Monitoring Intracellular Metabolite Dynamics in Saccharomyces cerevisiae during Industrially Relevant Famine Stimuli

Abstract

Carbon limitation is a common feeding strategy in bioprocesses to enable an efficient microbiological conversion of a substrate to a product. However, industrial settings inherently promote mixing insufficiencies, creating zones of famine conditions. Cells frequently traveling through such regions repeatedly experience substrate shortages and respond individually but often with a deteriorated production performance. A priori knowledge of the expected strain performance would enable targeted strain, process, and bioreactor engineering for minimizing performance loss. Today, computational fluid dynamics (CFD) coupled to data-driven kinetic models are a promising route for the in silico investigation of the impact of the dynamic environment in the large-scale bioreactor on microbial performance. However, profound wet-lab datasets are needed to cover relevant perturbations on realistic time scales. As a pioneering study, we quantified intracellular metabolome dynamics of Saccharomyces cerevisiae following an industrially relevant famine perturbation. Stimulus-response experiments were operated as chemostats with an intermittent feed and high-frequency sampling. Our results reveal that even mild glucose gradients in the range of 100 μmol·L^-1 impose significant perturbations in adapted and non-adapted yeast cells, altering energy and redox homeostasis. Apparently, yeast sacrifices catabolic reduction charges for the sake of anabolic persistence under acute carbon starvation conditions. After repeated exposure to famine conditions, adapted cells show 2.7% increased maintenance demands.

Keywords: Saccharomyces cerevisiae; baker’s yeast; bioprocess engineering; bioreactor; chemostat; metabolomics; scale-down; scale-up; stimulus-response experiment; substrate gradient; systems biology.

Figure 6

Dynamics of energy carriers and intermediates of the purine salvage pathway. (a) The non-adapted response (red) indicates dynamics following a single transition into a starvation scenario (“feed off” phase) and the adapted response (green) was sampled from representative 9 min cycles during steady-state DS. Time point 0 min of the non-adapted response was equal to steady-state RS. The adenylate energy charge was calculated according to [72]. All values indicate means ± standard deviation of three biological replicates. (b) Schematic representation of the adenylate kinase system attached to the purine salvage pathway, reproduced from [73,74]. Aah1, adenine deaminase; Ade12, adenylosuccinate synthase; Ade13, adenylosuccinate lyase; AdeS, adenylosuccinate; Adk1, adenylate kinase; Ado1, adenosine kinase; Amd1, AMP deaminase; Apt1, adenine phosphoribosyl transferase; ASP, aspartate; FUM, fumarate; Hpt1, hypoxanthine-guanine phosphoribosyl transferase; Isn1, IMP-specific 50-nucleotidase; Pnp1, purine nucleoside phosphorylase; PRPP, phosphoribosyl pyrophosphate; R1P, ribose-1-phosphate.

Figure A1

Exemplary deconvolution results for O₂ and CO₂ signals of one replicate after a single perturbation (see Figure 3c). Deconvolution of O₂ (left panel) and CO₂ (right panel) signals (green) was calculated based on equations (A1) and (A2), parameters from Table A1 and plotted against raw signals (red).

Figure 1

Basic procedure for data-driven scale-up/scale-down development. Concentration gradients are derived from large-scale simulations to design stimulus-response experiments and generate -omics datasets. This approach further allows the set-up of biological models to refine large-scale simulations. Ultimately, gained knowledge enables process-adapted strain engineering and the design of realistic scale-down simulators for validation experiments to replace classical scaling-up.

Figure 2

Simulated versus experimental glucose profiles experienced by yeast cells. (a) Exemplary lifeline of a single Saccharomyces cerevisiae trajectory recorded over 24 min during an industrial glucose-limited fed-batch process with a biomass concentration of 10 g·L⁻¹. The lifeline was simulated during the work of Sarkizi et al. [39], but not published. (b) Stimulus-response experiment as a glucose-limited chemostat with intermittent feed (this work). Extracellular glucose levels are the means ± standard deviation of six biological replicates (merged trends from adapted and non-adapted time series). All simulated values were computed using published glucose uptake kinetics [51]. Overflow metabolism was assumed to start at glucose concentrations >207 μmol·L⁻¹ [52] and starvation zones developed below 53 μmol·L⁻¹, where maintenance demands could not be covered anymore [53].

Figure 3

Relaxation of the intracellular metabolome and respiratory activity. (a) Multidimensional scaling (MDS) plot of the non-adapted (red) 6 h time series based on min–max normalized concentrations of 28 intracellular metabolites. Arrows provide a visual aid to follow the short-term (solid) and mid-term (dashed) dynamics. (b) Analogous MDS plot of the adapted (green) 9 min time series. (c) Evolutions of the oxygen and carbon dioxide transfer rates after a single starvation transition. (d) Analogous off gas analysis over 5 perturbation cycles during the dynamic steady state. Text labels in (a,b) represent the sample time in minutes. Blue and orange lines in (c,d) represent the mean and light areas represent the respective standard deviation of three biological replicates.

Figure 4

Dynamics of central catabolic metabolites after a 2 min glucose depletion phase. The nonadapted response (red) indicates dynamics following a single transition into a starvation scenario (“feed off” phase) and the adapted response (green) was sampled from representative 9 min cycles during steady-state DS. Time point 0 min of the non-adapted response was equal to steady-state RS. All values indicate means ± standard deviation of three biological replicates.

Figure 5

Dynamics of the reduction equivalents, conserved moieties and according to ratios. The non-adapted response (red) indicates dynamics following a single transition into a starvation scenario (“feed off” phase) and the adapted response (green) was sampled from representative 9 min cycles during steady-state RS. Time point 0 min of the non-adapted response was equal to steady-state RS. All values indicate means ± standard deviation of three biological replicates (except for the non-adapted time series, which was derived from two biological replicates).