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.