Dynamic
environmental conditions govern microbial metabolism and affect cellular
growth. Many applications in biotechnology require cultivating microorganisms
in large-scale bioreactors. These environments are commonly characterized by
physicochemical gradients, due to imperfect
...
Dynamic
environmental conditions govern microbial metabolism and affect cellular
growth. Many applications in biotechnology require cultivating microorganisms
in large-scale bioreactors. These environments are commonly characterized by
physicochemical gradients, due to imperfect mixing and have been the cause of
reduced performance of cell factories in industry. Changes in substrate and gas
concentrations, pH and temperature are some example of the generated gradients.
The aim of this thesis is to unravel and
understand the effects of repetitive substrate fluctuations on the cellular
behaviour of Escherichia coli K12 MG1655, using experimental and modelling
approaches. Chapter 1 is a general introduction to biotechnology and its
applications, with a focus on upstream bioprocesses. In addition, the role of
the bacterium Escherichia coli as a model organism, as well as a working horse
of biotechnology, is discussed. In Chapter 2, the quantitative experimental and
kinetic modelling approaches, currently used for studying microbial metabolism
under dynamic conditions, are summarized and discussed. Current challenges and
future perspectives finalize this chapter. In the experimental Chapter 3, a block-wise
feeding regime was applied to an aerobic E.coli culture, with the aim to grow
cells under substrate (glucose) gradients, following a reference chemostat
(steady-state) growth. This regime was called “fast feast-famine”, as cells
experienced periods of substrate excess, limitation and depletion in a
time-scale of seconds. The regime was characterized by repetitive cycles of 20
s feeding and 380 s without feeding. The perturbations were applied for at
least 8 generations, allowing the cells to adapt to the dynamic environment
(highly reproducible cellular response). The specific substrate and oxygen
consumption (average) rates increased during the feast-famine regime, compared
to the reference steady state cultivation. The increased rates at same
(average) growth rate led to a reduced biomass yield (30% lower), while there
was no significant by-product formation. Such observation suggests the
emergence of energy spilling reactions. With the increase in extracellular
substrate concentration, the cells rapidly increased their uptake rate. Within
10 seconds after the beginning of the feeding, the glucose 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 intracellular
metabolite concentrations changed rapidly, the cellular energy charge remained
homeostatic, suggesting a well-controlled balance between ATP producing and ATP
consuming reactions. The importance of
combining experimental perturbation studies and kinetic modelling, in order to
reveal metabolic strategies for coping with dynamic conditions is highlighted
in the following Chapter 4. In Chapter 4, a published kinetic model for central
carbon metabolism by Peskov K, et al. was used to investigate if the
experimental observations from Chapter 3 could be reproduced with a model
originating from steady-state calibration. Only after parameter optimization,
with significant changes, could the data be reproduced, highlighting
significant alterations in the enzymatic kinetics of glycolysis during
feast-famine, compared to steady-state growth. Post transcriptional
modifications were assumed to explain the sudden decrease in the substrate
uptake rate, observed while glucose was still in excess. To reflect such change
in the modelling approach, the feast-famine cycle was split into two phases and
the experimental uptake rate was used as fixed input. Nevertheless, this was
not yet sufficient to fully reproduce the experimental observations. The time
course of the glycolytic intermediates could only be reproduced when
introducing glycogen synthesis and assimilation in the model. Here, glycogen
acted as a storage pool, providing carbon and energy to reinitiate growth
during famine conditions. Furthermore, ATP spilling reactions were needed to
reproduce the observed adenylate energy homeostasis. Additionally, a continuous
draining of ATP supported the hypothesis of increased maintenance during the
feast-famine regime. In Chapter 5, multi-omics approaches, i.e. shotgun
cellular proteomics and 13C-labelled metabolomics were used for untargeted
analysis and generation of new hypotheses on cellular regulatory mechanisms,
when cells were subjected to fluctuations in substrate availability. The
extracellular dynamics were expected to trigger global stress responses, in
line with the observed reduced biomass yield. Surprisingly, this was not the
case – stress related proteins did not alter from steady-state to feast-famine
conditions. On the other hand, the cellular proteome adjusted for specific
functional categories, including biosynthesis and translation processes
(ribosomes). This increase can be explained by either increased protein
production to support the rapid growth changes, during the short time of
substrate availability, or ribosome stalling due to amino acid limitation
during the famine phase. During substrate-limited growth (constant feeding)
cells have an overcapacity of metabolic enzymes (involved in central carbon
pathways), which is used under nutrient up-shift to handle rapid increase in
metabolic fluxes. The down-regulation of several enzymes in glycolysis, TCA
cycle and pentose phosphate pathway, as well as, transporter proteins, revealed
that cells respond more to the substrate excess period than the starvation
period during the block-wise feeding regime. This is also in accordance with
the observed down-regulation of the glyoxylate-shunt enzymes. Moreover, the
increased levels of polyphosphate kinase indicated the use of a polyphosphate
pool as a putative buffer for energy homeostasis. Glycogen production and
degradation was verified by the proteomic and 13C tracing analysis and is
suggested to contribute to the ATP spilling (biomass yield losses), along with
the increased protein turnover, which was identified by an increased section of
the cellular proteasome. The generated insights of the whole thesis are
summarized in Chapter 6. Additionally, open questions are discussed. The future
challenges include scale-down experiments, research on the effects of dynamics
on production hosts, the use of mutant strains for validation experiments and
data integration toward multi-scale modeling.
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