Genome-wide overexpression screen for sodium acetate resistance in Saccharomyces cerevisiae.

The production of biofuels from cellulosic biomass is a promising technology for developing a renewable source of energy. Efforts to produce ethanol from cellulosic biomass using microbes, such as the yeast Saccharomyces cerevisiae, face major challenges, including the need for detoxification. Here, we apply a strategy to discover genetic alterations that lead to improved robustness of S. cerevisiae in the presence of acetate, which is present at toxic concentrations in hemicellulose hydrolysates. Acetate in its protonated form (acetic acid) enters the cell through passive diffusion and dissociates into a proton and acetate, acidifying the cytosol and inhibiting cell function, an effect that is exacerbated in the presence of sodium. Through flow cytometry analysis, implemented as part of a novel cell culture technique, the Cytostat, we characterized the deleterious effects of sodium acetate on growth and on cell size homeostasis. Further, using the Cytostat to screen a genome-wide, gene overexpression library, we identified that overexpressing the ENA2 gene, a P-type sodium pump ATPase, provides a significant growth improvement in the presence of sodium acetate. Together, our data support the proposed mechanism for the synergistic growth inhibition exerted by acetate and sodium, as well as the mechanism that develops resistance.

Predicting the adaptive evolution of Thermoanaerobacterium saccharolyticum.

A fully evolved metabolic network can be described as a weighted sum of elementary modes where the usage probabilities of modes are distributed according to the Boltzmann distribution law (Srienc and Unrean, 2010). An organism presumably achieves the fully evolved state through adaptive changes in the kinetics of rate-controlling enzymes. Metabolic control analysis identifies reactions catalyzed by such enzymes. Comparison of the experimentally determined metabolic flux distributions of Thermoanaerobacterium saccharolyticum AS411 with the predicted flux distribution of a fully evolved metabolic network identified phosphoglucose isomerase (PGI) as the enzyme with the greatest flux control, the rate-controlling enzyme. The analysis predicts that an increased activity of PGI would enable the metabolic network to approach the fully evolved state and result in a faster specific growth rate. The prediction was confirmed by experimental results that showed an increased specific activity of PGI in a culture of strain AS411 that adaptively evolved over 280 generations. Sequencing of the gene confirmed the occurrence of a group of mutations clustered in the subunit binding domain of the dimeric enzyme. The results indicate that the evolutionary path is predictable as the strain AS411 adapted toward the fully evolved state by increasing the PGI activity. This experimental finding confirms that enzymes with predicted highest metabolic flux control are the targets of adaptive metabolic pathway evolution.

Parallelization of Nullspace Algorithm for the computation of metabolic pathways.

Elementary mode analysis is a useful metabolic pathway analysis tool in understanding and analyzing cellular metabolism, since elementary modes can represent metabolic pathways with unique and minimal sets of enzyme-catalyzed reactions of a metabolic network under steady state conditions. However, computation of the elementary modes of a genome- scale metabolic network with 100-1000 reactions is very expensive and sometimes not feasible with the commonly used serial Nullspace Algorithm. In this work, we develop a distributed memory parallelization of the Nullspace Algorithm to handle efficiently the computation of the elementary modes of a large metabolic network. We give an implementation in C++ language with the support of MPI library functions for the parallel communication. Our proposed algorithm is accompanied with an analysis of the complexity and identification of major bottlenecks during computation of all possible pathways of a large metabolic network. The algorithm includes methods to achieve load balancing among the compute-nodes and specific communication patterns to reduce the communication overhead and improve efficiency.

Metabolic networks evolve towards states of maximum entropy production.

A metabolic network can be described by a set of elementary modes or pathways representing discrete metabolic states that support cell function. We have recently shown that in the most likely metabolic state the usage probability of individual elementary modes is distributed according to the Boltzmann distribution law while complying with the principle of maximum entropy production. To demonstrate that a metabolic network evolves towards such state we have carried out adaptive evolution experiments with Thermoanaerobacterium saccharolyticum operating with a reduced metabolic functionality based on a reduced set of elementary modes. In such reduced metabolic network metabolic fluxes can be conveniently computed from the measured metabolite secretion pattern. Over a time span of 300 generations the specific growth rate of the strain continuously increased together with a continuous increase in the rate of entropy production. We show that the rate of entropy production asymptotically approaches the maximum entropy production rate predicted from the state when the usage probability of individual elementary modes is distributed according to the Boltzmann distribution. Therefore, the outcome of evolution of a complex biological system can be predicted in highly quantitative terms using basic statistical mechanical principles.

Single-cell tracking with a reversing flow cytometer.

We have developed an instrument based on a flow cytometer platform that is capable of tracking individual, suspended cells over extended time periods. The instrument repeatedly moves in a capillary the same volume segment of fluid containing tens to hundreds of suspended cells through the focal point of a laser. Individual cells are then tracked based on the timing of when they cross the laser, and cell properties are measured as in a conventional flow cytometer. Because cells are repeatedly measured the single-cell rates of change can be determined. The developed instrumentation was applied to measure the variability of ABC transporter activity in a population of human cancer cells and the temperature dependence of constitutively expressed Gfp in yeast. A wide range of transport rates can be observed in the cancer cell population while the single-cell Gfp fluorescence in yeast shows pronounced oscillations in response to temperature shifts. These observations are not detectable at the population level. Therefore, such measurements are useful for investigating cell function as they reveal how variable properties of single cells change over time.

Continuous production of ethanol from hexoses and pentoses using immobilized mixed cultures of Escherichia coli strains.

We have developed highly efficient ethanologenic Escherichia coli strains that selectively consume pentoses and/or hexoses. Mixed cultures of these strains can be used to selectively adjust the sugar utilization kinetics in ethanol fermentations. Based on the kinetics of sugar utilization, we have designed and implemented an immobilized cell system for the optimized continuous conversion of sugars into ethanol. The results confirm that immobilized mixed cultures support a simultaneous conversion of hexoses and pentoses into ethanol at high yield and at a faster rate than immobilized homogenous cells. Continuous ethanol production has been maintained for several weeks at high productivity with near complete sugar utilization. The control of sugar utilization using immobilized mixed cultures can be adapted to any composition of hexoses and pentoses by adjusting the strain distribution of immobilized cells. The approach, therefore, holds promise for ethanol fermentation from lignocellulosic hydrolysates where the feedstock varies in sugar composition.

Thymineless death is associated with loss of essential genetic information from the replication origin.

Thymine starvation results in a terminal cellular condition known as thymineless death (TLD), which is the basis of action for several common antibiotics and anticancer drugs. We characterized the onset and progression of TLD in Escherichia coli and found that DNA damage is the only salient property that distinguishes cells irreversibly senesced under thymine starvation from cells reversibly arrested by the nucleotide limitation. The damage is manifested as the relative loss of genetic material spreading outward from the replication origin: the extent of TLD correlates with the progression of damage. The reduced lethality in mutants deficient in the RecFOR/JQ repair pathway also correlates with the extent of damage, which explains most of the observed variance in cell killing. We propose that such spatially localized and persistent DNA damage is the consequence of transcription-dependent initiation of replication in the thymine-starved cells and may be the underlying cause of TLD.

On algebraic properties of extreme pathways in metabolic networks.

We give a concise development of some of the major algebraic properties of extreme pathways (pathways that cannot be the result of combining other pathways) of metabolic networks, contrasting them to those of elementary flux modes (pathways involving a minimal set of reactions). In particular, we show that an extreme pathway can be recognized by a rank test as simple as the existing rank test for elementary flux modes, without computing all the modes. We make the observation that, unlike elementary flux modes, the property of being an extreme pathway depends on the presence or absence of reactions beyond those involved in the pathway itself. Hence, the property of being an extreme pathway is not a local property. As a consequence, we find that the set of all elementary flux modes for a network includes all the elementary flux modes for all its subnetworks, but that this property does not hold for the set of all extreme pathways.

Rational design and construction of an efficient E. coli for production of diapolycopendioic acid.

We applied elementary mode analysis to a recombinant metabolic network of carotenoid-producing E. coli in order to identify multiple-gene knockouts for an enhanced synthesis of the carotenoids diapolycopendial (DPL) and diapolycopendioic acid (DPA). Based on the model, all inefficient carotenoid biosynthesis pathways were eliminated in a strain containing a combination of eight gene deletions. To validate the model prediction, the designed strain was constructed and tested for its performance. The designed mutant produces the carotenoids at significantly increased yields and rates as compared to the wild-type. The consistency between model prediction and experimental results demonstrates that elementary mode analysis is useful as a guiding tool also for the rational strain design of more complex pathways for secondary metabolite production.

Toward advanced ionic liquids. Polar, enzyme-friendly solvents for biocatalysis.

Ionic liquids, also called molten salts, are mixtures of cations and anions that melt below 100 °C. Typical ionic liquids are dialkylimidazolium cations with weakly coordinating anions such as [MeOSO3] or [PF6]. Advanced ionic liquids such as choline citrate have biodegradable, less expensive and less toxic anions and cations. Deep eutectic solvents are also included in the advanced ionic liquids. Deep eutectic solvents are mixtures of salts such as choline chloride and uncharged hydrogen bond donors such as urea, oxalic acid, or glycerol. For example, a mixture of choline chloride and urea in 1:2 molar ratio liquifies to form a deep eutectic solvent. Their properties are similar to those of ionic liquids. Water-miscible ionic liquids as cosolvents with water enhance the solubility of substrates or products. Although traditional water-miscible organic solvents also enhance solubility, they often inactivate enzymes, while ionic liquids do not. The enhanced solubility of substrates can increase the rate of reaction and often increases the regio- or enantioselectivity. Ionic liquids can also be solvents for non-aqueous reactions. In these cases, they are especially suited to dissolve polar substrates. Polar organic solvent alternatives inactivate enzymes, but ionic liquids do not even when they have similar polarities. Besides their solubility properties, ionic liquids and deep eutectic solvents may be greener than organic solvents because ionic liquids are non-volatile and can be made from non-toxic components. This review covers selected examples of enzyme catalyzed reaction ionic liquids that demonstrate their advantages and unique properties and point out opportunities for new applications. Most examples involve hydrolases, but oxidoreductases and even whole cell reactions have been reported in ionic liquids.

Flow cytometry without alignment of collection optics.

This study describes the performance of a new waveguide flow cell constructed from Teflon AF (TFC) and the potential use of fiber optic splitters to replace collection objectives and dichroic mirrors. The TFC has the unique optical property that the refractive index of the polymer is lower than water and therefore, water filled TFC behaves and functions as a liquid core waveguide. Thus, as cells flow through the TFC and are illuminated by a laser orthogonal to the flow direction, scattered and fluorescent light is directed down the axis of the TFC to a fiber optic. The total signal in the fiber optic is then split into multiple fibers by fiber optic splitters to enable measurement of signal intensities at different wavelengths. Optical filters are placed at the terminus of each fiber before measurement of specific wavelengths by a PMT. The constructed system was used to measure DNA content of CHO and yeast cells. Polystyrene beads were used for alignment and to assess the performance of the system. Polystyrene beads were observed to produce light scattering signals with unique bimodal characteristics dependent on the direction of flow relative to the collecting fiber optic.

Metabolic engineering of Escherichia coli for efficient conversion of glycerol to ethanol.

Based on elementary mode analysis, an Escherichia coli strain was designed for efficient conversion of glycerol to ethanol. By using nine gene knockout mutations, the functional space of the central metabolism of E. coli was reduced from over 15,000 possible pathways to a total of 28 glycerol-utilizing pathways that support cell function. Among these pathways are eight aerobic and eight anaerobic pathways that do not support cell growth but convert glycerol into ethanol with a theoretical yield of 0.50 g ethanol/g glycerol. The remaining 12 pathways aerobically coproduce biomass and ethanol from glycerol. The optimal ethanol production depends on the oxygen availability that regulates the two competing pathways for biomass and ethanol production. The coupling between cell growth and ethanol production enabled metabolic evolution of the designed strain through serial dilution that resulted in strains with improved ethanol yields and productivities. In defined medium, the evolved strain can convert 40 g/liter of glycerol to ethanol in 48 h with 90% of the theoretical ethanol yield. The performance of the designed strain is predicted by the property space of remaining elementary modes.

Rapid strain improvement through optimized evolution in the cytostat.

Acetate is present in lignocellulosic hydrolysates at growth inhibiting concentrations. Industrial processes based on such feedstock require strains that are tolerant of this and other inhibitors present. We investigated the effect of acetate on Saccharomyces cerevisiae and show that elevated acetate concentrations result in a decreased specific growth rate, an accumulation of cells in the G1 phase of the cell cycle, and an increased cell size. With the cytostat cultivation technology under previously derived optimal operating conditions, several acetate resistant mutants were enriched and isolated in the shortest possible time. In each case, the isolation time was less than 5 days. The independently isolated mutant strains have increased specific growth rates under conditions of high acetate concentrations, high ethanol concentrations, and high temperature. In the presence of high acetate concentrations, the isolated mutants produce ethanol at higher rates and titers than the parental strain and a commercial ethanol producing strain that has been analyzed for comparison. Whole genome microarray analysis revealed gene amplifications in each mutant. In one case, the LPP1 gene, coding for lipid phosphate phosphatase, was amplified. Two mutants contained amplified ENA1, ENA2, and ENA5 genes, which code for P-type ATPase sodium pumps. LPP1 was overexpressed on a plasmid, and the growth data at elevated acetate concentrations suggest that LPP1 likely contributes to the phenotype of acetate tolerance. A diploid cross of the two mutants with the amplified ENA genes grew faster than either individual haploid parent strain when 20 g/L acetate was supplemented to the medium, which suggests that these genes contribute to acetate tolerance in a gene dosage dependent manner.

Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism.

Elementary mode analysis is a useful metabolic pathway analysis tool to identify the structure of a metabolic network that links the cellular phenotype to the corresponding genotype. The analysis can decompose the intricate metabolic network comprised of highly interconnected reactions into uniquely organized pathways. These pathways consisting of a minimal set of enzymes that can support steady state operation of cellular metabolism represent independent cellular physiological states. Such pathway definition provides a rigorous basis to systematically characterize cellular phenotypes, metabolic network regulation, robustness, and fragility that facilitate understanding of cell physiology and implementation of metabolic engineering strategies. This mini-review aims to overview the development and application of elementary mode analysis as a metabolic pathway analysis tool in studying cell physiology and as a basis of metabolic engineering.

Optimized evolution in the cytostat: a Monte Carlo simulation.

Rational genetic alterations of a microorganism for a specific purpose are not possible in many situations where our knowledge of the relationship between phenotype and genotype is limited. In such cases evolutionary techniques must be applied. Evolutionary methods are usually time consuming; therefore, more efficient techniques are highly desirable. In this work we present the optimization of strain development in a cytostat. The time required for mutant strain isolation is dependent on the total cells present, the wild-type specific growth rate, the beneficial mutation probability, the mutant specific growth rate, and several bioreactor operating conditions. These parameters are highly related, and a theoretical model, as developed here, is needed to define the conditions that optimize the isolation. The model is based on a discrete, stochastic description of mutant formation and selection in the background of abundant wild-type cells. Using the model, we determined the optimal cytostat operating strategy for mutant isolation that varies according to the probability of beneficial mutations. It is also shown that mutants with as little as a 5% growth advantage can be isolated in less than 15 days which is significantly faster than in a chemostat. The described optimal mutant isolation procedure is expected to be particularly useful for the generation of industrial strains that are robust in challenging growth conditions.

Mammalian cell culture scale-up and fed-batch control using automated flow cytometry.

Detailed knowledge of mammalian cell culture proliferation kinetics is important to determine fed-batch strategies for industrial bioreactor operations. In particular, predicting the end of exponential proliferation in batch culture is a critical process parameter during culture scale-up. Using automated flow cytometry we show that an increase in the non-viable sub-population in CHO cell culture can predict the onset of stationary phase by approximately 40 h. This enables a completely automated culture scale-up process as well as a reliable and reproducible control of fed-batch additions during culture expansion. It is shown that the automated scale-up results in a significantly higher total cell count in the reactor than manual scale up initiated in stationary growth phase. During individual, subsequent culture expansions, a significant variation in the proliferation rate was observed despite control of bulk culture parameters. Thus, automated flow cytometry is critical to uncovering useful process parameters that enable new control strategies. Such improved process supervision derived from knowledge-based data analysis is central to the FDA's Process Analytical Technology (PAT) initiative and is expected to result in better and higher quality products.

Growth dynamics of mammalian cells monitored with automated cell cycle staining and flow cytometry.

To accurately observe high-frequency events during transient cell cycle kinetics, we have implemented a single step 15-min DNA staining protocol using automated flow cytometry. This protocol was used to sample and to analyze a Chinese hamster ovary cell culture for the DNA distribution, viable cell concentration, apoptotic cell concentration, and light scattering properties every 25 min over 4.5 days in response to a nutrient deprivation and a nutrient upshift. After the nutrient deprivation and exposure to fresh growth medium, two populations of cells started proliferating at different times likely corresponding to cells leaving the G0 and G1 cell cycle phases. After a nutrient upshift in late exponential growth, a cell cycle arrest occurred at the G1/S and G2/M boundary. The resulting cell cycle and proliferation kinetics followed damped oscillations that directly reveal the average time cells spend in each cell cycle phase. The observed detailed dynamics of the cell cycle progression is made possible through the high-frequency sampling enabled by automated flow cytometry. The approach should be useful in studying cell cycle perturbations in response to different environmental conditions resulting from exposure to specific nutrients or to drugs.

Minimal Escherichia coli cell for the most efficient production of ethanol from hexoses and pentoses.

To obtain an efficient ethanologenic Escherichia coli strain, we reduced the functional space of the central metabolic network, with eight gene knockout mutations, from over 15,000 pathway possibilities to 6 pathway options that support cell function. The remaining pathways, identified by elementary mode analysis, consist of four pathways with non-growth-associated conversion of pentoses and hexoses into ethanol at theoretical yields and two pathways with tight coupling of anaerobic cell growth with ethanol formation at high yields. Elimination of three additional genes resulted in a strain that selectively grows only on pentoses, even in the presence of glucose, with a high ethanol yield. We showed that the ethanol yields of strains with minimized metabolic functionality closely matched the theoretical predictions. Remarkably, catabolite repression was completely absent during anaerobic growth, resulting in the simultaneous utilization of pentoses and hexoses for ethanol production.

Hydrolase-catalyzed biotransformations in deep eutectic solvents.

Hydrolases show good catalytic activity in deep eutectic solvents, despite the presence of urea, which can denature enzymes, or alcohols, which can interfere with hydrolase-catalyzed reactions.