Glycogen Metabolism Supports Photosynthesis Start through the Oxidative Pentose Phosphate Pathway in Cyanobacteria

Abstract

Cyanobacteria experience drastic changes in their carbon metabolism under daily light/dark cycles. During the day, the Calvin-Benson cycle fixes CO 2 and diverts excess carbon into glycogen storage. At night, glycogen is degraded to support cellular respiration. The dark/light transition represents a universal environmental stress for cyanobacteria and other photosynthetic lifeforms. Recent studies revealed the essential genetic background necessary for the fitness of cyanobacteria during diurnal growth. However, the metabolic processes underlying the dark/light transition are not well understood. In this study, we observed that glycogen metabolism supports photosynthesis in the cyanobacterium Synechococcus elongatus PCC 7942 when photosynthesis reactions start upon light exposure. Compared with the wild type, the glycogen mutant ΔglgC showed a reduced photosynthetic efficiency and a slower P700 1 rereduction rate when photosynthesis starts. Proteomic analyses indicated that glycogen is degraded through the oxidative pentose phosphate (OPP) pathway during the dark/light transition. We confirmed that the OPP pathway is essential for the initiation of photosynthesis and further showed that glycogen degradation through the OPP pathway contributes to the activation of key Calvin-Benson cycle enzymes by modulating NADPH levels. This strategy stimulates photosynthesis in cyanobacteria following dark respiration and stabilizes the Calvin-Benson cycle under fluctuating environmental conditions, thereby offering evolutionary advantages for photosynthetic organisms using the Calvin-Benson cycle for carbon fixation.

Dynamic Allocation of Carbon Storage and Nutrient-Dependent Exudation in a Revised Genome-Scale Model of Prochlorococcus

“…We then used dynamic FBA in COMETS with PRO99 medium ( Moore et al, 2007 ) with limited ammonium and diel light conditions to simulate growth over 7 days. The growth curves where qualitatively compared with experimental data from Shinde et al (2020) .…”

Dynamic allocation of carbon storage and nutrient-dependent exudation in a revised genome-scale model ofProchlorococcus

“…Nutrient and light limitations are well-known modulators of carbon storage in Prochlorococcus (Zinser et al, 2009; Szul et al, 2019). Recent work has suggested the storage of carbon to be one of the major metabolic tasks during the day-night cycle (Cano et al, 2018; Szul et al, 2019; Shinde et al, 2020). Sampling 10,000 environmental conditions showed that glycogen production decreased at lower light intensities and was less saturated at higher rates (Figure 3).…”

“…Next, we explored the ability of our model to dynamically capture biologically relevant phenotypes by performing dynamic FBA simulations of knock-out mutants in Prochlorococcus, focusing on two gene deletions disrupting different parts of glycogen metabolism. Δ glgC breaks synthesis of ADP-glucose and thus the storage of glycogen and Δ gnd , knocking out 6-phosphogluconate dehydrogenase, a key reaction in the Pentose Phosphate pathway found to fuel the Calvin cycle with precursor metabolites during the onset of photosynthesis (Shinde et al, 2020). Our dynamic FBA simulations in COMETS (Figure S5) showed similar growth between Δ gnd and the wild type and slightly lower growth for Δ glgC .…”

“…Since genetic tools for the modification of Prochlorococcus are still lacking (Laurenceau et al, 2020), we chose data from the closely related cyanobacteria Synechocystis as recent work described the impact of Δ glgC and Δ gnd on its growth during diel cycles (Shinde et al, 2020). Indeed, we found very good agreement between measured and predicted growth for both the wild-type and Δ glgC mutant where glycogen storage is disrupted (Shinde et al, 2020) (Figure S5). One of the notable limitations of dynamic FBA is the ability to quantify intermediates and precursor pools that might drive the initiation of a pathway.…”

“…We included an arbitrarily derived death rate of 0.1 d -1 (Grossowicz et al, 2017) in these simulations. The growth curves where qualitatively compared with experimental data from Shinde and co-workers (Shinde et al, 2020). The model parameters are given in COMETS parameter and model files in https://github.com/shashany/iSO595/tree/master/COMETS_files.…”