Enhanced ethyl caproate production of Chinese liquor yeast by overexpressing EHT1 with deleted FAA1

The fruity odor of Chinese liquor is largely derived from ester formation. Ethyl caproate, an ethyl ester eliciting apple-like flavor, is one of the most important esters in the strong aromatic Chinese liquor (or Luzhou-flavor liquor), which is the most popular and best-selling liquor in China. In the traditional fermentation process, ethyl caproate in strong aromatic liquor is mainly produced by aroma-producing yeast, bacteria, and mold with high esterification abilities in a mud pit at later fermentation stages at the expense of both fermentation time and grains rather than by the ethanol-fermenting yeast Saccharomyces cerevisiae. To increase the production of ethyl caproate by Chinese liquor yeast (S. cerevisiae AY15) and shorten the fermentation period, we constructed a recombinant strain EY15 by overexpressing EHT1 (encoding ethanol hexanoyl transferase), in which FAA1 (encoding acyl-CoA synthetases) was deleted. In liquid fermentation of corn hydrolysate and solid fermentation of sorghum, ethyl caproate production by EY15 was remarkably increased to 2.23 and 2.83 mg/L, respectively, which were 2.97- and 2.80-fold higher than those of the parental strain AY15. Furthermore, an increase in ethyl octanoate (52 and 43 %) and ethyl decanoate (61 and 40 %) production was observed. The differences in fermentation performance between EY15 and AY15 were negligible. This study resulted in the creation of a promising recombinant yeast strain and introduced a method that can be used for the clean production of strong aromatic Chinese liquor by ester-producing S. cerevisiae without the need for a mud pit.     

果蔬废弃物两相厌氧发酵产己酸的效能研究

利用厌氧菌群生物合成己酸被认为是一种非常有潜力的新型废弃物资源化技术,但是其合成效能的提高是目前亟待解决的关键问题。本研究以实际果蔬废弃物为原料,对两相厌氧发酵产己酸的效能进行了研究。首先优化接种比以提高酸化相的水解转化效率;在此基础上通过调控醇酸比和pH以强化产己酸相的发酵效能。结果显示,果蔬废弃物厌氧产酸的最佳接种比为2∶1,此时水解率和酸化率分别可达到98.1%和83.2%,乙酸和丁酸产量分别达到5.4 g/L和3.3 g/L。合理控制醇酸比和pH对提高产己酸相的发酵效能非常关键。当醇酸比和pH控制为4∶1和7.5时,己酸生成量可达14.9 g/L,约占液相总COD的80.84%;而低醇酸比和低pH易造成丁酸的累积,从而降低了己酸产量。己酸发酵过程属于非生长偶联型,己酸菌(Clostridium kluyveri)指数增长期伴随着丁酸的生成,而己酸合成主要发生在生长中后期。此外,己酸菌对于pH变化较为敏感,适当提高pH有助于减轻有机酸毒性,提高生物量;但是碱性环境会严重抑制己酸菌的生长繁殖。研究表明,通过分别对酸化相和产己酸相进行优化和调控,两相发酵策略更有利于提高己酸合成效能。     

Quantitative analysis of growth and volatile fatty acid production by the anaerobic ruminal bacterium Megasphaera elsdenii T81

Megasphaera elsdenii T81 grew on either dl-lactate or d-glucose at similar rates (0.85 h^?1) but displayed major differences in the fermentation of these substrates. Lactate was fermented at up to 210-mM concentration to yield acetic, propionic, butyric, and valeric acids. The bacterium was able to grow at much higher concentrations of d-glucose (500 mM), but never removed more than 80 mM of glucose from the medium, and nearly 60 % the glucose removed was sequestered as intracellular glycogen, with low yields of even-carbon acids (acetate, butyrate, caproate). In the presence of both substrates, glucose was not used until lactate was nearly exhausted, even by cells pregrown on glucose. Glucose-grown cultures maintained only low extracellular concentrations of acetate, and addition of exogenous acetate increased yields of butyrate, but not caproate. By contrast, exogenous acetate had little effect on lactate fermentation. At pH 6.6, growth rate was halved by exogenous addition of 60 mM propionate, 69 mM butyrate, 44 mM valerate, or 33 mM caproate; at pH 5.9, these values were reduced to 49, 49, 18, and 22 mM, respectively. The results are consistent with this species’ role as an effective ruminal lactate consumer and suggest that this organism may be useful for industrial production of volatile fatty acids from lactate if product tolerance could be improved. The poor fermentation of glucose and sensitivity to caproate suggests that this strain is not practical for industrial caproate production.     

A prototype neural network supervised control system for Bacillus thuringiensis fermentations

This article discusses the development of a prototype neural network-based supervisory control system for Bacillus thuringiensis fermentations. The input pattern to the neural network included the type of inoculum, operation temperature, pH value, accumulated process time, optical density in fermentation medium, and change in optical density. The output from the neural network was the predicted optical density for the next sampling time. The control system has been implemented in both a computer simulation and a laboratory fermentation experiment with promising results. (c) 1994 John Wiley & Sons, Inc.     

Dynamic neural networks as a tool for the online optimization of industrial fermentation

A system for online optimization of industrial fermentation based on a model with dynamic neural networks is described. The developed dynamic neural network, consisting of adapted neurons to consider the process dynamics, can model the complex, non-linear fermentation of beer in order to predict the future process. The predicted trajectories of gravity, pH, and diacetyl are in agreement with the experimental data measured at an automated pilot fermenter. It was possible to predict the future course of the batch fermentation as soon as 12 h of process data were available. In combination with the variational principle, the process model was used to optimize productivity. The temperature trajectory is optimized using a cost functional, including technical and technological conditions of the brewery in order to reduce the process time by steady product quality. The results show a reduction of the process time of up to 20%, which leads to an increase in utilization capacity.     

Polyploid engineering by increasing mutant gene dosage in yeasts

The yeast Saccharomyces cerevisiae, widely used for ethanol production, is one of the best-understood biological systems. Diploid strains of S. cerevisiae are preferred for industrial use due to the better fermentation efficiency, in terms of vitality and endurance as compared to those of haploid strains. Whole-genome duplications is known to promote adaptive mutations in microorganisms, and allelic variations considerably contribute to the product composition in ethanol fermentation. Although fermentation can be regulated using various strains of yeast, it is quite difficult to make fine adjustment of each component in final products. In this study, we demonstrate the use of polyploids with varying gene dosage (the number of copies of a particular gene present in a genome) in the regulation of ethanol fermentation. Ethyl caproate is one of the major flavouring agents in a Japanese alcoholic beverage called sake. A point mutation in FAS2 encoding the α subunit of fatty acid synthetase induces an increase in the amount of caproic acid, a precursor of ethyl caproate. Using the FAS2 as a model, we generated and evaluated yeast strains with varying mutant gene dosage. We demonstrated the possibility to increase mutant gene dosage via loss of heterozygosity in diploid and tetraploid strains. Productivity of ethyl caproate gradually increased with mutant gene dosage among tetraploid strains. This approach can potentially be applied to a variety of yeast strain development via growth-based screening.     

Improved ethyl caproate production of Chinese liquor yeast by overexpressing fatty acid synthesis genes with <Emphasis Type="Italic">OPI1</Emphasis> deletion

During yeast fermentation, ethyl esters play a key role in the development of the flavor profiles of Chinese liquor. Ethyl caproate, an ethyl ester eliciting apple-like flavor, is the characteristic flavor of strong aromatic liquor, which is the best selling liquor in China. In the traditional fermentation process, ethyl caproate is mainly produced at the later fermentation stage by aroma-producing yeast, bacteria, and mold in a mud pit instead of Saccharomyces cerevisiae at the expense of grains and fermentation time. To improve the production of ethyl caproate by Chinese liquor yeast (S. cerevisiae) with less food consumption and shorter fermentation time, we constructed three recombinant strains, namely, α5-ACC1ΔOPI1, α5-FAS1ΔOPI1, and α5-FAS2ΔOPI1 by overexpressing acetyl-CoA carboxylase (ACC1), fatty acid synthase 1 (FAS1), and fatty acid synthase 2 (FAS2) with OPI1 (an inositol/choline-mediated negative regulatory gene) deletion, respectively. In the liquid fermentation of corn hydrolysate, the contents of ethyl caproate produced by α5-ACC1ΔOPI1, α5-FAS1ΔOPI1, and α5-FAS2ΔOPI1 increased by 0.40-, 1.75-, and 0.31-fold, correspondingly, compared with the initial strain α5. The contents of other fatty acid ethyl esters (FAEEs) (C8:0, C10:0, C12:0) also increased. In comparison, the content of FAEEs produced by α5-FAS1ΔOPI1 significantly improved. Meanwhile, the contents of acetyl-CoA and ethyl acetate were enhanced by α5-FAS1ΔOPI1. Overall, this study offers a promising platform for the development of pure yeast culture fermentation of Chinese strong aromatic liquor without the use of a mud pit.     

Using historical data for bioprocess optimization: modeling wine characteristics using artificial neural networks and archived process information

Optimization of fermentation processes is a difficult task that relies on an understanding of the complex effects of processing inputs on productivity and quality outputs. Because of the complexity of these biological systems, traditional optimization methods utilizing mathematical models and statistically designed experiments are less effective, especially on a production scale. At the same time, information is being collected on a regular basis during the course of normal manufacturing and process development that is rarely fully utilized. We are developing an optimization method in which historical process data is used to train an artificial neural network for correlation of processing inputs and outputs. Subsequently, an optimization routine is used in conjunction with the trained neural network to find optimal processing conditions given the desired product characteristics and any constraints on inputs. Wine processing is being used as a case study for this work. Using data from wine produced in our pilot winery over the past 3 years, we have demonstrated that trained neural networks can be used successfully to predict the yeast-fermentation kinetics, as well as chemical and sensory properties of the finished wine, based solely on the properties of the grapes and the intended processing. To accomplish this, a hybrid neural network training method, Stop Training with Validation (STV), has been developed to find the most desirable neural network architecture and training level. As industrial historical data will not be evenly spaced over the entire possible search space, we have also investigated the ability of the trained neural networks to interpolate and extrapolate with data not used during training. Because a company will utilize its own existing process data for this method, the result of this work will be a general fermentation optimization method that can be applied to fermentation processes to improve quality and productivity.     

Neural network modeling in optimisation of continuous fermentation processes

The capability of self-recurrent neural networks in dynamic modeling of continuous fermentation is investigated in this simulation study. In the past, feedforward neural networks have been successfully used as one-step-ahead predictors. However, in steady-state optimisation of continuous fermentations the neural network model has to be iterated to predict many time steps ahead into the future in order to get steady-state values of the variables involved in objective cost function, and this iteration may result in increasing errors. Therefore, as an alternative to classical feedforward neural network trained by using backpropagation method, self-recurrent multilayer neural net trained by backpropagation through time method was chosen in order to improve accuracy of long-term predictions. Prediction capabilities of the resulting neural network model is tested by implementing this into the Integrated System Optimisation and Parameter Estimation (ISOPE) optimisation algorithm. Maximisation of cellular productivity of the baker's yeast continuous fermentation was used as the goal of the proposed optimising control problem. The training and prediction results of proposed neural network and performances of resulting optimisation structure are demonstrated.     

Backpropagation neural network predictive control and control scheme comparison of 2,3-butanediol fermentation by Klebsiella oxytoca

A backpropagation neural network (BPN) was applied for the control study of 2,3-butanediol fermentation (2,3-BDL) carried by Klebsiella oxytoca. The measurements of cell mass and glucose were not included in the network models, instead, only the on-line measured product concentrations from the MIMS (membrane introduction mass spectrometer) were involved. Oxygen composition was chosen to be the control variable for this fermentation system for the formation of 2,3-BDL is regulated by oxygen. Oxygen composition was directly correlated to the measured product concentrations. A two-dimensional (number of input nodes by number of data sets) moving window to supply data for on-line, dynamic learning of this fermentation system was applied. The input nodes of the networks were also properly selected. Two neural network control schemes for this 2,3-BDL fermentation were discussed and compared in this work. Fermentations often exist time delay due to the measurement and their slow reaction nature. Hence, the order of time delay for the network controller was also investigated.     

Modeling of the pyruvate production with Escherichia coli: comparison of mechanistic and neural networks-based models

Three different models: the unstructured mechanistic black-box model, the input–output neural network-based model and the externally recurrent neural network model were used to describe the pyruvate production process from glucose and acetate using the genetically modified Escherichia coli YYC202 ldhA::Kan strain. The experimental data were used from the recently described batch and fed-batch experiments [ Zelić B, Study of the process development for Escherichia coli-based pyruvate production. PhD Thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, Zagreb, Croatia, July 2003. (In English); Zelić et al. Bioproc Biosyst Eng 26:249–258 (2004); Zelić et al. Eng Life Sci 3:299–305 (2003); Zelić et al Biotechnol Bioeng 85:638–646 (2004)]. The neural networks were built out of the experimental data obtained in the fed-batch pyruvate production experiments with the constant glucose feed rate. The model validation was performed using the experimental results obtained from the batch and fed-batch pyruvate production experiments with the constant acetate feed rate. Dynamics of the substrate and product concentration changes was estimated using two neural network-based models for biomass and pyruvate. It was shown that neural networks could be used for the modeling of complex microbial fermentation processes, even in conditions in which mechanistic unstructured models cannot be applied.     

Optimization of a fermentation medium using neural networks and genetic algorithms

Artificial neural networks and genetic algorithms are used to model and optimize a fermentation medium for the production of the enzyme hydantoinase by Agrobacterium radiobacter. Experimental data reported in the literature were used to build two neural network models. The concentrations of four medium components served as inputs to the neural network models, and hydantoinase or cell concentration served as a single output of each model. Genetic algorithms were used to optimize the input space of the neural network models to find the optimum settings for maximum enzyme and cell production. Using this procedure, two artificial intelligence techniques have been effectively integrated to create a powerful tool for process modeling and optimization.     

Improvement of trypanocidal metabolites production by Aspergillus fumigatus using neural networks

An optimization procedure using artificial neural networks was developed to determine the optimal combination of parameters, such as medium culture, initial pH, temperature and time of fermentation for maximal trypanocidal metabolites production by Aspergillus fumigatus. A data set of 81 experiments was carried out and an artificial neural network was trained to identify the optimal conditions for this process. Good correlation was obtained between the experimental and predicted values of lysis of the trypomastigote forms of Trypanosoma cruzi (r2 = 0.9990). The simulations of fermentation performance were undertaken on combinations of input variables and the highest level of activity against T. cruzi was obtained from the chloroform extract of the modified Jackson medium culture, initial pH of 6.0, incubated at 40 degrees C for 144 h. It displayed lysis of 95% of the trypomastigote forms of T. cruzi and the red blood cells remained normal.     

Prediction of problematic wine fermentations using artificial neural networks

Artificial neural networks (ANNs) have been used for the recognition of non-linear patterns, a characteristic of bioprocesses like wine production. In this work, ANNs were tested to predict problems of wine fermentation. A database of about 20,000 data from industrial fermentations of Cabernet Sauvignon and 33 variables was used. Two different ways of inputting data into the model were studied, by points and by fermentation. Additionally, different sub-cases were studied by varying the predictor variables (total sugar, alcohol, glycerol, density, organic acids and nitrogen compounds) and the time of fermentation (72, 96 and 256 h). The input of data by fermentations gave better results than the input of data by points. In fact, it was possible to predict 100% of normal and problematic fermentations using three predictor variables: sugars, density and alcohol at 72 h (3 days). Overall, ANNs were capable of obtaining 80% of prediction using only one predictor variable at 72 h; however, it is recommended to add more fermentations to confirm this promising result.     

Metabolic Energy-Based Modelling Explains Product Yielding in Anaerobic Mixed Culture Fermentations

The fermentation of glucose using microbial mixed cultures is of great interest given its potential to convert wastes into valuable products at low cost, however, the difficulties associated with the control of the process still pose important challenges for its industrial implementation. A deeper understanding of the fermentation process involving metabolic and biochemical principles is very necessary to overcome these difficulties. In this work a novel metabolic energy based model is presented that accurately predicts for the first time the experimentally observed changes in product spectrum with pH. The model predicts the observed shift towards formate production at high pH, accompanied with ethanol and acetate production. Acetate (accompanied with a more reduced product) and butyrate are predicted main products at low pH. The production of propionate between pH 6 and 8 is also predicted. These results are mechanistically explained for the first time considering the impact that variable proton motive potential and active transport energy costs have in terms of energy harvest over different products yielding. The model results, in line with numerous reported experiments, validate the mechanistic and bioenergetics hypotheses that fermentative mixed cultures products yielding appears to be controlled by the principle of maximum energy harvest and the necessity of balancing the redox equivalents in absence of external electron acceptors.     

Bioprocess model building using artificial neural networks

Artificial neural networks are made upon of highly interconnected layers of simple neuron-like nodes. The neurons act as non-linear processing elements within the network. An attractive property of artificial neural networks is that given the appropriate network topology, they are capable of learning and characterising non-linear functional relationships. Furthermore, the structure of the resulting neural network based process model may be considered generic, in the sense that little prior process knowledge is required in its determination. The methodology therefore provides a cost efficient and reliable process modelling technique. One area where such a technique could be useful is biotechnological systems. Here, for example, the use of a process model within an estimation scheme has long been considered an effective means of overcoming inherent on-line measurement problems. However, the development of an accurate process model is extremely time consuming and often results in a model of limited applicability. Artificial neural networks could therefore prove to be a useful model building tool when striving to improve bioprocess operability. Two large scale industrial fermentation systems have been considered as test cases; a fed-batch penicillin fermentation and a continuous mycelial fermentation. Both systems serve to demonstrate the utility, flexibility and potential of the artificial neural network approach to process modelling.     

Directed evolution of industrial enzymes: an update

The use of enzymes in industrial processes can often eliminate the use of high temperatures, organic solvents and extremes of pH, while at the same time offering increased reaction specificity, product purity and reduced environmental impact. The growing use of industrial enzymes is dependent on constant innovation to improve performance and reduce cost. This innovation is driven by a rapidly increasing database of natural enzyme diversity, recombinant DNA and fermentation technologies that allow this diversity to be produced at low cost, and protein modification tools that enable enzymes to be tuned to fit into the industrial marketplace.     

Production of caproic acid by cocultures of ruminal cellulolytic bacteria and Clostridium kluyveri grown on cellulose and ethanol

Ruminal cellulolytic bacteria (Fibrobacter succinogenes S85 or Ruminococcus flavefaciens FD-1) were combined with the non-ruminal bacterium Clostridium kluyveri and grown together on cellulose and ethanol. Succinate and acetate produced by the cellulolytic organisms were converted to butyrate and caproate only when the culture medium was supplemented with ethanol. Ethanol (244 mM) and butyrate (30 mM at pH 6.8) did not inhibit cellulose digestion or product formation by S85 or FD-1; however caproate (30 mM at pH 6.8) was moderately inhibitory to FD-1. Succinate consumption and caproate production were sensitive to culture pH, with more caproic acid being produced when the culture was controlled at a pH near neutrality. In a representative experiment under conditions of controlled pH (at 6.8) 6.0 g cellulose 1^–1 and 4.4 g ethanol 1^–1 were converted to 2.6 g butyrate 1^–1 and 4.6 g caproate 1^–1. The results suggest that bacteria that efficiently produce low levels of ethanol and acetate or succinate from cellulose should be useful in cocultures for the production of caproic acid, a potentially useful industrial chemical and bio-fuel precursor.Mention of specific products is intended only to provide information and does not contitute an endorsement by the U.S. Department of Agriculture over other products not mentioned.     

Use of Artificial Neural Networks and a Gamma-Concept-Based Approach To Model Growth of and Bacteriocin Production by Streptococcus macedonicus ACA-DC 198 under Simulated Conditions of Kasseri Cheese Production

Growth of and bacteriocin production by Streptococcus macedonicus ACA-DC 198 were assessed and modeled under conditions simulating Kasseri cheese production. Controlled fermentations were performed in milk supplemented with yeast extract at different combinations of temperature (25, 40, and 55°C), constant pH (pHs 5 and 6), and added NaCl (at concentrations of 0, 2, and 4%, wt/vol). The data obtained were used to construct two types of predictive models, namely, a modeling approach based on the gamma concept, as well as a model based on artificial neural networks (ANNs). The latter computational methods were used on 36 control fermentations to quantify the complex relationships between the conditions applied (temperature, pH, and NaCl) and population behavior and to calculate the associated biokinetic parameters, i.e., maximum specific growth and cell count decrease rates and specific bacteriocin production. The functions obtained were able to estimate these biokinetic parameters for four validation fermentation experiments and obtained good agreement between modeled and experimental values. Overall, these experiments show that both methods can be successfully used to unravel complex kinetic patterns within biological data of this kind and to predict population kinetics. Whereas ANNs yield a better correlation between experimental and predicted results, the gamma-concept-based model is more suitable for biological interpretation. Also, while the gamma-concept-based model has not been designed for modeling of other biokinetic parameters than the specific growth rate, ANNs are able to deal with any parameter of relevance, including specific bacteriocin production.     

Stress tolerance and growth physiology of yeast strains from the Brazilian fuel ethanol industry

Improved biofuels production requires a better understanding of industrial microorganisms. Some wild Saccharomyces cerevisiae strains, isolated from the fuel ethanol industry in Brazil, present exceptional fermentation performance, persistence and prevalence in the harsh industrial environment. Nevertheless, their physiology has not yet been systematically investigated. Here we present a first systematic evaluation of the widely used industrial strains PE-2, CAT-1, BG-1 and JP1, in terms of their tolerance towards process-related stressors. We also analyzed their growth physiology under heat stress. These strains were evaluated in parallel to laboratory and baker’s strains. Whereas the industrial strains performed in general better than the laboratory strains under ethanol or acetic acid stresses and on industrial media, high sugar stress was tolerated equally by all strains. Heat and low pH stresses clearly distinguished fuel ethanol strains from the others, indicating that these conditions might be the ones that mostly exert selective pressure on cells in the industrial environment. During shake-flask cultivations using a synthetic medium at 37 °C, industrial strains presented higher ethanol yields on glucose than the laboratory strains, indicating that they could have been selected for this trait—a response to energy-demanding fermentation conditions. These results might be useful to guide future improvements of large-scale fuel ethanol production via engineering of stress tolerance traits in other strains, and eventually also for promoting the use of these fuel ethanol strains in different industrial bioprocesses.