Real-time monitoring of the budding index in Saccharomyces cerevisiae batch cultivations with in situ microscopy

Adaptation of Saccharomyces to High Glucose Concentrations and Its Impact on Growth Kinetics of Alcoholic Fermentations

Prior adaptation of Saccharomyces cerevisiae to the fermentation medium ensures its implantation and success in alcoholic fermentations. Fermentation kinetics can be characterized with mathematical models to objectively measure the success of adaptation and growth. The study aims at assessing and comparing two pre-culture procedures using, respectively, one or two adaptation steps, analyzing the impact of different initial glucose concentrations on the fermentation profiles of S. cerevisiae cultures, and assessing the performance of three predictive growth models (Buchanan's, modified Gompertz, and Baranyi and Roberts models) under varied initial glucose concentrations. We concluded that both protocols produced S. cerevisiae pre-cultures with similar viability and biomass increase, which suggests that short protocols may be more cost-effective. Furthermore, the study highlights the need of inoculating a high S. cerevisiae population to minimize the depletion of dissolved oxygen in the medium and to ensure that glucose is predominantly directed toward the ethanol formation at early fermentative steps. This study shows that the relationship between kinetic parameters is model-dependent, which hinders inter-study comparisons and stresses the need for standardized growth models. We advocate for the generalized use of confidence intervals of the kinetic parameters to facilitate objective inter-study comparisons.

Real-Time Detection of Yeast Growth on Solid Medium through Passive Microresonator Biosensor

This study presents a biosensor fabricated based on integrated passive device (IPD) technology to measure microbial growth on solid media in real-time. Yeast (Pichia pastoris, strain GS115) is used as a model organism to demonstrate biosensor performance. The biosensor comprises an interdigital capacitor in the center with a helical inductive structure surrounding it. Additionally, 12 air bridges are added to the capacitor to increase the strength of the electric field radiated by the biosensor at the same height. Feasibility is verified by using a capacitive biosensor, and the change in capacitance values during the capacitance detection process with the growth of yeast indicates that the growth of yeast can induce changes in electrical parameters. The proposed IPD-based biosensor is used to measure yeast drop-added on a 3 mm medium for 100 h at an operating frequency of 1.84 GHz. The resonant amplitude of the biosensor varies continuously from 24 to 72 h due to the change in colony height during vertical growth of the yeast, with a maximum change of 0.21 dB. The overall measurement results also fit well with the Gompertz curve. The change in resonant amplitude between 24 and 72 h is then analyzed and reveals a linear relationship with time with a coefficient of determination of 0.9844, indicating that the biosensor is suitable for monitoring yeast growth. Thus, the proposed biosensor is proved to have potential in the field of microbial proliferation detection.

The monitoring of oil production process by deep learning based on morphology in oleaginous yeasts

BACKGROUND

Monitoring jar fermenter-cultured microorganisms in real time is important for controlling productivity of bioproducts in large-scale cultivation settings. Morphological data is used to understand the growth and fermentation states of these microorganisms during monitoring. Oleaginous yeasts are used for their high productivity of single-cell oils but the relationship between lipid productivity and morphology has not been elucidated in these organisms.

RESULTS

In this study, we investigated the relationship between the morphology of oleaginous yeasts (Lipomyces starkeyi and Rhodosporidium toruloides were used) and their cultivation state in a large-scale cultivation setting using a real-time monitoring system. We combined this with deep learning by feeding a large amount of high-definition cell images obtained from the monitoring system to a deep learning algorithm. Our results showed that the cell images could be grouped into 7 distinct groups and that a strong correlation existed between each group and its biochemical activity (growth and oil-productivity).

CONCLUSIONS

This is the first report describing the morphological variations of oleaginous yeasts in a large-scale cultivation, and describes a promising new avenue for improving productivity of microorganisms in large-scale cultivation through the use of a real-time monitoring system combined with deep learning.

KEY POINTS

• A real-time monitoring system followed the morphological change of oleaginous yeasts. • Deep learning grouped them into 7 distinct groups based on their morphology. • A correlation between the cultivation state and the shape of the yeast was observed.

A real-time monitoring system for automatic morphology analysis of yeast cultivation in a jar fermenter

The monitoring of microbial cultivation in real time and controlling their cultivation aid in increasing the production yield of useful material in a jar fermenter. Common sensors such as dissolved oxygen (DO) and pH can easily provide general-purpose indexes but do not reveal the physiological states of microbes because of the complexity of measuring them in culture conditions. It is well known from microscopic observations that the microbial morphology changes in response to the intracellular state or extracellular environment. Recently, studies have focused on rapid and quantitative image analysis techniques using machine learning or deep learning for gleaning insights into the morphological, physiological or gene expression information in microbes. During image analysis, it is necessary to retrieve high-definition images to analyze the microbial morphology in detail. In this study, we have developed a microfluidic device with a high-speed camera for the microscopic observation of yeast, and have constructed a system capable of generating their morphological information in real-time and at high definition. This system was connected to a jar fermenter, which enabled the automatic sampling for monitoring the cultivation. We successfully acquired high-definition images of over 10,000 yeast cells in about 2.2 s during ethanol fermentation automatically for over 168 h. We recorded 33,600 captures containing over 1,680,000 cell images. By analyzing these images, the morphological changes of yeast cells through ethanol fermentation could be captured, suggesting the expansion of the application of this system in controlling microbial fermentation using the morphological information generated. KEY POINTS: • Enables real-time visualization of microbes in a jar fermenter using microscopy. • Microfluidic device for acquiring high-definition images. • Generates a large amount of image data by using a high-speed camera.

Enhancing biofuels production by engineering the actin cytoskeleton in Saccharomyces cerevisiae

Saccharomyces cerevisiae is widely employed as a cell factory for the production of biofuels. However, product toxicity has hindered improvements in biofuel production. Here, we engineer the actin cytoskeleton in S. cerevisiae to increase both the cell growth and production of n-butanol and medium-chain fatty acids. Actin cable tortuosity is regulated using an n-butanol responsive promoter-based autonomous bidirectional signal conditioner in S. cerevisiae. The budding index is increased by 14.0%, resulting in the highest n-butanol titer of 1674.3 mg L-1. Moreover, actin patch density is fine-tuned using a medium-chain fatty acid responsive promoter-based autonomous bidirectional signal conditioner. The intracellular pH is stabilized at 6.4, yielding the highest medium-chain fatty acids titer of 692.3 mg L-1 in yeast extract peptone dextrose medium. Engineering the actin cytoskeleton in S. cerevisiae can efficiently alleviate biofuels toxicity and enhance biofuels production.

Potassium and Sodium Salt Stress Characterization in the Yeasts Saccharomyces cerevisiae, Kluyveromyces marxianus, and Rhodotorula toruloides

Biotechnology requires efficient microbial cell factories. The budding yeast Saccharomyces cerevisiae is a vital cell factory, but more diverse cell factories are essential for the sustainable use of natural resources. Here, we benchmarked nonconventional yeasts Kluyveromyces marxianus and Rhodotorula toruloides against S. cerevisiae strains CEN.PK and W303 for their responses to potassium and sodium salt stress. We found an inverse relationship between the maximum growth rate and the median cell volume that was responsive to salt stress. The supplementation of K⁺ to CEN.PK cultures reduced Na⁺ toxicity and increased the specific growth rate 4-fold. The higher K⁺ and Na⁺ concentrations impaired ethanol and acetate metabolism in CEN.PK and acetate metabolism in W303. In R. toruloides cultures, these salt supplementations induced a trade-off between glucose utilization and cellular aggregate formation. Their combined use increased the beta-carotene yield by 60% compared with that of the reference. Neural network-based image analysis of exponential-phase cultures showed that the vacuole-to-cell volume ratio increased with increased cell volume for W303 and K. marxianus but not for CEN.PK and R. toruloides in response to salt stress. Our results provide insights into common salt stress responses in yeasts and will help design efficient bioprocesses. IMPORTANCE Characterization of microbial cell factories under industrially relevant conditions is crucial for designing efficient bioprocesses. Salt stress, typical in industrial bioprocesses, impinges upon cell volume and affects productivity. This study presents an open-source neural network-based analysis method to evaluate volumetric changes using yeast optical microscopy images. It allows quantification of cell and vacuole volumes relevant to cellular physiology. On applying salt stress in yeasts, we found that the combined use of K⁺ and Na⁺ improves the cellular fitness of Saccharomyces cerevisiae strain CEN.PK and increases the beta-carotene productivity in Rhodotorula toruloides, a commercially important antioxidant and a valuable additive in foods.

Application of In-Situ and Soft-Sensors for Estimation of Recombinant P. pastoris GS115 Biomass Concentration: A Case Analysis of HBcAg (Mut+) and HBsAg (MutS) Production Processes under Varying Conditions

Microbial biomass concentration is a key bioprocess parameter, estimated using various labor, operator and process cross-sensitive techniques, analyzed in a broad context and therefore the subject of correct interpretation. In this paper, the authors present the results of P. pastoris cell density estimation based on off-line (optical density, wet/dry cell weight concentration), in-situ (turbidity, permittivity), and soft-sensor (off-gas O₂/CO₂, alkali consumption) techniques. Cultivations were performed in a 5 L oxygen-enriched stirred tank bioreactor. The experimental plan determined varying aeration rates/levels, glycerol or methanol substrates, residual methanol levels, and temperature. In total, results from 13 up to 150 g (dry cell weight)/L cultivation runs were analyzed. Linear and exponential correlation models were identified for the turbidity sensor signal and dry cell weight concentration (DCW). Evaluated linear correlation between permittivity and DCW in the glycerol consumption phase (<60 g/L) and medium (for Mut⁺ strain) to significant (for Mut^S strain) linearity decline for methanol consumption phase. DCW and permittivity-based biomass estimates used for soft-sensor parameters identification. Dataset consisting from 4 Mut⁺ strain cultivation experiments used for estimation quality (expressed in NRMSE) comparison for turbidity-based (8%), permittivity-based (11%), O₂ uptake-based (10%), CO₂ production-based (13%), and alkali consumption-based (8%) biomass estimates. Additionally, the authors present a novel solution (algorithm) for uncommon in-situ turbidity and permittivity sensor signal shift (caused by the intensive stirrer rate change and antifoam agent addition) on-line identification and minimization. The sensor signal filtering method leads to about 5-fold and 2-fold minimized biomass estimate drifts for turbidity- and permittivity-based biomass estimates, respectively.

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 of Novel Bioreactor Control Systems Based on Smart Sensors and Actuators

Bioreactors of various forms have been widely used in environmental protection, healthcare, industrial biotechnology, and space exploration. Robust demand in the field stimulated the development of novel designs of bioreactor geometries and process control strategies and the evolution of the physical structure of the control system. After the introduction of digital computers to bioreactor process control, a hierarchical structure control system (HSCS) for bioreactors has become the dominant physical structure, having high efficiency and robustness. However, inherent drawbacks of the HSCS for bioreactors have produced a need for a more consolidated solution of the control system. With the fast progress in sensors, machinery, and information technology, the development of a flat organizational control system (FOCS) for bioreactors based on parallel distributed smart sensors and actuators may provide a more concise solution for process control in bioreactors. Here, we review the evolution of the physical structure of bioreactor control systems and discuss the properties of the novel FOCS for bioreactors and related smart sensors and actuators and their application circumstances, with the hope of further improving the efficiency, robustness, and economics of bioprocess control.

Application of a multiphase microreactor chemostat for the determination of reaction kinetics of Staphylococcus carnosus

Bioreactors at the microliter scale offer a promising approach to accelerate bioprocess development. Advantages of such microbioreactors include a reduction in the use of expensive reagents. In this study, a chemostat operation mode of a cuvette-based microbubble column bioreactor made of polystyrene (working volume of 550 µL) was demonstrated. Aeration occurs through a nozzle (Ø ≤ 100 µm) and supports submerged whole-cell cultivation of Staphylococcus carnosus. Stationary concentrations of biomass and glucose were determined in the dilution rate regime ranging from 0.12 to 0.80 1/h with a glucose feed concentration of 1 g/L. For the first time, reaction kinetics of S. carnosus were estimated from data obtained from continuous cultivation. The maximal specific growth rate (µ_max = 0.824 1/h), Monod constant (K_S = 34 × 10^{- 3}g_S/L), substrate-related biomass yield coefficient (Y_X/S = 0.315 g_CDW/g_S), and maintenance coefficient (m_S = 0.0035 g_S/(g_CDW·h)) were determined. These parameters are now available for further studies in the field of synthetic biology.