Dynamic Inventory of Intermediate Metabolites of Cyanobacteria in a Diurnal Cycle

Abstract

Cyanobacteria are gaining importance both as hosts for photoautotrophic production of chemicals and as model systems for studies of diurnal lifestyle. The proteome and transcriptome of cyanobacteria have been closely examined under diurnal growth, whereas the downstream effects on the intermediary metabolism have not received sufficient attention. The present study focuses on identifying the cellular metabolites whose inventories undergo dramatic changes in a fast-growing cyanobacterium, Synechococcus elongatus PCC 11801. We identified and quantified 67 polar metabolites, whose inventory changes significantly during diurnal growth, with some metabolites changing by 100-fold. The Calvin-Benson-Bassham cycle intermediates peak at midday to support fast growth. The hitherto unexplored g-glutamyl peptides act as reservoirs of amino acids. Interestingly, several storage molecules or their precursors accumulate during the dark phase, dispelling the notion that all biosynthetic activity takes place in the light phase. Our results will guide metabolic modeling and strain engineering of cyanobacteria.

Probing the metabolism of γ‐glutamyl peptides in cyanobacteria via metabolite profiling and 13C labeling

“…This method is now well established for the detection and quantification of charged and polar metabolites (Jaiswal et al, 2020a;Mccloskey and Gangoiti, 2015). Ten γ-glutamyl dipeptides and the reduced and oxidized forms of glutathione (GSH and GSSG, respectively) were readily identified using the MetDIA and MS-DIAL metabolite identification tools (Li et al, 2016;Tsugawa et al, 2015), as has been reported previously (Table 1) (Jaiswal and Wangikar, 2020a). A few additional dipeptides, such as γ-glutamyl tryptophan, were identified based on MS1 matches followed by matching the fragmentation pattern.…”

“…The LC method was based on reverse-phase chromatography with tributylamine as the ion-pairing agent added in the mobile phase (Jaiswal et al, 2020a;Mccloskey and Gangoiti, 2015). This method is now well established for the detection and quantification of charged and polar metabolites (Jaiswal et al, 2020a;Mccloskey and Gangoiti, 2015). Ten γ-glutamyl dipeptides and the reduced and oxidized forms of glutathione (GSH and GSSG, respectively) were readily identified using the MetDIA and MS-DIAL metabolite identification tools (Li et al, 2016;Tsugawa et al, 2015), as has been reported previously (Table 1) (Jaiswal and Wangikar, 2020a).…”

“…The MS data were acquired in both data-dependent and dataindependent manners (DDA and DIA, respectively); the latter with the SWATH method (Jaiswal and Wangikar, 2020b;Jaiswal et al, 2018a). The LC method was based on reverse-phase chromatography with tributylamine as the ion-pairing agent added in the mobile phase (Jaiswal et al, 2020a;Mccloskey and Gangoiti, 2015). This method is now well established for the detection and quantification of charged and polar metabolites (Jaiswal et al, 2020a;Mccloskey and Gangoiti, 2015).…”

Probing Light-Dependent Regulation of the Calvin Cycle Using a Multi-Omics Approach

“…Small changes in inactive pool sizes can result in large differences in absolute flux. Recent metabolomic studies confirm significant changes in intermediates across the diurnal cycle to maximize growth during the day ( Werner et al, 2019 ; Jaiswal and Wangikar, 2020 ). The ability to modulate the fractional size of active intermediates during different light conditions provide an additional means to control the rate of CBB cycle reactions.…”

The Effect of Promoter and RBS Combination on the Growth and Glycogen Productivity of Sodium-Dependent Bicarbonate Transporter (SbtA) Overexpressing Synechococcus sp. PCC 7002 Cells

“…The underlying mechanisms of regulation of the pigments are not clear and will require further investigation which is out of the scope of current study. Of interest will be to identify the metabolomic changes in the transformants (Hasunuma et al, 2013;Schwarz et al, 2013;Behrendt et al, 2018;Jaiswal and Wangikar, 2020) and how the variation in SbtA levels alter the bicarbonate uptake rates and intracellular labeling rates through 13C-metabolic labeling studies (Alagesan et al, 2013;Prasannan et al, 2019) as well as the proteomic changes in the transformants.…”