Astaxanthin is a high-value red pigment and antioxidant used by pharmaceutical, cosmetics, and food industries. The astaxanthin produced chemically is costly and is not approved for human consumption due to the presence of by-products. The astaxanthin production by natural microalgae requires large open areas and specialized equipment, the process takes a long time, and results in low titers. Recombinant microbial cell factories can be engineered to produce astaxanthin by fermentation in standard equipment. In this work, an oleaginous yeast Yarrowia lipolytica was engineered to produce astaxanthin at high titers in submerged fermentation. First, a platform strain was created with an optimised pathway towards β-carotene. The platform strain produced 331 ± 66 mg/L of β-carotene in small-scale cultivation, with the cellular content of 2.25% of dry cell weight. Next, the genes encoding β-ketolase and β-hydroxylase of bacterial (Paracoccus sp. and Pantoea ananatis) and algal (Haematococcus pluvialis) origins were introduced into the platform strain in different copy numbers. The resulting strains were screened for astaxanthin production, and the best strain, containing algal β-ketolase and β-hydroxylase, resulted in astaxanthin titer of 44 ± 1 mg/L. The same strain was cultivated in controlled bioreactors, and a titer of 285 ± 19 mg/L of astaxanthin was obtained after seven days of fermentation on complex medium with glucose. Our study shows the potential of Y. lipolytica as the cell factory for astaxanthin production.

Exploring the dynamic behavior of cellular metabolism requires a standard laboratory method that guarantees rapid sampling and extraction of the cellular content. We propose a versatile sampling technique applicable to cells with different cell wall and cell membrane properties. The technique is based on irreversible electroporation with simultaneous quenching and extraction by using a microfluidic device. By application of electric pulses in the millisecond range, permanent lethal pores are formed in the cell membrane of Escherichia coli and Saccharomyces cerevisiae, facilitating the release of the cellular contents; here demonstrated by the measurement of glucose-6-phosphate and the activity of the enzyme glucose-6-phosphate dehydrogenase. The successful application of this device was demonstrated by pulsed electric field treatment in a flow-through configuration of the microfluidic chip in combination with sampling, inactivation, and extraction of the intracellular content in a few seconds. Minimum electric field strengths of 10 kV/cm for E. coli and 7.5 kV/cm for yeast S. cerevisiae were required for successful cell lysis. The results are discussed in the context of applications in industrial biotechnology, where metabolomics analyses are important.

Engineering microbial cells for the commercial production of biomolecules and biochemicals requires understanding how cells respond to dynamically changing substrate (feast–famine) conditions in industrial-scale bioreactors. Scale-down methods that oscillate substrate are commonly applied to predict the industrial-scale behavior of microbes. We followed a compartment modeling approach to design a scale-down method based on the simulation of an industrial-scale bioreactor. This study uses high cell–density scale-down experiments to investigate Escherichia coli knockout strains of five major glucose-sensitive transcription factors (Cra, Crp, FliA, PrpR, and RpoS) to study their regulatory role during glucose oscillations. RNA-sequencing analysis revealed that the glucose oscillations caused the down-regulation of several stress-related functions in E. coli. An in-depth analysis of strain physiology and transcriptome revealed a distinct phenotype of the strains tested under glucose oscillations. Specifically, the knockout strains of Cra, Crp, and RpoS resulted in a more sensitive transcriptional response than the control strain, while the knockouts of FliA and PrpR responded less severely. These findings imply that the regulation orchestrated by Cra, Crp, and RpoS may be essential for robust E. coli production strains. In contrast, the regulation by FliA and PrpR may be undesirable for temporal oscillations in glucose availability.

Industrial-sized fermenters differ from the laboratory environment in which bioprocess development initially took place. One of the issues that can lead to reduced productivity on a large scale or even early termination of the process is the presence of bioreactor heterogeneities. This work proposes and adopts a design–build–test–learn-type workflow that estimates the substrate, oxygen, and resulting growth heterogeneities through a compartmental modelling approach and maps Yarrowia lipolytica-specific behavior in this relevant range of conditions. The results indicate that at a growth rate of 0.1 h⁻¹, the largest simulated volume (90 m³) reached partial oxygen limitation. Throughout the fed-batch, the cells experienced dissolved oxygen values from 0 to 75% and grew at rates of 0 to 0.2 h⁻¹. These simulated large-scale conditions were tested in small-scale cultivations, which elucidated a transcriptome with a strong downregulation of various transporter and central carbon metabolism genes during oxygen limitation. The relation between oxygen availability and differential gene expression was dynamic and did not show a simple on–off behavior. This indicates that Y. lipolytica can differentiate between different available oxygen concentrations and adjust its transcription accordingly. The workflow presented can be used for Y. lipolytica-based strain engineering, thereby accelerating bioprocess development.

Escherichia coli , a common microbial host for industrial bioproduction, experiences a highly dynamic environment in industrial‐scale bioreactors due to significant glucose and oxygen gradients. In this study, we mimic the combined gradients of glucose and oxygen in high‐throughput bioreactors to study the transcriptional response of E. coli to industrial‐scale conditions. Under oscillating oxygen conditions, E. coli formed less biomass and accumulated the anaerobic by‐product acetate. With respect to oxygen‐responsive genes, we found that genes of the TCA cycle and of different electron transport chain complexes were differentially expressed. A global analysis of the expression data revealed that oxygen oscillations had caused a transition towards a catabolite‐repressed state and upregulation of several stress‐related regulatory programs. Interestingly, the transcriptional changes persisted after oxygen limitation stopped. In contrast, the changes we observed due to glucose starvation, such as induction of the stringent response, were primarily transient. Most importantly, we found that effects of combined oxygen and glucose oscillations were distinct from the ones of oxygen and substrate oscillations alone, suggesting an important interplay between the different metabolic regimes in industrial‐scale bioreactors.

Bacterial growth to high cell densities is commonly observed in infections, microbiomes, and biomanufacturing, yet there is limited understanding of how transcriptional regulation changes to balance between growth and survival under associated stresses. Here, we utilized well-controlled culturing systems to grow wild-type and metabolically engineered Escherichia coli strains into high cell densities (50–80 g _{cell dry weight} L ⁻¹ ) and determine the associated transcriptional dynamics. Knowledge-enriched, machine-learning-based data analytics reveal distinct stress-related gene expression patterns that are consistent with a fundamental trade-off between resistance and persistence. We suggest that this trade-off explains observed growth arrests in high-density cultures and that it results from the disruption of cellular homeostasis, due to re-allocation of limited cellular resources from resistance functions toward increased maintenance requirements. This study deepens our understanding of high-density physiology, illustrates a common link between biomanufacturing and infection, and suggests that persistence phenotypes are programmed as hidden transcriptional states in E. coli . IMPORTANCE: This study presents the first systems-level characterization of Escherichia coli high-cell-density physiology, using an integrated experimental and computational approach. Knowledge-enriched, machine-learning-based analysis of the >470 transcriptomic samples reveals distinct stress-related gene expression patterns that allow the first functional and quantitative description of associated stimulons. The identified stimulons reveal a hitherto undiscovered trade-off between resistance- and persistence-like functions. Our findings have fundamental implications for genome editing of strains optimized for resilience toward stresses of cell-dense environments, particularly those encountered in biomanufacturing.