Diel light:dark cycles significantly reduce FFA accumulation in FFA producing mutants of Synechocystis sp. PCC 6803 compared to continuous light

Rhythm of the Night (and Day): Predictive Metabolic Modeling of Diurnal Growth in Chlamydomonas

Economical production of photosynthetic organisms requires the use of natural day/night cycles. These induce strong circadian rhythms that lead to transient changes in the cells, requiring complex modeling to capture. In this study, we coupled times series transcriptomic data from the model green alga Chlamydomonas reinhardtii to a metabolic model of the same organism in order to develop the first transient metabolic model for diurnal growth of algae capable of predicting phenotype from genotype. We first transformed a set of discrete transcriptomic measurements (D. Strenkert, S. Schmollinger, S. D. Gallaher, P. A. Salomé, et al., Proc Natl Acad Sci U S A 116:2374–2383, 2019, https://doi.org/10.1073/pnas.1815238116) into continuous curves, producing a complete database of the cell’s transcriptome that can be interrogated at any time point. We also decoupled the standard biomass formation equation to allow different components of biomass to be synthesized at different times of the day. The resulting model was able to predict qualitative phenotypical outcomes of a starchless mutant. We also extended this approach to simulate all single-knockout mutants and identified potential targets for rational engineering efforts to increase productivity. This model enables us to evaluate the impact of genetic and environmental changes on the growth, biomass composition, and intracellular fluxes for diurnal growth.

IMPORTANCE We have developed the first transient metabolic model for diurnal growth of algae based on experimental data and capable of predicting phenotype from genotype. This model enables us to evaluate the impact of genetic and environmental changes on the growth, biomass composition and intracellular fluxes of the model green alga, Chlamydomonas reinhardtii. The availability of this model will enable faster and more efficient design of cells for production of fuels, chemicals, and pharmaceuticals.

A comprehensive time-course metabolite profiling of the model cyanobacterium Synechocystis sp. PCC 6803 under diurnal light:dark cycles

Cyanobacteria are a model photoautotroph and a chassis for the sustainable production of fuels and chemicals. Knowledge of photoautotrophic metabolism in the natural environment of day/night cycles is lacking, yet has implications for improved yield from plants, algae and cyanobacteria. Here, a thorough approach to characterizing diverse metabolites-including carbohydrates, lipids, amino acids, pigments, cofactors, nucleic acids and polysaccharides-in the model cyanobacterium Synechocystis sp. PCC 6803 (S. 6803) under sinusoidal diurnal light:dark cycles was developed and applied. A custom photobioreactor and multi-platform mass spectrometry workflow enabled metabolite profiling every 30-120 min across a 24-h diurnal sinusoidal LD ('sinLD') cycle peaking at 1600 μmol photons m^-2 sec^-1 . We report widespread oscillations across the sinLD cycle with 90%, 94% and 40% of the identified polar/semi-polar, non-polar and polymeric metabolites displaying statistically significant oscillations, respectively. Microbial growth displayed distinct lag, biomass accumulation and cell division phases of growth. During the lag phase, amino acids and nucleic acids accumulated to high levels per cell followed by decreased levels during the biomass accumulation phase, presumably due to protein and DNA synthesis. Insoluble carbohydrates displayed sharp oscillations per cell at the day-to-night transition. Potential bottlenecks in central carbon metabolism are highlighted. Together, this report provides a comprehensive view of photosynthetic metabolite behavior with high temporal resolution, offering insight into the impact of growth synchronization to light cycles via circadian rhythms. Incorporation into computational modeling and metabolic engineering efforts promises to improve industrially relevant strain design.

High-Throughput Inertial Focusing of Micrometer- and Sub-Micrometer-Sized Particles Separation

The ability to study individual bacteria or subcellular organelles using inertial microfluidics is still nascent. This is due, in no small part, to the significant challenges associated with concentrating and separating specific sizes of micrometer and sub-micrometer bioparticles in a microfluidic format. In this study, using a rigid polymeric microfluidic network with optimized microchannel geometry dimensions, it is demonstrated that 2 µm, and even sub-micrometer, particles can be continuously and accurately focused to stable equilibrium positions. Suspensions have been processed at flow rates up to 1400 µL min^-1 in an ultrashort 4 mm working channel length. A wide range of suspension concentrations-from 0.01 to 1 v/v%-have been systematically investigated, with yields greater than 97%, demonstrating the potential of this technology for large-scale implementation. Additionally, the ability of this chip to separate micrometer- and sub-micrometer-sized particles and to focus bioparticles (cyanobacteria) has been demonstrated. This study pushes the microfluidic inertial focusing particle range down to sub-micrometer length scales, enabling novel routes for investigation of individual microorganisms and subcellular organelles.