Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris

Abstract

Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. Here, we used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealed an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted flux trends and gene expression trends was found for 65% of multisubunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acid metabolism. Furthermore, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of, CZ946, thus increasing our knowledgebase by 10% for this model green alga.

The social network of microorganisms — how auxotrophies shape complex communities

“…Genome analysis, often combined with computational modelling approaches, has provided new insight into the nutrient requirements of various microorganisms that inhabit the human body. The ability to investigate multiple microorganisms simultaneously despite our lack of success in cultivating them in the laboratory 29,30 has opened the door to in proteome allocation (such as amino acid composition) 36 and are also dependent on the growth stage of the organism 37 . Therefore, the energetic burden for different microorganisms is not evenly distributed within a community.…”

Applications of stable isotope‐based metabolomics and fluxomics toward synthetic biology of cyanobacteria

“…It has been well documented that nutrient stress induces major shifts of fluxes into new pathways that deviate from canonical metabolic pathways (Kumar et al, ; Qian et al, ; R. Schwarz & Forchhammer, ). Nutrient deprivation under photoautotrophic conditions has been reported to increase carbohydrate (glycogen) accumulation in cyanobacteria and lipid content in microalgae, albeit invariably with reduced growth rates and biomass yields (Abernathy et al, ; Kumar et al, ; R. Schwarz & Forchhammer, ; Tossavainen et al, ; X. Wang et al, ; Zuñiga et al, ). These prior studies demonstrate how cyanobacteria adjust photosynthesis and carbon partitioning between biosynthetic pathways under nutrient‐limited conditions.…”

Engineering the metabolic pathways of lipid biosynthesis to develop robust microalgal strains for biodiesel production

“…The mathematical study of these higher photosynthetic organisms needs to include further pathways in order to build a model with the ability to elaborate the energy fixation using light and carbon sourced through CO 2 fixation. A metabolic model reconstruction can be initiated by finding and putting together the reactions individually or it can be established de novo by the formation of a draft on the basis of sequence homology to phylogenetic proximate . C. reinhardtii and Synechocystis were the first oleaginous microalgal species for which GSMs were designed .…”