Comparative performance of different scale-down simulators of substrate gradients in Penicillium chrysogenum cultures: the need of a biological systems response analysis

Penicillium chrysogenum: Beyond the penicillin

Almost one century after the Sir Alexander Fleming's fortuitous discovery of penicillin and the identification of the fungal producer as Penicillium notatum, later Penicillium chrysogenum (currently reidentified as Penicillium rubens), the molecular mechanisms behind the massive production of penicillin titers by industrial strains could be considered almost fully characterized. However, this filamentous fungus is not only circumscribed to penicillin, and instead, it seems to be full of surprises, thereby producing important metabolites and providing expanded biotechnological applications. This review, in addition to summarizing the classical role of P. chrysogenum as penicillin producer, highlights its ability to generate an array of additional bioactive secondary metabolites and enzymes, together with the use of this microorganism in relevant biotechnological processes, such as bioremediation, biocontrol, production of bioactive nanoparticles and compounds with pharmaceutical interest, revalorization of agricultural and food-derived wastes or the enhancement of food industrial processes and the agricultural production.

Developing rational scale-down simulators for mimicking substrate heterogeneities based on cell lifelines in industrial-scale bioreactors

The influence of extracellular variations on the cellular metabolism and thereby the process performance at large-scale can be evaluated using the so-called scale-down simulators. Nevertheless, the major challenge is to design an appropriate scale-down simulator, which can accurately mimic the cell lifelines that record the flow paths and experiences of cells circulating in large-scale bioreactors. To address this, a dedicated SDSA (scale-down simulator application) was purposedly developed on the basis of black box model and process reaction model established for Penicillium chrysogenum strain as well as cell lifelines or trajectories information in an industrial-scale fermentor. Guided by the SDSA, the industrial-relevant metabolic regimes for substrate availability, i.e., excess, limitation and starvation, were successfully reproduced at laboratory-scale three-compartment scale-down (SD) system. In addition, such SDSA can also display individual process dynamics in each compartment, and demonstrate how individual factors influence the entire bioprocess performance, thus serving both educational and research purposes.

Machine learning in bioprocess development: from promise to practice

Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

Microbial lifelines in bioprocesses: From concept to application

Bioprocesses are scaled up for the production of large product quantities. With larger fermenter volumes, mixing becomes increasingly inefficient and environmental gradients get more prominent than in smaller scales. Environmental gradients have an impact on the microorganism's metabolism, which makes the prediction of large-scale performance difficult and can lead to scale-up failure. A promising approach for improved understanding and estimation of dynamics of microbial populations in large-scale bioprocesses is the analysis of microbial lifelines. The lifeline of a microbe in a bioprocess is the experience of environmental gradients from a cell's perspective, which can be described as a time series of position, environment and intracellular condition. Currently, lifelines are predominantly determined using models with computational fluid dynamics, but new technical developments in flow-following sensor particles and microfluidic single-cell cultivation open the door to a more interdisciplinary concept. We critically review the current concepts and challenges in lifeline determination and application of lifeline analysis, as well as strategies for the integration of these techniques into bioprocess development. Lifelines can contribute to a successful scale-up by guiding scale-down experiments and identifying strain engineering targets or bioreactor optimisations.

Modeling multiphase fluid flow, mass transfer, and chemical reactions in bioreactors using large-eddy simulation

We present a transient large eddy simulation (LES) modeling approach for simulating the interlinked physics describing free surface hydrodynamics, multiphase mixing, reaction kinetics, and mass transport in bioreactor systems. Presented case-studies include non-reacting and reacting bioreactor systems, modeled through the inclusion of uniform reaction rates and more complex biochemical reactions described using Contois type kinetics. It is shown that the presence of reactions can result in a non-uniform spatially varying species concentration field, the magnitude and extent of which is directly related to the reaction rates and the underlying variations in the local volumetric mass transfer coefficient.

Optimization and Scale-Up of Fermentation Processes Driven by Models

In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.

Changes in Oxygen Availability during Glucose-Limited Chemostat Cultivations of Penicillium chrysogenum Lead to Rapid Metabolite, Flux and Productivity Responses

Bioreactor scale-up from the laboratory scale to the industrial scale has always been a pivotal step in bioprocess development. However, the transition of a bioeconomy from innovation to commercialization is often hampered by performance loss in titer, rate and yield. These are often ascribed to temporal variations of substrate and dissolved oxygen (for instance) in the environment, experienced by microorganisms at the industrial scale. Oscillations in dissolved oxygen (DO) concentration are not uncommon. Furthermore, these fluctuations can be exacerbated with poor mixing and mass transfer limitations, especially in fermentations with filamentous fungus as the microbial cell factory. In this work, the response of glucose-limited chemostat cultures of an industrial Penicillium chrysogenum strain to different dissolved oxygen levels was assessed under both DO shift-down (60% → 20%, 10% and 5%) and DO ramp-down (60% → 0% in 24 h) conditions. Collectively, the results revealed that the penicillin productivity decreased as the DO level dropped down below 20%, while the byproducts, e.g., 6-oxopiperidine-2-carboxylic acid (OPC) and 6-aminopenicillanic acid (6APA), accumulated. Following DO ramp-down, penicillin productivity under DO shift-up experiments returned to its maximum value in 60 h when the DO was reset to 60%. The result showed that a higher cytosolic redox status, indicated by NADH/NAD+, was observed in the presence of insufficient oxygen supply. Consistent with this, flux balance analysis indicated that the flux through the glyoxylate shunt was increased by a factor of 50 at a DO value of 5% compared to the reference control, favoring the maintenance of redox status. Interestingly, it was observed that, in comparison with the reference control, the penicillin productivity was reduced by 25% at a DO value of 5% under steady state conditions. Only a 14% reduction in penicillin productivity was observed as the DO level was ramped down to 0. Furthermore, intracellular levels of amino acids were less sensitive to DO levels at DO shift-down relative to DO ramp-down conditions; this difference could be caused by different timescales between turnover rates of amino acid pools (tens of seconds to minutes) and DO switches (hours to days at steady state and minutes to hours at ramp-down). In summary, this study showed that changes in oxygen availability can lead to rapid metabolite, flux and productivity responses, and dynamic DO perturbations could provide insight into understanding of metabolic responses in large-scale bioreactors.

Dynamic response of Aspergillus niger to periodical glucose pulse stimuli in chemostat cultures

In industrial large-scale bioreactors, microorganisms encounter heterogeneous substrate concentration conditions, which can impact growth or product formation. Here we carried out an extended (12 h) experiment of repeated glucose pulsing with a 10-min period to simulate fluctuating glucose concentrations with Aspergillus niger producing glucoamylase, and investigated its dynamic response by rapid sampling and quantitative metabolomics. The 10-min period represents worst-case conditions, as in industrial bioreactors the average cycling duration is usually in the order of 1 min. We found that cell growth and the glucoamylase productivity were not significantly affected, despite striking metabolomic dynamics. Periodical dynamic responses were found across all central carbon metabolism pathways, with different time scales, and the frequently reported ATP paradox was confirmed for this A. niger strain under the dynamic conditions. A thermodynamics analysis revealed that several reactions of the central carbon metabolism remained in equilibrium even under periodical dynamic conditions. The dynamic response profiles of the intracellular metabolites did not change during the pulse exposure, showing no significant adaptation of the strain to the more than 60 perturbation cycles applied. The apparent high tolerance of the glucoamylase producing A. niger strain for extreme variations in the glucose availability presents valuable information for the design of robust industrial microbial hosts.

Impact of Altered Trehalose Metabolism on Physiological Response of Penicillium chrysogenum Chemostat Cultures during Industrially Relevant Rapid Feast/Famine Conditions

Due to insufficient mass transfer and mixing issues, cells in the industrial-scale bioreactor are often forced to experience glucose feast/famine cycles, mostly resulting in reduced commercial metrics (titer, yield and productivity). Trehalose cycling has been confirmed as a double-edged sword in the Penicillium chrysogenum strain, which facilitates the maintenance of a metabolically balanced state, but it consumes extra amounts of the ATP responsible for the repeated breakdown and formation of trehalose molecules in response to extracellular glucose perturbations. This loss of ATP would be in competition with the high ATP-demanding penicillin biosynthesis. In this work, the role of trehalose metabolism was further explored under industrially relevant conditions by cultivating a high-yielding Penicillium chrysogenum strain, and the derived trehalose-null strains in the glucose-limited chemostat system where the glucose feast/famine condition was imposed. This dynamic feast/famine regime with a block-wise feed/no feed regime (36 s on, 324 s off) allows one to generate repetitive cycles of moderate changes in glucose availability. The results obtained using quantitative metabolomics and stoichiometric analysis revealed that the intact trehalose metabolism is vitally important for maintaining penicillin production capacity in the Penicillium chrysogenum strain under both steady state and dynamic conditions. Additionally, cells lacking such a key metabolic regulator would become more sensitive to industrially relevant conditions, and are more able to sustain metabolic rearrangements, which manifests in the shrinkage of the central metabolite pool size and the formation of ATP-consuming futile cycles.

Understanding gradients in industrial bioreactors

Gradients in industrial bioreactors have attracted substantial research attention since exposure to fluctuating environmental conditions has been shown to lead to changes in the metabolome, transcriptome as well as population heterogeneity in industrially relevant microorganisms. Such changes have also been found to impact key process parameters like the yield on substrate and the productivity. Hence, understanding gradients is important from both the academic and industrial perspectives. In this review the causes of gradients are outlined, along with their impact on microbial physiology. Quantifying the impact of gradients requires a detailed understanding of both fluid flow inside industrial equipment and microbial physiology. This review critically examines approaches used to investigate gradients including large-scale experimental work, computational methods and scale-down approaches. Avenues for future work have been highlighted, particularly the need for further coordinated development of both in silico and experimental tools which can be used to further the current understanding of gradients in industrial equipment.

Development, characterization, and application of a 2-Compartment system to investigate the impact of pH inhomogeneities in large-scale CHO-based processes

Large-scale bioreactors for the production of monoclonal antibodies reach volumes of up to 25 000 L. With increasing bioreactor size, mixing is however affected negatively, resulting in the formation of gradients throughout the reactor. These gradients can adversely affect process performance at large scale. Since mammalian cells are sensitive to changes in pH, this study investigated the effects of pH gradients on process performance. A 2-Compartment System was established for this purpose to expose only a fraction of the cell population to pH excursions and thereby mimicking a large-scale bioreactor. Cells were exposed to repeated pH amplitudes of 0.4 units (pH 7.3), which resulted in decreased viable cell counts, as well as the inhibition of the lactate metabolic shift. These effects were furthermore accompanied by increased absolute lactate levels. Continuous assessment of molecular attributes of the expressed target protein revealed that subunit assembly or N-glycosylation patterns were only slightly influenced by the pH excursions. The exposure of more cells to the same pH amplitudes further impaired process performance, indicating this is an important factor, which influences the impact of pH inhomogeneity. This knowledge can aid in the design of pH control strategies to minimize the effects of pH inhomogeneity in large-scale bioreactors.

Scale-down simulators for mammalian cell culture as tools to access the impact of inhomogeneities occurring in large-scale bioreactors

During the scale-up of a bioprocess, not all characteristics of the process can be kept constant throughout the different scales. This typically results in increased mixing times with increasing reactor volumes. The poor mixing leads in turn to the formation of concentration gradients throughout the reactor and exposes cells to varying external conditions based on their location in the bioreactor. This can affect process performance and complicate process scale-up. Scale-down simulators, which aim at replicating the large-scale environment, expose the cells to changing environmental conditions. This has the potential to reveal adaptation mechanisms, which cells are using to adjust to rapidly fluctuating environmental conditions and can identify possible root causes for difficulties maintaining similar process performance at different scales. This understanding is of utmost importance in process validation. Additionally, these simulators also have the potential to be used for selecting cells, which are most robust when encountering changing extracellular conditions. The aim of this review is to summarize recent work in this interesting and promising area with the focus on mammalian bioprocesses, since microbial processes have been extensively reviewed.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.

Study of characteristics on metabolism of Penicillium chrysogenum F1 during bioleaching of heavy metals from contaminated soil

Penicillium chrysogenum F1 is very efficient in bioleaching heavy metals from the soil and is used for that purpose. We found that F1 can extract 19.8 mg Cd, Cu, Pb, and Zn from 2.5 g soil; the total heavy metals’ bioleaching ratio was 60.4%. In this study, the bioleaching mechanism was investigated by means of metabonomics; different metabolite ions were screened (relative standard deviation >30%) and analyzed using clustering, univariate and multivariate analysis. Statistical analyses via Volcano Plot, principal component analysis, and partial least square discriminant analysis models revealed a difference between Ctrl 7 (the controls cultured and sampled on day 7) and Ctrl 15 (the controls cultured and sampled on day 15). Samp 15 (the samples cultured with heavy-metal-contaminated soil) was significantly different from Ctrl 7 and Ctrl 15. Analysis of the different ions demonstrated that the glucose catabolism pathways of glycolysis and the tricarboxylic acid (TCA) cycle were enhanced, and glucose anabolism through the pentose phosphate pathway was inhibited during bioleaching. At the same time, the metabolism of glutathione was also downregulated. Therefore, the catabolism of glucose was unaffected by the addition of heavy-metal-contaminated soil, and increasing glucose is beneficial to catabolism. The extraction of metals is mainly attributed to the metabolites of the TCA cycle.

Comparative performance of different scale-down simulators of substrate gradients in Penicillium chrysogenum cultures: the need of a biological systems response analysis

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 (τc) 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.