Edible mycelium as proliferation and differentiation support for anchorage-dependent animal cells in cultivated meat production

Cultivated meat production requires bioprocess optimization to achieve cell densities that are multiple orders of magnitude higher compared to conventional cell culture techniques. These processes must maximize resource efficiency and cost-effectiveness by attaining high cell growth productivity per unit of medium. Microcarriers, or carriers, are compatible with large-scale bioreactor use, and offer a large surface-area-to-volume ratio for the adhesion and proliferation of anchorage-dependent animal cells. An ongoing challenge persists in the efficient retrieval of cells from the carriers, with conflicting reports on the effectiveness of trypsinization and the need for additional optimization measures such as carrier sieving. To surmount this issue, edible carriers have been proposed, offering the advantage of integration into the final food product while providing opportunities for texture, flavor, and nutritional incorporation. Recently, a proof of concept (POC) utilizing inactivated mycelium biomass derived from edible filamentous fungus demonstrated its potential as a support structure for myoblasts. However, this POC relied on a model mammalian cell line combination with a single mycelium species, limiting realistic applicability to cultivated meat production. This study aims to advance the POC. We found that the species of fungi composing the carriers impacts C2C12 myoblast cell attachment-with carriers derived from Aspergillus oryzae promoting the best proliferation. C2C12 myoblasts effectively differentiated on mycelium carriers when induced in myogenic differentiation media. Mycelium carriers also supported proliferation and differentiation of bovine satellite cells. These findings demonstrate the potential of edible mycelium carrier technology to be readily adapted in product development within the cultivated meat industry.

Multi-objective Bayesian algorithm automatically discovers low-cost high-growth serum-free media for cellular agriculture application

In this work, we applied a multi-information source modeling technique to solve a multi-objective Bayesian optimization problem involving the simultaneous minimization of cost and maximization of growth for serum-free C2C12 cells using a hyper-volume improvement acquisition function. In sequential batches of custom media experiments designed using our Bayesian criteria, collected using multiple assays targeting different cellular growth dynamics, the algorithm learned to identify the trade-off relationship between long-term growth and cost. We were able to identify several media with > 100 % more growth of C2C12 cells than the control, as well as a medium with 23% more growth at only 62.5% of the cost of the control. These algorithmically generated media also maintained growth far past the study period, indicating the modeling approach approximates the cell growth well from an extremely limited data set.

Multi-Information Source Bayesian Optimization of Culture Media for Cellular Agriculture

Culture media used in industrial bioprocessing and the emerging field of cellular agriculture is difficult to optimize due to the lack of rigorous mathematical models of cell growth and culture conditions, as well as the complexity of the design space. Rapid growth assays are inaccurate yet convenient, while robust measures of cell number can be time‐consuming to the point of limiting experimentation. In this study, we optimized a cell culture media with 14 components using a multi‐information source Bayesian optimization algorithm that locates optimal media conditions based on an iterative refinement of an uncertainty‐weighted desirability function. As a model system, we utilized murine C2C12 cells, using AlamarBlue, LIVE stain, and trypan blue exclusion cell counting assays to determine cell number. Using this experimental optimization algorithm, we were able to design media with 181% more cells than a common commercial variant with a similar economic cost, while doing so in 38% fewer experiments than an efficient design‐of‐experiments method. The optimal medium generalized well to long‐term growth up to four passages of C2C12 cells, indicating the multi‐information source assay improved measurement robustness relative to rapid growth assays alone.

Metabolic flux sampling predicts strain-dependent differences related to aroma production among commercial wine yeasts

BACKGROUND

Metabolomics coupled with genome-scale metabolic modeling approaches have been employed recently to quantitatively analyze the physiological states of various organisms, including Saccharomyces cerevisiae. Although yeast physiology in laboratory strains is well-studied, the metabolic states under industrially relevant scenarios such as winemaking are still not sufficiently understood, especially as there is considerable variation in metabolism between commercial strains. To study the potential causes of strain-dependent variation in the production of volatile compounds during enological conditions, random flux sampling and statistical methods were used, along with experimental extracellular metabolite flux data to characterize the differences in predicted intracellular metabolic states between strains.

RESULTS

It was observed that four selected commercial wine yeast strains (Elixir, Opale, R2, and Uvaferm) produced variable amounts of key volatile organic compounds (VOCs). Principal component analysis was performed on extracellular metabolite data from the strains at three time points of cell cultivation (24, 58, and 144 h). Separation of the strains was observed at all three time points. Furthermore, Uvaferm at 24 h, for instance, was most associated with propanol and ethyl hexanoate. R2 was found to be associated with ethyl acetate and Opale could be associated with isobutanol while Elixir was most associated with phenylethanol and phenylethyl acetate. Constraint-based modeling (CBM) was employed using the latest genome-scale metabolic model of yeast (Yeast8) and random flux sampling was performed with experimentally derived fluxes at various stages of growth as constraints for the model. The flux sampling simulations allowed us to characterize intracellular metabolic flux states and illustrate the key parts of metabolism that likely determine the observed strain differences. Flux sampling determined that Uvaferm and Elixir are similar while R2 and Opale exhibited the highest degree of differences in the Ehrlich pathway and carbon metabolism, thereby causing strain-specific variation in VOC production. The model predictions also established the top 20 fluxes that relate to phenotypic strain variation (e.g. at 24 h). These fluxes indicated that Opale had a higher median flux for pyruvate decarboxylase reactions compared with the other strains. Conversely, R2 which was lower in all VOCs, had higher median fluxes going toward central metabolism. For Elixir and Uvaferm, the differences in metabolism were most evident in fluxes pertaining to transaminase and hexokinase associated reactions. The applied analysis of metabolic divergence unveiled strain-specific differences in yeast metabolism linked to fusel alcohol and ester production.

CONCLUSIONS

Overall, this approach proved useful in elucidating key reactions in amino acid, carbon, and glycerophospholipid metabolism which suggest genetic divergence in activity in metabolic subsystems among these wine strains related to the observed differences in VOC formation. The findings in this study could steer more focused research endeavors in developing or selecting optimal aroma-producing yeast stains for winemaking and other types of alcoholic fermentations.

Nitrogenous Compound Utilization and Production of Volatile Organic Compounds among Commercial Wine Yeasts Highlight Strain-Specific Metabolic Diversity

Genetic background and environmental conditions affect the production of sensory impact compounds by Saccharomyces cerevisiae. The relative importance of the strain-specific metabolic capabilities for the production of volatile organic compounds (VOCs) remains unclear. We investigated which amino acids contribute to VOC production and whether amino acid-VOC relations are conserved among yeast strains. Amino acid consumption and production of VOCs during grape juice fermentation was investigated using four commercial wine yeast strains: Elixir, Opale, R2, and Uvaferm. Principal component analysis of the VOC data demonstrated that Uvaferm correlated with ethyl acetate and ethyl hexanoate production, R2 negatively correlated with the acetate esters, and Opale positively correlated with fusel alcohols. Biomass formation was similar for all strains, pointing to metabolic differences in the utilization of nutrients to form VOCs. Partial least-squares linear regression showed that total aroma production is a function of nitrogen utilization (R2 = 0.87). We found that glycine, tyrosine, leucine, and lysine utilization were positively correlated with fusel alcohols and acetate esters. Mechanistic modeling of the yeast metabolic network via parsimonious flux balance analysis and flux enrichment analysis revealed enzymes with crucial roles, such as transaminases and decarboxylases. Our work provides insights in VOC production in wine yeasts. IMPORTANCE Saccharomyces cerevisiae is widely used in grape juice fermentation to produce wines. Along with the genetic background, the nitrogen in the environment in which S. cerevisiae grows impacts its regulation of metabolism. Also, commercial S. cerevisiae strains exhibit immense diversity in their formation of aromas, and a desirable aroma bouquet is an essential characteristic for wines. Since nitrogen affects aroma formation in wines, it is essential to know the extent of this connection and how it leads to strain-dependent aroma profiles in wines. We evaluated the differences in the production of key aroma compounds among four commercial wine strains. Moreover, we analyzed the role of nitrogen utilization on the formation of various aroma compounds. This work illustrates the unique aroma-producing differences among industrial yeast strains and suggests more intricate, nitrogen-associated routes influencing those aroma-producing differences.

Optimization of muscle cell culture media using nonlinear design of experiments

Optimizing media for biological processes, such as those used in tissue engineering and cultivated meat production, is difficult due to the extensive experimentation required, number of media components, nonlinear and interactive responses, and the number of conflicting design objectives. Here we demonstrate the capacity of a nonlinear design-of-experiments (DOE) method to predict optimal media conditions in fewer experiments than a traditional DOE. The approach is based on a hybridization of a coordinate search for local optimization with dynamically adjusted search spaces and a global search method utilizing a truncated genetic algorithm using radial basis functions to store and model prior knowledge. Using this method, we were able to reduce the cost of muscle cell proliferation media while maintaining cell growth 48 hours after seeding using 30 common components of typical commercial growth medium in fewer experiments than a traditional DOE (70 vs 103). While we clearly demonstrated that the experimental optimization algorithm significantly outperforms conventional DOE, due to the choice of a 48 hour growth assay weighted by medium cost as an objective function, these findings were limited to performance at a single passage, and did not generalize to growth over multiple passages. This underscores the importance of choosing objective functions that align well with process goals. This article is protected by copyright. All rights reserved.

Considerations for the development of cost-effective cell culture media for cultivated meat production

Innovation in cultivated meat development has been rapidly accelerating in recent years because it holds the potential to help attenuate issues facing production of dietary protein for a growing world population. There are technical obstacles still hindering large-scale commercialization of cultivated meat, of which many are related to the media that are used to culture the muscle, fat, and connective tissue cells. While animal cell culture media has been used and refined for roughly a century, it has not been specifically designed with the requirements of cultivated meat in mind. Perhaps the most common industrial use of animal cell culture is currently the production of therapeutic monoclonal antibodies, which sell for orders of magnitude more than meat. Successful production of cultivated meat requires media that is food grade with minimal cost, can regulate large-scale cell proliferation and differentiation, has acceptable sensory qualities, and is animal ingredient-free. Much insight into strategies for achieving media formulations with these qualities can be obtained from knowledge of conventional culture media applications and from the metabolic pathways involved in myogenesis and protein synthesis. In addition, application of principles used to optimize media for large-scale microbial fermentation processes producing lower value commodity chemicals and food ingredients can also be instructive. As such, the present review shall provide an overview of the current understanding of cell culture media as it relates to cultivated meat.

Downshifting Yeast Dominance: Cell Physiology and Phospholipid Composition Are Altered With Establishment of the [GAR +] Prion in Saccharomyces cerevisiae

Establishment of the [GAR ⁺] prion in Saccharomyces cerevisiae reduces both transcriptional expression of the HXT3 hexose transporter gene and fermentation capacity in high sugar conditions. We evaluated the impact of deletion of the HXT3 gene on the expression of [GAR ⁺] prion phenotype in a vineyard isolate, UCD932, and found that changes in fermentation capacity were observable even with complete loss of the Hxt3 transporter, suggesting other cellular functions affecting fermentation rate may be impacted in [GAR ⁺] strains. In a comparison of isogenic [GAR ⁺] and [gar ^-] strains, localization of the Pma1 plasma membrane ATPase showed differences in distribution within the membrane. In addition, plasma membrane lipid composition varied between the two cell types. Oxygen uptake was decreased in prion induced cells suggesting membrane changes affect plasma membrane functionality beyond glucose transport. Thus, multiple cell surface properties are altered upon induction of the [GAR ⁺] prion in addition to changes in expression of the HXT3 gene. We propose a model wherein [GAR ⁺] prion establishment within a yeast population is associated with modulation of plasma membrane functionality, fermentation capacity, niche dominance, and cell physiology to facilitate growth and mitigate cytotoxicity under certain environmental conditions. Down-regulation of expression of the HXT3 hexose transporter gene is only one component of a suite of physiological differences. Our data show the [GAR ⁺] prion state is accompanied by multiple changes in the yeast cell surface that prioritize population survivability over maximizing metabolic capacity and enable progeny to establish an alternative adaptive state while maintaining reversibility.

A combined phenolic extraction and fermentation reactor engineering model for multiphase red wine fermentation

Red wine production begins with a simultaneous fermentation and solid-phase extraction process. Red wine color and mouthfeel is the result of the extraction of phenolics from grape skins and seeds during fermentation, where extraction is a strong function of temperature and ethanol concentration. During fermentation, grape solids form a porous "cap" at the top of the fermentor, resulting in a heterogeneous fermentation system with significant temperature and concentration gradients. In this work, we present a spatial, time-variant reactor engineering model for phenolic extraction during red wine fermentation, incorporating fermentation kinetics, mass transfer, heat transfer, compressible fluid flow, and phenolic extraction kinetics. The temperature and ethanol concentration profiles predicted by this model allow for the calculation of phenolic extraction rates over the course of fermentation. Phenolic extraction predictions were validated against prior experimental data to good agreement and compared to a well-mixed model's predictions to show the utility of a spatial model over well-mixed models.

A Mechanistic Model for the Extraction of Phenolics from Grapes During Red Wine Fermentation

Phenolic extraction is a critical part of red wine making. Though empirical models of phenolic extraction kinetics exist, the current level of mechanistic understanding does not allow for accurate predictions. In this work, we propose a mechanistic model for the extraction of phenolics from grape skins and seeds as a function of temperature and ethanol. This model examines the release of phenolics, the adsorption of phenolics onto grape material, and the disappearance of anthocyanins from solution. Additionally, we performed epifluorescence microscopy to explore our finding that seed tannins' release rate appears independent of concentration, and found that the grape seed appears to ablate over fermentation. We also determined the activation energy of anthocyanin disappearance, in good agreement with similar systems. The proposed model results in an excellent fit, and increases the understanding of phenolic extraction and the ability to predict and optimize product outcome in red wine making.

Conversion of cassava leaf to bioavailable, high-protein yeast cell biomass

BACKGROUND

Cassava leaves are an abundant global agricultural residue because the roots are a major source of dietary carbohydrates. Although cassava leaves are high in protein, the protein is not bioavailable. This work aimed to convert cassava leaves to a bioavailable protein-rich animal feed ingredient using high-protein yeasts.

RESULTS

The structural proteins (ca 200 g kg^-1 d.b.) from sundried cassava leaves were solubilized by mild alkali pretreatment, and the resulting cassava leaf hydrolysate (CLH) was used to screen for growth of 46 high-protein yeasts from 30 species. Promising candidates from the initial screen cultivated at a 10 mL scale demonstrated increases in relative abundance of essential amino acids over that of CLH. In particular, lysine, growth-limiting for some livestock, was increased up to 226% over the CLH content. One yeast, Pichia kudriavzevii UCDFST 11-602, was grown in 3 L of CLH in a bioreactor to examine the scale-up potential of the yeast protein production. While glucose was completely consumed, yeast growth exited log phase before depleting either carbon or nitrogen, suggesting other growth-limiting factors at the larger scale.

CONCLUSIONS

High-value animal feed with enriched essential amino acid profiles can be produced by yeasts grown on agricultural residues. Yeasts convert structural protein solubilized from cassava leaves to essential amino acid-enriched, digestible protein. The low carbohydrate content of the leaves (ca 200 g kg^-1 d.b.), however, necessitated glucose supplementation for yeast growth. © 2018 Society of Chemical Industry.

Impact of cold soak duration on Cabernet Sauvignon fermentation and phenolic composition

BACKGROUND

Cold soak is a prefermentative maceration technique believed to enhance grape skin extraction. Studies show variable results depending on cold soak and winemaking conditions. To investigate the effect of cold soak more fully, systematic and highly reproducible Cabernet Sauvignon fermentations with increasing cold-soak durations were performed.

RESULTS

Phenolic extraction during cold soak and fermentation showed significant differences among all treatments for monitored phenolics at the end of the cold soak. At the end of alcoholic fermentation only gallic acid, (-)-epicatechin, and the flavonols were significant, and only (-)-epicatechin was significant after bottle ageing. Descriptive analysis of the bottled wines showed that the 4- and 7-day treatments were significantly higher in caramelized/vanilla/browned flavor compared to the 1-day treatment and lower levels of bitterness were observed up to 2 days of cold soak. While oligosaccharide content increased with increasing cold-soak duration, differences were not large enough to result in sensory differences.

CONCLUSION

While increased cold soak duration led to differences in phenolic extraction during early fermentation, these differences did not last through to the end product. Thus, under the conditions of this study, cold-soak duration had little overall impact on Cabernet Sauvignon wine composition and style. © 2018 Society of Chemical Industry.

Creation and validation of a reactor engineering model for multiphase red wine fermentations

Red wine fermentations are performed in the presence of grape skins and seeds to ensure the extraction of color and other phenolics. The presence of these solids results in two distinct phases in the fermentor, as the solids float to the top to form a "cap." Modeling of red wine fermentation is, therefore, complex and must consider spatial heterogeneity to predict fermentation kinetics. We have developed a reactor-engineering model for red wine fermentations that includes the fundamentals of fermentation kinetics, heat transfer, diffusion, and compressible fluid flow. To develop the heat transfer component of the model, the heat transfer properties of grapes were experimentally determined as a function of fermentation progression. COMSOL was used to solve all components of the model simultaneously utilizing a finite element analysis approach. Predictions from this model were validated using prior experimental work. Model prediction and experimental data showed excellent agreement. The model was then used to predict spatial profiles of active yeast cell concentration and ethanol productivity, as well as liquid velocity profiles. Finally, the model was used to predict how these gradients would change with differences in initial bioavailable nitrogen concentration, a key parameter in predicting fermentation outcome in nitrogen-limited wine fermentations.

Characterizing the release of bioactive N-glycans from dairy products by a novel endo-β-N-acetylglucosaminidase

Endo-β-N-acetylglucosaminidase isolated from B. infantis ATCC 15697 (EndoBI-1) is a novel enzyme that cleaves N-N'-diacetyl chitobiose moieties found in the N-glycan core of high mannose, hybrid, and complex N-glycans. These conjugated N-glycans are recently shown as a new prebiotic source that stimulates the growth of a key infant gut microbe, Bifidobacterium longum subsp. Infantis. The effects of pH (4.45-8.45), temperature (27.5-77.5°C), reaction time (15-475 min), and enzyme/protein ratio (1:3,000-1:333) were evaluated on the release of N-glycans from bovine colostrum whey by EndoBI-1. A central composite design was used, including a two-level factorial design (2(4)) with four center points and eight axial points. In general, low pH values, longer reaction times, higher enzyme/protein ratio, and temperatures around 52°C resulted in the highest yield. The results demonstrated that bovine colostrum whey, considered to be a by/waste product, can be used as a glycan source with a yield of 20 mg N-glycan/g total protein under optimal conditions for the ranges investigated. Importantly, these processing conditions are suitable to be incorporated into routine dairy processing activities, opening the door for an entirely new class of products (released bioactive glycans and glycan-free milk). The new enzyme's activity was also compared with a commercially available enzyme, showing that EndoBI-1 is more active on native proteins than PNGase F and can be efficiently used during pasteurization, streamlining its integration into existing processing strategies.

A novel endo-β-N-acetylglucosaminidase releases specific N-glycans depending on different reaction conditions

Milk glycoproteins are involved in different functions and contribute to different cellular processes, including adhesion and signaling, and shape the development of the infant microbiome. Methods have been developed to study the complexities of milk protein glycosylation and understand the role of N-glycans in protein functionality. Endo-β-N-acetylglucosaminidase (EndoBI-1) isolated from Bifidobacterium longum subsp. infantis ATCC 15697 is a recently isolated heat-stable enzyme that cleaves the N-N'-diacetyl chitobiose moiety found in the N-glycan core. The effects of different processing conditions (pH, temperature, reaction time, and enzyme/protein ratio) were evaluated for their ability to change EndoBI-1 activity on bovine colostrum whey glycoproteins using advanced mass spectrometry. This study shows that EndoBI-1 is able to cleave a high diversity of N-glycan structures. Nano-LC-Chip-Q-TOF MS data also revealed that different reaction conditions resulted in different N-glycan compositions released, thus modifying the relative abundance of N-glycan types. In general, more sialylated N-glycans were released at lower temperatures and pH values. These results demonstrated that EndoBI-1 is able to release a wide variety of N-glycans, whose compositions can be selectively manipulated using different processing conditions.

Kinetic characterization of a novel endo-β-N-acetylglucosaminidase on concentrated bovine colostrum whey to release bioactive glycans

EndoBI-1 is a recently isolated endo-β-N-acetylglucosaminidase, which cleaves the N-N'-diacetyl chitobiose moiety found in the N-glycan core of high mannose, hybrid and complex N-glycans. These N-glycans have selective prebiotic activity for a key infant gut microbe, Bifidobacterium longum subsp. infantis. The broad specificity of EndoBI-1 suggests the enzyme may be useful for many applications, particularly for deglycosylating milk glycoproteins in dairy processing. To facilitate its commercial use, we determined kinetic parameters for EndoBI-1 on the model substrates ribonuclease B and bovine lactoferrin, as well as on concentrated bovine colostrum whey. Km values ranging from 0.25 to 0.49, 0.43 to 1.00 and 0.90 to 3.18 mg/mL and Vmax values ranging from 3.5×10(-3) to 5.09×10(-3), 4.5×10(-3) to 7.75×10(-3) and 1.9×10(-2)to 5.2×10(-2) mg/mL×min were determined for ribonuclease B, lactoferrin and whey, respectively. In general, EndoBI-1 showed the highest apparent affinity for ribonuclease B, while the maximum reaction rate was the highest for concentrated whey. EndoBI-1-released N-glycans were quantified by a phenol-sulphuric total carbohydrate assay and the resultant N-glycan structures monitored by nano-LC-Chip-Q-TOF MS. The kinetic parameters and structural characterization of glycans released suggest EndoBI-1 can facilitate large-scale release of complex, bioactive glycans from a variety of glycoprotein substrates. Moreover, these results suggest that whey, often considered as a waste product, can be used effectively as a source of prebiotic N-glycans.

Investigating the Effect of Cold Soak Duration on Phenolic Extraction during Cabernet Sauvignon Fermentation

The impact of increasing cold soak (CS) duration (0, 1, 4, 7, and 10 days at 10 °C) on the extraction of phenolic compounds during the CS period and primary fermentation as well as the final composition of Cabernet Sauvignon wine was investigated. The results showed that CS duration had no effect on hydroxycinnamate and flavonol extractions. Greater amounts of gallic acid, (+)-catechin, (−)-epicatechin, and total tannins were extracted with increasing CS duration, with differences maintained during bottle aging. Anthocyanin extraction and color density increased with longer periods of CS; however, by the end of primary fermentation, as well as three months’ bottle aging, there were no significant differences due to CS duration. The wines made with seven and 10 days of CS had higher seed tannin contributions and total tannin compared to the non-CS wine, which could potentially result in increased astringency.

Utilization of galactooligosaccharides by Bifidobacterium longum subsp. infantis isolates

Prebiotics are non-digestible substrates that stimulate the growth of beneficial microbial populations in the intestine, especially Bifidobacterium species. Among them, fructo- and galacto-oligosaccharides are commonly used in the food industry, especially as a supplement for infant formulas. Mechanistic details on the enrichment of bifidobacteria by these prebiotics are important to understand the effects of these dietary interventions. In this study the consumption of galactooligosaccharides was studied for 22 isolates of Bifidobacterium longum subsp. infantis, one of the most representative species in the infant gut microbiota. In general all isolates showed a vigorous growth on these oligosaccharides, but consumption of larger galactooligosaccharides was variable. Bifidobacterium infantis ATCC 15697 has five genes encoding β-galactosidases, and three of them were induced during bacterial growth on commercial galactooligosaccharides. Recombinant β-galactosidases from B. infantis ATCC 15697 displayed different preferences for β-galactosides such as 4' and 6'-galactobiose, and four β-galactosidases in this strain released monosaccharides from galactooligosaccharides. Finally, we determined the amounts of short chain fatty acids produced by strain ATCC 15697 after growth on different prebiotics. We observed that biomass and product yields of substrate were higher for lactose and galactooligosaccharides, but the amount of acids produced per cell was larger after growth on human milk oligosaccharides. These results provide a molecular basis for galactooligosaccharide consumption in B. infantis, and also represent evidence for physiological differences in the metabolism of prebiotics that might have a differential impact on the host.

Analysis of major phospholipid species and ergosterol in fermenting industrial yeast strains using atmospheric pressure ionization ion-trap mass spectrometry

Knowledge of the individual lipid species that are associated with ethanol tolerance in Saccharomyces cerevisiae is necessary to understand potential mechanisms of how this organism uses these molecules to mitigate the toxic effects of ethanol. Three industrial yeast strains with varying degrees of ethanol tolerance were examined utilizing normal phase high-performance liquid chromatography and atmospheric pressure ionization-ion-trap mass spectrometry methods to quantitatively determine phospholipid and ergosterol levels at numerous fermentation time points. Both high and low Brix fermentations were performed to assess the sugar utilization capabilities of the strains. The results indicated that the strain with the most robust fermentation characteristics had the highest phosphatidylinositol levels and lowest phosphatidylcholine levels. Examination of the phospholipid structural data from tandem MS experiments indicated that the levels of several phospholipid species were unique to the slowest fermenting strain. The relation of ergosterol and other phospholipids to ethanol tolerance is also discussed.

Atypical ethanol production by carbon catabolite derepressed lactobacilli

Cost effective use of lignocellulosic biomass for bio-based chemical production requires the discovery of novel strains and processes. Lactobacillus pentosus JH5XP5 is a carbon catabolite repression negative mutant which utilizes glucose and pentoses derived from lignocellulosic biomass in the media simultaneously. With a broad range of carbon substrates, L. pentosus JH5XP5 produced a significant amount of ethanol without acetate formation. The yields of ethanol were 2.0- to 2.5-fold higher than those of lactate when glucose, galactose or maltose was used either as a single carbon source or simultaneously with glucose. L. pentosus JH5XP5 was successfully used in an integrated process of simultaneous saccharification and mixed sugar fermentation of rice straw hydrolysate. During the fermentation, the enzyme activities for the saccharification of cellulose were not diminished. Moreover glucose, xylose, and arabinose sugars derived from rice straw hyrolysate were consumed concurrently as if a single carbon source existed and no sugars or cellulosic fiber remained after the fermentation.

Simultaneous consumption of pentose and hexose sugars: an optimal microbial phenotype for efficient fermentation of lignocellulosic biomass

Lignocellulosic biomass is an attractive carbon source for bio-based fuel and chemical production; however, its compositional heterogeneity hinders its commercial use. Since most microbes possess carbon catabolite repression (CCR), mixed sugars derived from the lignocellulose are consumed sequentially, reducing the efficacy of the overall process. To overcome this barrier, microbes that exhibit the simultaneous consumption of mixed sugars have been isolated and/or developed and evaluated for the lignocellulosic biomass utilization. Specific strains of Escherichia coli, Saccharomyces cerevisiae, and Zymomonas mobilis have been engineered for simultaneous glucose and xylose utilization via mutagenesis or introduction of a xylose metabolic pathway. Other microbes, such as Lactobacillus brevis, Lactobacillus buchneri, and Candida shehatae possess a relaxed CCR mechanism, showing simultaneous consumption of glucose and xylose. By exploiting CCR-negative phenotypes, various integrated processes have been developed that incorporate both enzyme hydrolysis of lignocellulosic material and mixed sugar fermentation, thereby enabling greater productivity and fermentation efficacy.

Consumption of human milk oligosaccharides by gut-related microbes

Human milk contains large amounts of complex oligosaccharides that putatively modulate the intestinal microbiota of breast-fed infants by acting as decoy binding sites for pathogens and as prebiotics for enrichment of beneficial bacteria. Several bifidobacterial species have been shown to grow well on human milk oligosaccharides. However, few data exist on other bacterial species. This work examined 16 bacterial strains belonging to 10 different genera for growth on human milk oligosaccharides. For this propose, a chemically defined medium, ZMB1, was used, which allows vigorous growth of a number of gut-related microorganisms in a fashion similar to complex media. Interestingly, Bifidobacterium longum subsp. infantis, Bacteroides fragilis , and Bacteroides vulgatus strains were able to metabolize milk oligosaccharides with high efficiency, whereas Enterococcus , Streptococcus , Veillonella , Eubacterium , Clostridium , and Escherichia coli strains grew less well or not at all. Mass spectrometry-based glycoprofiling of the oligosaccharide consumption behavior revealed a specific preference for fucosylated oligosaccharides by Bi. longum subsp. infantis and Ba. vulgatus. This work expands the current knowledge of human milk oligosaccharide consumption by gut microbes, revealing bacteroides as avid consumers of this substrate. These results provide insight on how human milk oligosaccharides shape the infant intestinal microbiota.

Conversion of rice straw to bio-based chemicals: an integrated process using Lactobacillus brevis

Commercialization of lignocellulosic biomass as a feedstock for bio-based chemical production is problematic due to the high processing costs of pretreatment and saccharifying enzymes combined with low product yields. Such low product yield can be attributed, in large part, to the incomplete utilization of the various carbohydrate sugars found in the lignocellulosic biomass. In this study, we demonstrate that Lactobacillus brevis is able to simultaneously metabolize all fermentable carbohydrates in acid pre-processed rice straw hydrolysate, thereby allowing complete utilization of all released sugars. Inhibitors present in rice straw hydrolysate did not affect lactic acid production. Moreover, the activity of exogenously added cellulases was not reduced in the presence of growing cultures of L. brevis. These factors enabled the use of L. brevis in a process termed simultaneous saccharification and mixed sugar fermentation (SSMSF). In SSMSF with L. brevis, sugars present in rice straw hydrolysate were completely utilized while the cellulase maintained its maximum activity due to the lack of feedback inhibition from glucose and/or cellobiose. By comparison to a sequential hydrolysis and fermentation process, SSMSF reduced operation time and the amount of cellulase enzyme necessary to produce the same amount of lactic acid.

Using highly efficient nonlinear experimental design methods for optimization of Lactococcus lactis fermentation in chemically defined media

Optimization of fermentation media and processes is a difficult task due to the potential for high dimensionality and nonlinearity. Here we develop and evaluate variations on two novel and highly efficient methods for experimental fermentation optimization. The first approach is based on using a truncated genetic algorithm with a developing neural network model to choose the best experiments to run. The second approach uses information theory, along with Bayesian regularized neural network models, for experiment selection. To evaluate these methods experimentally, we used them to develop a new chemically defined medium for Lactococcus lactis IL1403, along with an optimal temperature and initial pH, to achieve maximum cell growth. The media consisted of 19 defined components or groups of components. The optimization results show that the maximum cell growth from the optimal process of each novel method is generally comparable to or higher than that achieved using a traditional statistical experimental design method, but these optima are reached in about half of the experiments (73-94 vs. 161, depending on the variants of methods). The optimal chemically defined media developed in this work are rich media that can support high cell density growth 3.5-4 times higher than the best reported synthetic medium and 72% higher than a commonly used complex medium (M17) at optimization scale. The best chemically defined medium found using the method was evaluated and compared with other defined or complex media at flask- and fermentor-scales.

A dynamic, genome-scale flux model of Lactococcus lactis to increase specific recombinant protein expression

Recently, lactic acid bacteria (LAB) have attracted a great deal of interest because of their potential to serve as oral delivery vehicles for recombinant protein vaccines. An important limitation to their use is the typically low level of heterologous expression obtained in LAB. To address this, a dynamic flux balance analysis (DFBA) model was used to identify gene targets for increasing specific expression of Green Fluorescent Protein (GFP), a model heterologous protein, in Lactococcus lactis IL1403. Two strains, each targeting one of the top model-identified genes, were constructed and tested in vivo. Data show that both strains, by a conservative estimate, achieved 15% higher GFP per cell than the control strain, a qualitative confirmation of the model predictions. A genome-scale DFBA model for L. lactis growing on M17 medium is presented along with the procedure for screening gene targets and a powerful method for visualizing fluxes in genome-scale metabolic networks.

Temperature-dependent kinetic model for nitrogen-limited wine fermentations

A physical and mathematical model for wine fermentation kinetics was adapted to include the influence of temperature, perhaps the most critical factor influencing fermentation kinetics. The model was based on flask-scale white wine fermentations at different temperatures (11 to 35 degrees C) and different initial concentrations of sugar (265 to 300 g/liter) and nitrogen (70 to 350 mg N/liter). The results show that fermentation temperature and inadequate levels of nitrogen will cause stuck or sluggish fermentations. Model parameters representing cell growth rate, sugar utilization rate, and the inactivation rate of cells in the presence of ethanol are highly temperature dependent. All other variables (yield coefficient of cell mass to utilized nitrogen, yield coefficient of ethanol to utilized sugar, Monod constant for nitrogen-limited growth, and Michaelis-Menten-type constant for sugar transport) were determined to vary insignificantly with temperature. The resulting mathematical model accurately predicts the observed wine fermentation kinetics with respect to different temperatures and different initial conditions, including data from fermentations not used for model development. This is the first wine fermentation model that accurately predicts a transition from sluggish to normal to stuck fermentations as temperature increases from 11 to 35 degrees C. Furthermore, this comprehensive model provides insight into combined effects of time, temperature, and ethanol concentration on yeast (Saccharomyces cerevisiae) activity and physiology.

Kinetics of Lactococcus lactis growth and metabolite formation under aerobic and anaerobic conditions in the presence or absence of hemin

The study of batch kinetics of Lactococcus lactis cell growth and product formation reveals three distinct metabolic behaviors depending upon the availability of oxygen to the culture and the presence of hemin in the medium. These three cultivation modes, anerobic homolactic fermentation, aerobic heterolactic fermentation, and hemin-stimulated respiration have been studied at pH 6.0 and 30 degrees C with a medium containing a high concentration of glucose (60 g/L). A maximum cell density of 5.78 g/L was obtained in the batch culture under hemin-stimulated respiration conditions, about three times as much as that achieved with anerobic homolactic fermentation (1.87 g/L) and aerobic heterolactic fermentation (1.80 g/L). The maximum specific growth rate was 0.60/h in hemin-stimulated respiration, slightly higher than that achieved in homolactic fermentation (0.56/h) and substantially higher than that in heterolactic fermentation (0.40/h). Alteration of metabolism caused by the supplementation of oxygen and hemin is evidenced by changes in both cell growth kinetics and metabolite formation kinetics, which are characterized by a unique pseudo-diauxic growth of L. lactis. We hypothesise that Lactococcus lactis generates bioenergy (ATP) through simultaneous lactate formation and hemin-stimulated respiration in the primary exponential phase, when glucose is abundant, and utilizes lactate for cell growth and cell maintenance in the stationary phase, after glucose is exhausted. We also examined the applicability of a modified logistic model and the Luedeking-Piret model for cell growth kinetics and metabolite formation kinetics, respectively.

Optimization of fed-batch production of the model recombinant protein GFP inLactococcus lactis

Optimization of recombinant protein production using lactic acid bacteria (LAB) remains an important obstacle on the road to realizing LAB as oral vaccine delivery vehicles. Despite this, there have been few published investigations to explore the higher limits of LAB recombinant protein expression in fed-batch fermentations. In this study, results from response surface experiments suggested an optimal set of conditions for expression of green fluorescent protein (GFP), a model recombinant protein, in bench-scale, fed-batch Lactococcus lactis IL1403 fermentations. The 48 4-L fed-batch fermentations in this set of experiments, along with preliminary studies, investigated the effects of pH, temperature, hemin concentration, concentration of the nisin inducer per cell, and time of induction. Cell densities in this data set ranged from 2.9 to 7.4 g/L and maximum GFP expression per cell ranged from 0.1 to 4.4 relative fluorescence units (RFU)/g. The optimal 4-L, fed-batch fermentation process found here yields growth and protein expression values that dramatically improve upon results from traditional test tube and flask processes. Relative to the traditional process, the experimental optimum conditions yield 4.9 times the cell density, 1.6 times the protein per cell mass, and 8 times the total protein concentration. Unexpectedly, experiments also revealed that the compound hemin, known previously to improve growth and survival of Lactococcus lactis (L. lactis), negatively impacted recombinant protein production when added in concentrations from 5 to 20 microg/mL with this strain. The improvement in protein expression over traditional processes demonstrated here is an important step toward commercial development of LAB for oral delivery of recombinant vaccines and therapeutic proteins.

Retrospective optimization of time-dependent fermentation control strategies using time-independent historical data

We have previously shown the usefulness of historical data for fermentation process optimization. The methodology developed includes identification of important process inputs, training of an artificial neural network (ANN) process model, and ultimately use of the ANN model with a genetic algorithm to find the optimal values of each critical process input. However, this approach ignores the time-dependent nature of the system, and therefore, does not fully utilize the available information within a database. In this work, we propose a method for incorporating time-dependent optimization into our previously developed three-step optimization routine. This is achieved by an additional step that uses a fermentation model (consisting of coupled ordinary differential equations (ODE)) to interpret important time-course features of the collected data through adjustments in model parameters. Important process variables not explicitly included in the model were then identified for each model parameter using automatic relevance determination (ARD) with Gaussian process (GP) models. The developed GP models were then combined with the fermentation model to form a hybrid neural network model that predicted the time-course activity of the cell and protein concentrations of novel fermentation conditions. A hybrid-genetic algorithm was then used in conjunction with the hybrid model to suggest optimal time-dependent control strategies. The presented method was implemented upon an E. coli fermentation database generated in our laboratory. Optimization of two different criteria (final protein yield and a simplified economic criteria) was attempted. While the overall protein yield was not increased using this methodology, we were successful in increasing a simplified economic criterion by 15% compared to what had been previously observed. These process conditions included using 35% less arabinose (the inducer) and 33% less typtone in the media and reducing the time required to reach the maximum protein concentration by 10% while producing approximately the same level of protein as the previous optimum.

Elasticity and phase behavior of DPPC membrane modulated by cholesterol, ergosterol, and ethanol

Giant vesicles formed of 1,2-dipalmitoylphosphatidylcholine (DPPC) and sterols (cholesterol or ergosterol) in water and water/ethanol solutions have been used to examine the effect of sterol composition and ethanol concentration on the area compressibility modulus (K(a)), overall mechanical behavior, vesicle morphology, and induction of lipid alkyl chain interdigitation. Our results from micropipette aspiration suggest that cholesterol and ergosterol impact the order and microstructure of the gel (L(beta)') phase DPPC membrane. At low concentration (10-15 mol%) these sterols disrupt the long-range lateral order and fluidize the membrane (K(a) approximately 300 mN/m). Then at 18 mol%, these sterols participate in the formation of a continuous cohesive liquid-ordered (L(o)) phase with a sterol-dependent membrane density (K(a) approximately 750 for DPPC/ergosterol and K(a) approximately 1100 mN/m for DPPC/cholesterol). Finally at approximately 40 mol% both cholesterol and ergosterol impart similar condensation to the membrane (K(a) approximately 1200 mN/m). Introduction of ethanol (5-25 vol%) results in drops in the magnitude of K(a), which can be substantial, and sometimes individual vesicles with lowered K(a) reveal two slopes of tension versus apparent area strain. We postulate that this behavior represents disruption of lipid-sterol intermolecular interactions and therefore the membrane becomes interdigitation prone. We find that for DPPC vesicles with sterol concentrations of 20-25 mol%, significantly more ethanol is required to induce interdigitation compared to pure DPPC vesicles; approximately 7 vol% more for ergosterol and approximately 10 vol% more for cholesterol. For lower sterol concentrations (10-15 mol%), interdigitation is offset, but by <5 vol%. These data support the idea that ergosterol and cholesterol do enhance survivability for cells exposed to high concentrations of ethanol and provide evidence that the appearance of the interdigitated (L(beta)I) phase bilayer is a major factor in the disruption of cellular activity, which typically occurs between approximately 12 and approximately 16 vol% ethanol in yeast fermentations. We summarize our findings by producing, for the first time, "elasticity/phase diagrams" over a wide range of sterol (cholesterol and ergosterol) and ethanol concentrations.

Production of Botrytis cinerea for potential introduction into a vineyard

Botrytis cinerea was produced in solid-phase fermentation, liquid fermentation and on potato dextrose agar. Stored products were evaluated for grape colonization in grape bioassays and in field trials, and for B. cinerea density using colony forming unit analyses and a nucleic-acid-based method. B. cinerea colony forming unit density was significantly correlated to the probability of successful grape colonization in grape bioassays (p-value=0.0002). Solid fermentation products could be stored longer than liquid fermentation and potato dextrose agar products. There was little difference in the rate of grape colonization in laboratory bioassays among solid-phase fermentation, liquid fermentation and plate culture products. Although the initial B. cinerea colonization rate of field grapes was slightly greater on vines treated with solid-phase fermentation and plate culture products compared to vines treated with product from liquid fermentation, there was no significant difference in final colonization between vines treated with solid-phase fermentation, liquid fermentation and plate culture products and untreated vines.

An integrated approach to optimization of Escherichia coli fermentations using historical data

Using a fermentation database for Escherichia coli producing green fluorescent protein (GFP), we have implemented a novel three-step optimization method to identify the process input variables most important in modeling the fermentation, as well as the values of those critical input variables that result in an increase in the desired output. In the first step of this algorithm, we use either decision-tree analysis (DTA) or information theoretic subset selection (ITSS) as a database mining technique to identify which process input variables best classify each of the process outputs (maximum cell concentration, maximum product concentration, and productivity) monitored in the experimental fermentations. The second step of the optimization method is to train an artificial neural network (ANN) model of the process input-output data, using the critical inputs identified in the first step. Finally, a hybrid genetic algorithm (hybrid GA), which includes both gradient and stochastic search methods, is used to identify the maximum output modeled by the ANN and the values of the input conditions that result in that maximum. The results of the database mining techniques are compared, both in terms of the inputs selected and the subsequent ANN performance. For the E. coli process used in this study, we identified 6 inputs from the original 13 that resulted in an ANN that best modeled the GFP fluorescence outputs of an independent test set. Values of the six inputs that resulted in a modeled maximum fluorescence were identified by applying a hybrid GA to the ANN model developed. When these conditions were tested in laboratory fermentors, an actual maximum fluorescence of 2.16E6 AU was obtained. The previous high value of fluorescence that was observed was 1.51E6 AU. Thus, this input condition set that was suggested by implementing the proposed optimization scheme on the available historical database increased the maximum fluorescence by 55%.

Identification of critical batch operating parameters in fed-batch recombinant E. coli fermentations using decision tree analysis

To develop a useful fermentation process model, it is first necessary to identify which batch operating parameters are critical in determining the process outcome. To identify critical processing inputs in large databases, we have explored the use of Decision Tree Analysis with the decision metrics of Gain (i.e., Shannon Entropy changes), Gain Ratio, and a multiple hypergeometric distribution. The usefulness of this approach lies in its ability to treat "categorical" variables, which are typical of archived fermentation databases, as well as "continuous" variables. In this work, we demonstrate the use of Decision Tree Analysis for the problem of optimizing recombinant green fluorescent protein production in E. coli. A database of 85 fermentations was generated to examine the effect of 15 process input parameters on final biomass yield, maximum recombinant protein concentration, and productivity. The use of Decision Tree Analysis led to a considerable reduction in the fermentation database through the identification of the significant as well as insignificant inputs. However, different decision metrics selected different inputs and different numbers of inputs to classify the data for each output.

Kinetic model for nitrogen-limited wine fermentations

A physical and mathematical model for wine fermentation kinetics has been developed to predict sugar utilization curves based on experimental data from wine fermentations with various initial nitrogen and sugar concentrations in the juice. The model is based on: (1) yeast cell growth limited by nitrogen; (2) sugar utilization rates and ethanol production rates proportional solely to the number of viable cells; and (3) a death rate for cells proportional to alcohol content. All but one parameter in the model can be estimated from existing data. However, experiments to find this final parameter, a constant describing cell death, indicate that cell death may not be the critical factor in determining fermentation kinetics as cell viability remains significant until sugar utilization has ceased. The model, nevertheless, predicts a transition from normal to sluggish to stuck fermentations as initial nitrogen levels decrease. It also predicts that fermentations with high initial Brix levels may go to completion when supplemented with nitrogen in the form of ammonia. Therefore, we hypothesize that the model is valid but that ethanol causes the yeast cells to become inactive while remaining viable. Experimental verification of the model has been performed using flask-scale experiments. The model has also been used to evaluate the possibility of using nitrogen or viable cell additions to avoid or correct problem (i.e., sluggish or stuck) fermentations.

Technology-enhanced distance education: from experimentation to concerted action

Despite the rush to embrace technology-enhanced learning (TEL), descriptions of how schools of nursing move toward systemwide implementation of this type of teaching are scarce. There is a wide gap between sporadic dabbling by individual instructors in specific courses and adoption and implementation of TEL throughout a program. This article describes the experiences of a group of nursing faculty who helped move one school of nursing from experimentation with TEL in distance education to concerted action toward a strategic schoolwide plan. J Prof Nurs 17:135-140, 2001.