Abstract

BackgroundMicrobial metabolism is highly dependent on the environmental conditions. Especially, the substrate concentration, as well as oxygen availability, determine the metabolic rates. In large-scale bioreactors, microorganisms encounter dynamic conditions in substrate and oxygen availability (mixing limitations), which influence their metabolism and subsequently their physiology. Earlier, single substrate pulse experiments were not able to explain the observed physiological changes generated under large-scale industrial fermentation conditions.ResultsIn this study we applied a repetitive feast–famine regime in an aerobic Escherichia coli culture in a time-scale of seconds. The regime was applied for several generations, allowing cells to adapt to the (repetitive) dynamic environment. The observed response was highly reproducible over the cycles, indicating that cells were indeed fully adapted to the regime. We observed an increase of the specific substrate and oxygen consumption (average) rates during the feast–famine regime, compared to a steady-state (chemostat) reference environment. The increased rates at same (average) growth rate led to a reduced biomass yield (30% lower). Interestingly, this drop was not followed by increased by-product formation, pointing to the existence of energy-spilling reactions. During the feast–famine cycle, the cells rapidly increased their uptake rate. Within 10 s after the beginning of the feeding, the substrate uptake rate was higher (4.68 μmol/gCDW/s) than reported during batch growth (3.3 μmol/gCDW/s). The high uptake led to an accumulation of several intracellular metabolites, during the feast phase, accounting for up to 34% of the carbon supplied. Although the metabolite concentrations changed rapidly, the cellular energy charge remained unaffected, suggesting well-controlled balance between ATP producing and ATP consuming reactions.ConclusionsThe adaptation of the physiology and metabolism of E. coli under substrate dynamics, representative for large-scale fermenters, revealed the existence of several cellular mechanisms coping with stress. Changes in the substrate uptake system, storage potential and energy-spilling processes resulted to be of great importance. These metabolic strategies consist a meaningful step to further tackle reduced microbial performance, observed under large-scale cultivations.

Highlights

  • Microbial metabolism is highly dependent on the environmental conditions

  • Metabolites of the central carbon pathways [glycolysis, pentose phosphate pathway (PPP), tricarboxylic cycle (TCA)] were quantified with GC–MS/MS (7890A GC coupled to a 7000 Quadrupole MS/MS, both from Agilent, Santa Clara, CA, equipped with a CTC Combi PAL autosampler, CTC Analytics AG, Zwingen, Switzerland), as described in [43] and/or anion-exchange LC–MS/MS [44]

  • The adaptation of E. coli to repetitive, dynamic perturbations was evaluated with respect to short and long-term physiological and metabolic characteristics: 1. Comparison between average metabolic rates and yields under repetitive dynamic conditions and steady-state conditions, at the same dilution rate

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Summary

Introduction

Microbial metabolism is highly dependent on the environmental conditions. Especially, the substrate concentration, as well as oxygen availability, determine the metabolic rates. In large-scale bioreactors, microorganisms encounter dynamic conditions in substrate and oxygen availability (mixing limitations), which influence their metabolism and subsequently their physiology. Vasilakou et al Microb Cell Fact (2020) 19:116 is not a trivial process, as strain performance usually declines from lab to industrial-scale bioreactors [5,6,7]. One root of this problem is the mixing limitations, which characterize large-scale bioreactors, and lead to several heterogeneities in the cultivation environment. The substrate concentration should be kept at low levels to avoid overflow metabolism [14,15,16,17] To achieve such conditions, fed-batch or chemostat regimes are applied. The presence and origin of these gradients has been demonstrated by computational fluid dynamic simulations [18, 21,22,23]

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