale modeling to characterize organism-level, light-dependent metabolic changes in the model diatom Phaeodactylum tricornutum. Oxygen evolution and photon absorption rates were combined with condition-specific biomass compositions to predict metabolic pathway usage for cells acclimated to four different light intensities. Photorespiration, an ornithine-glutamine shunt, and branched-chain amino acid metabolism were hypothesized as the primary intercompartment reductant shuttles for mediating excess light energy dissipation. Additionally, simulations suggested that carbon shunted through photorespiration is recycled back to the chloroplast as pyruvate, a mechanism distinct from known strategies in photosynthetic organisms. Our results suggest a flexible metabolic network in P. tricornutum that tunes intercompartment metabolism to optimize energy transport between the organelles, consuming excess energy as needed. Characterization of these intercompartment reductant shuttles broadens our understanding of energy partitioning strategies in this clade of ecologically important primary producers.

Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis.

The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology.

Life cycle GHG emissions from microalgal biodiesel--a CA-GREET model.

A life cycle assessment (LCA) focused on greenhouse gas (GHG) emissions from the production of microalgal biodiesel was carried out based on a detailed engineering and economic analysis. This LCA applies the methodology of the California Low Carbon Fuel Standard (CA LCFS) and uses life cycle inventory (LCI) data for process inputs, based on the California-Modified Greenhouse Gases, Regulated Emissions, and Energy use in Transportation (CA GREET) model. Based on detailed mass and energy balances, calculated GHG emissions from this algal biodiesel system are 70% lower than those of conventional diesel fuel, meeting the minimum 50% GHG reduction requirements under the EPA RFS2 and 60% for the European Union Renewable Energy Directive. This LCA study provides a guide to the research and development objectives that must be achieved to meet both economic and environmental goals for microalgae biodiesel production.

Simultaneous quantum yield measurements of carbon uptake and oxygen evolution in microalgal cultures.

The photosynthetic quantum yield (Φ), defined as carbon fixed or oxygen evolved per unit of light absorbed, is a fundamental but rarely determined biophysical parameter. A method to estimate Φ for both net carbon uptake and net oxygen evolution simultaneously can provide important insights into energy and mass fluxes. Here we present details for a novel system that allows quantification of carbon fluxes using pH oscillation and simultaneous oxygen fluxes by integration with a membrane inlet mass spectrometer. The pHOS system was validated using Phaeodactylum tricornutum cultured with continuous illumination of 110 µmole quanta m-2 s-1 at 25°C. Furthermore, simultaneous measurements of carbon and oxygen flux using the pHOS-MIMS and photon flux based on spectral absorption were carried out to explore the kinetics of Φ in P. tricornutum during its acclimation from low to high light (110 to 750 µmole quanta m-2 s-1). Comparing results at 0 and 24 hours, we observed strong decreases in cellular chlorophyll a (0.58 to 0.21 pg cell-1), Fv/Fm (0.71 to 0.59) and maximum ΦCO2 (0.019 to 0.004) and ΦO2 (0.028 to 0.007), confirming the transition toward high light acclimation. The Φ time-series indicated a non-synchronized acclimation response between carbon uptake and oxygen evolution, which has been previously inferred based on transcriptomic changes for a similar experimental design with the same diatom that lacked physiological data. The integrated pHOS-MIMS system can provide simultaneous carbon and oxygen measurements accurately, and at the time-resolution required to resolve high-resolution carbon and oxygen physiological dynamics.