Obtaining meaningful snapshots of the metabolome of microorganisms requires rapid sampling and immediate quenching of all metabolic activity, to prevent any changes in metabolite levels after sampling. Furthermore, a suitable extraction method is required ensuring complete extraction of metabolites from the cells and inactivation of enzymatic activity, with minimal degradation of labile compounds. Finally, a sensitive, high-throughput analysis platform is needed to quantify a large number of metabolites in a small amount of sample. An issue which has often been overlooked in microbial metabolomics is the fact that many intracellular metabolites are also present in significant amounts outside the cells and may interfere with the quantification of the endo metabolome. Attempts to remove the extracellular metabolites with dedicated quenching methods often induce release of intracellular metabolites into the quenching solution. For eukaryotic microorganisms, this release can be minimized by adaptation of the quenching method. For prokaryotic cells, this has not yet been accomplished, so the application of a differential method whereby metabolites are measured in the culture supernatant as well as in total broth samples, to calculate the intracellular levels by subtraction, seems to be the most suitable approach. Here we present an overview of different sampling, quenching, and extraction methods developed for microbial metabolomics, described in the literature. Detailed protocols are provided for rapid sampling, quenching, and extraction, for measurement of metabolites in total broth samples, washed cell samples, and supernatant, to be applied for quantitative metabolomics of both eukaryotic and prokaryotic microorganisms.

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In a 54 m³ large-scale penicillin fermentor, the cells experience substrate gradient cycles at the timescales of global mixing time about 20–40 s. Here, we used an intermittent feeding regime (IFR) and a two-compartment reactor (TCR) to mimic these substrate gradients at laboratory-scale continuous cultures. The IFR was applied to simulate substrate dynamics experienced by the cells at full scale at timescales of tens of seconds to minutes (30 s, 3 min and 6 min), while the TCR was designed to simulate substrate gradients at an applied mean residence time ((Formula presented.)) of 6 min. A biological systems analysis of the response of an industrial high-yielding P. chrysogenum strain has been performed in these continuous cultures. Compared to an undisturbed continuous feeding regime in a single reactor, the penicillin productivity (q_PenG) was reduced in all scale-down simulators. The dynamic metabolomics data indicated that in the IFRs, the cells accumulated high levels of the central metabolites during the feast phase to actively cope with external substrate deprivation during the famine phase. In contrast, in the TCR system, the storage pool (e.g. mannitol and arabitol) constituted a large contribution of carbon supply in the non-feed compartment. Further, transcript analysis revealed that all scale-down simulators gave different expression levels of the glucose/hexose transporter genes and the penicillin gene clusters. The results showed that q_PenG did not correlate well with exposure to the substrate regimes (excess, limitation and starvation), but there was a clear inverse relation between q_PenG and the intracellular glucose level.

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In the present work, by performing chemostat experiments at 400 and 600 RPM, two typical power inputs representative of industrial penicillin fermentation (P/V, 1.00 kW/m³ in more remote zones and 3.83 kW/m³ in the vicinity of the impellers, respectively) were scaled-down to bench-scale bioreactors. It was found that at 400 RPM applied in prolonged glucose-limited chemostat cultures, the previously reported degeneration of penicillin production using an industrial Penicillium chrysogenum strain was virtually absent. To investigate this, the cellular response was studied at flux (stoichiometry), residual glucose, intracellular metabolite and transcript levels. At 600 RPM, 20% more cell lysis was observed and the increased degeneration of penicillin production was accompanied by a 22% larger ATP gap and an unexpected 20-fold decrease in the residual glucose concentration (C_s,out). At the same time, the biomass specific glucose consumption rate (q_s) did not change but the intracellular glucose concentration was about sixfold higher, which indicates a change to a higher affinity glucose transporter at 600 RPM. In addition, power input differences cause differences in the diffusion rates of glucose and the calculated Batchelor diffusion length scale suggests the presence of a glucose diffusion layer at the glucose transporting parts of the hyphae, which was further substantiated by a simple proposed glucose diffusion-uptake model. By analysis of calculated mass action ratios (MARs) and energy consumption, it indicated that at 600 RPM glucose sensing and signal transduction in response to the low C_s,out appear to trigger a gluconeogenic type of metabolic flux rearrangement, a futile cycle through the pentose phosphate pathway (PPP) and a declining redox state of the cytosol. In support of the change in glucose transport and degeneration of penicillin production at 600 RPM, the transcript levels of the putative high-affinity glucose/hexose transporter genes Pc12g02880 and Pc06g01340 increased 3.5- and 3.3-fold, respectively, and those of the pcbC gene encoding isopenicillin N-synthetase (IPNS) were more than twofold lower in the time range of 100–200 hr of the chemostat cultures. Summarizing, changes at power input have unexpected effects on degeneration and glucose transport, and result in significant metabolic rearrangements. These findings are relevant for the industrial production of penicillin, and other fermentations with filamentous microorganisms.

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We assess the effect of substrate heterogeneity on the metabolic response of P. chrysogenum in industrial bioreactors via the coupling of a 9-pool metabolic model with Euler-Lagrange CFD simulations. In this work, we outline how this coupled hydrodynamic-metabolic modeling can be utilized in 5 steps. (1) A model response study with a fixed spatial extra-cellular glucose concentration gradient, which reveals a drop in penicillin production rate q_p of 18–50% for the simulated reactor, depending on model setup. (2) CFD-based scale-down design, where we design a 1-vessel scale down simulator based on the organism lifelines. (3) Scale-down verification, numerically comparing the model response in the proposed scale-down simulator with large-scale CFD response. (4) Reactor design optimization, reducing the drop in penicillin production by a change of feed location. (5) Long-term fed-batch simulation, where we verify model predictions against experimental data, and discuss population heterogeneity. Overall, these steps present a coupled hydrodynamic-metabolic approach towards bioreactor evaluation, scale-down and optimization.

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This chapter deals with fermentation processes, converting low cost renewable feedstocks into valuable bio-products, with the help of microorganisms or mammalian cells in bioreactors or fermenters. In industrial vessels, the volumetric conversion rate, i.e. the fermentation intensity, is limited by a transport step: mass transfer, liquid mixing or cooling. In special processes where the growth of the cells is marginal, intensification is possible by active cell retention. A comparison with chemical process intensification reveals that the same four main principles are valid, i.e. (1) maximize the rate at optimal selectivity, (2) minimize the impact of substrate concentration gradients, shear rate gradients and other local differences, (3) relieve the transport limitations and (4) arrange smart integration of operation steps of which cell retention is the most important. In essence, optimized microorganisms in fermentations can be viewed as efficient, smartly integrated cell factories. The main principles are illustrated with four intensification examples, showing that debottlenecking of the oxygen transfer capacity is most important, followed by liquid mixing. The limits of fermentation intensity have been estimated for fed-batch fermentations supplied with air or pure oxygen and point at significant optimization space. In contrast, aerobic continuous fermentation is expected to remain difficult due to fundamental restrictions.

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A powerful approach for the optimization of industrial bioprocesses is to perform detailed simulations integrating large-scale computational fluid dynamics (CFD) and cellular reaction dynamics (CRD). However, complex metabolic kinetic models containing a large number of equations pose formidable challenges in CFD-CRD coupling and computation time afterward. This necessitates to formulate a relatively simple but yet representative model structure. Such a kinetic model should be able to reproduce metabolic responses for short-term (mixing time scale of tens of seconds) and long-term (fed-batch cultivation of hours/days) dynamics in industrial bioprocesses. In this paper, we used Penicillium chrysogenum as a model system and developed a metabolically structured kinetic model for growth and production. By lumping the most important intracellular metabolites in 5 pools and 4 intracellular enzyme pools, linked by 10 reactions, we succeeded in maintaining the model structure relatively simple, while providing informative insight into the state of the organism. The performance of this 9-pool model was validated with a periodic glucose feast–famine cycle experiment at the minute time scale. Comparison of this model and a reported black box model for this strain shows the necessity of employing a structured model under feast–famine conditions. This proposed model provides deeper insight into the in vivo kinetics and, most importantly, can be straightforwardly integrated into a computational fluid dynamic framework for simulating complete fermentation performance and cell population dynamics in large scale and small scale fermentors. Biotechnol. Bioeng. 2017;114: 1733–1743.

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In its natural environment, the filamentous fungus Aspergillus niger grows on decaying fruits and plant material, thereby enzymatically degrading the lignocellulosic constituents (lignin, cellulose, hemicellulose, and pectin) into a mixture of mono- and oligosaccharides. To investigate the kinetics and stoichiometry of growth of this fungus on lignocellulosic sugars, we carried out batch cultivations on six representative monosaccharides (glucose, xylose, mannose, rhamnose, arabinose, and galacturonic acid) and a mixture of these. Growth on these substrates was characterized in terms of biomass yields, oxygen/biomass ratios, and specific conversion rates. Interestingly, in combination, some of the carbon sources were consumed simultaneously and some sequentially. With a previously developed protocol, a sequential chemostat cultivation experiment was performed on a feed mixture of the six substrates. We found that the uptake of glucose, xylose, and mannose could be described with a Michaelis–Menten-type kinetics; however, these carbon sources seem to be competing for the same transport systems, while the uptake of arabinose, galacturonic acid, and rhamnose appeared to be repressed by the presence of other substrates.

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Building on the recent revolution in molecular biology, enabling a wealth of bio-product innovations made from renewable feedstocks, the biotechnology field is in a transition phase to bring the products to the market. This requires a shift from natural sciences to engineering sciences with first conception of new, efficient large-scale bioprocess designs, followed by implementation of the most promising design in practice. Inspired by a former publication by O. Levenspiel in 1988, an outline is presented of main challenges that the field of biochemical engineering is currently facing, in a context of major global sustainability trends. The critical stage is the conceptual design phase. Issues can best be addressed and overcome by adopting an attitude where one begins with the end in mind. This applies to three principal components: 1. the bioprocess value chain, where the product specifications and downstream purification schemes should be set before defining the upstream sections, 2. the time perspective, starting in the future assuming that feedstock and product-market combinations are already in place and then going back to today, and 3. the scale of operation, where the industrial operation sets the boundaries for all labscale research and development, and not vice versa. In this way, and ideal process is defined taking constraints from anticipated manufacturing into account. For illustration, three bioprocess design examples are provided, that show how new, ideal conceptual designs can be generated. These also make clear that the engineering sciences are undergoing a revolution, where bio-based approaches replace fossil routes, and gross simplification is replaced by highly detailed computational methods. For biochemical processes, lifeline modeling frameworks are highlighted as powerful means to reconcile the competing needs for high speed and high quality in biochemical engineering, both in the design and implementation stages, thereby enabling significant growth of the bio-based economy.

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The objective of this research study is to be able to scale-up and optimize aerobic fermentation processes via computer simulations. As an example the production of penicillin by P. chrysogenum is studied. In vivo kinetics of the cell can be understood by conducting stimulus response and scale down experiments. These experiments provide dynamic information that can be used to estimate parameters of enzyme kinetic models. The transport phenomena inside the bioreactor can by understood by solving hydrodynamic model using computational fluid dynamics (CFD). Thus, with information from intracellular and extracellular environments, an integrated model is solved using CFD. In these simulations, the multiphase flow and transport phenomena taking place inside the bioreactor are simulated, and integrated with the microbial reaction kinetics. The micro-organisms inside the reactor are followed along their trajectories, where they experience a changing environment. The dynamic response of the cells to this environment dictates the productivity and selectivity of the cells. By simulating both the flow in the reactor and the response of the cells, the entire process inside the bioreactor can be assessed and scaled-up and optimization can be done without the need of costly experiments, both in time and financial resources.@en

Ammonium (NH4+) is the most common N-source for yeast fermentations, and N-limitation is frequently applied to reduce growth and increase product yields. While there is significant molecular knowledge on NH4 + transport and assimilation, there have been few attempts to measure the in vivo concentration of this metabolite. In this article, we present a sensitive and accurate analytical method to quantify the in vivo intracellular ammonium concentration in
Saccharomyces cerevisiae based on standard rapid sampling and metabolomics techniques. The method validation experiments required the development of a proper sample processing protocol to minimize ammonium production/consumption during biomass extraction by assessing the impact of amino acid degradation—an element that is often overlooked. The resulting cold chloroform metabolite extraction method, together with quantification using ultra high performance liquid chromatography-isotope dilution mass spectrometry (UHPLC-IDMS), was not only more sensitive than most of the existing methods but also more accurate than methods that use electrodes, enzymatic reactions, or boiling water or boiling ethanol biomass extraction because it minimized ammonium consumption/production during sampling processing and interference from other metabolites in the quantification of intracellular ammonium. Finally, our validation experiments showed that other
metabolites such as pyruvate or 2-oxoglutarate (KG) need to be extracted with cold chloroform to avoid measurements being biased by the degradation of other metabolites (e.g., amino acids).@en

Ammonium is the most common N source for yeast fermentations. Although its transport and assimilation mechanisms are well documented, there have been only a few attempts to measure the in vivo intracellular concentration of ammonium and assess its impact on gene expression. Using an isotope dilution mass spectrometry (IDMS)-based method, we were able to measure the intracellular ammonium concentration in N-limited aerobic chemostat cultivations using three different N sources (ammonium, urea, and glutamate) at the same growth rate (0.05 h^-1). The experimental results suggest that, at this growth rate, a similar concentration of intracellular (IC) ammonium, about 3.6 mmol NH₄ ⁺/liter_IC, is required to supply the reactions in the central N metabolism, independent of the N source. Based on the experimental results and different assumptions, the vacuolar and cytosolic ammonium concentrations were estimated. Furthermore, we identified a futile cycle caused by NH₃ leakage into the extracellular space, which can cost up to 30% of the ATP production of the cell under N-limited conditions, and a futile redox cycle between Gdh1 and Gdh2 reactions. Finally, using shotgun proteomics with protein expression determined relative to a labeled reference, differences between the various environmental conditions were identified and correlated with previously identified N compound-sensing mechanisms.

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Currently, research is being focused on the industrial-scale production of fumaric acid and other relevant organic acids from renewable feedstocks via fermentation, preferably at low pH for better product recovery. However, at low pH a large fraction of the extracellular acid is present in the undissociated form, which is lipophilic and can diffuse into the cell. There have been no studies done on the impact of high extracellular concentrations of fumaric acid under aerobic conditions in S. cerevisiae, which is a relevant issue to study for industrial-scale production. In this work we studied the uptake and metabolism of fumaric acid in S. cerevisiae in glucose-limited chemostat cultures at a cultivation pH of 3.0 (pH

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The trajectories, referred to as lifelines, of individual microorganisms in an industrial scale fermentor under substrate limiting conditions were studied using an Euler-Lagrange computational fluid dynamics approach. The metabolic response to substrate concentration variations along these lifelines provides deep insight in the dynamic environment inside a large-scale fermentor, from the point of view of the microorganisms themselves. We present a novel methodology to evaluate this metabolic response, based on transitions between metabolic “regimes” that can provide a comprehensive statistical insight in the environmental fluctuations experienced by microorganisms inside an industrial bioreactor. These statistics provide the groundwork for the design of representative scale-down simulators, mimicking substrate variations experimentally. To focus on the methodology we use an industrial fermentation of Penicillium chrysogenum in a simplified representation, dealing with only glucose gradients, single-phase hydrodynamics, and assuming no limitation in oxygen supply, but reasonably capturing the relevant timescales. Nevertheless, the methodology provides useful insight in the relation between flow and component fluctuation timescales that are expected to hold in physically more thorough simulations. Microorganisms experience substrate fluctuations at timescales of seconds, in the order of magnitude of the global circulation time. Such rapid fluctuations should be replicated in truly industrially representative scale-down simulators.

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The field of metabolic engineering has delivered new microbial cell factories and processes for the production of different compounds including biofuels, (di)carboxylic acids, alcohols, and amino acids. Most of these processes are aerobic, with few exceptions (e.g., alcoholic fermentation), and attention is focused on assembling a high-flux product pathway with a production limit usually set by the oxygen transfer rate. By contrast, anaerobic product synthesis offers significant benefits compared to aerobic systems: higher yields, less heat generation, reduced biomass production, and lower mechanical energy input, which can significantly reduce production costs. Using simple thermodynamic calculations, we demonstrate that many products can theoretically be produced under anaerobic conditions using several conventional and non-conventional substrates.

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Eukaryotic metabolism consists of a complex network of enzymatic reactions and transport processes which are distributed over different subcellular compartments. Currently, available metabolite measurement protocols allow to measure metabolite whole cell amounts which hinder progress to describe the in vivo dynamics in different compartments, which are driven by compartment specific concentrations. Phosphate (Pi) is an essential component for: (1) the metabolic balance of upper and lower glycolytic flux; (2) Together with ATP and ADP determines the phosphorylation energy. Especially, the cytosolic Pi has a critical role in disregulation of glycolysis in tps1 knockout. Here we developed a method that enables us to monitor the cytosolic Pi concentration in S. cerevisiae using an equilibrium sensor reaction: maltose+Pi glucose+glucose-1-phosphate. The required enzyme, maltose phosphorylase from L. sanfranciscensis was overexpressed in S. cerevisiae. With this reaction in place, the cytosolic Pi concentration was obtained from intracellular glucose, G1P and maltose concentrations. The cytosolic Pi concentration was determined in batch and chemostat (D=0.1 h^-1) conditions, which was 17.88μmol/gDW and 25.02μmol/gDW, respectively under Pi-excess conditions. Under Pi-limited steady state (D=0.1 h^-1) conditions, the cytosolic Pi concentration dropped to only 17.7% of the cytosolic Pi in Pi-excess condition (4.42μmol/gDW vs. 25.02μmol/gDW). In response to a Pi pulse, the cytosolic Pi increased very rapidly, together with the concentration of sugar phosphates. Main sources of the rapid Pi increase are vacuolar Pi (and not the polyPi), as well as Pi uptake from the extracellular space. The temporal increase of cytosolic Pi increases the driving force of GAPDH reaction of the lower glycolytic reactions. The novel cytosol specific Pi concentration measurements provide new insight into the thermodynamic driving force for ATP hydrolysis, GAPDH reaction, and Pi transport over the plasma and vacuolar membranes.

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