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Gregory Stephanopoulos

Publications and source records attributed to Gregory Stephanopoulos.

At least 19 recordsLinked to original sources
PubMed2007

Engineering promoter regulation.

Systems for easily controlled, conditional induction or repression of gene expression are indispensable tools in fundamental research and industrial-scale biotechnological applications. Both native and rationally designed inducible promoters have been widely used for this purpose. However, inherent regulation modalities or toxic, expensive or inconvenient inducers can impose limitations on their use. Tailored promoters with user-specified regulatory properties would permit sophisticated manipulations of gene expression. Here, we report a generally applicable strategy for the directed evolution of promoter regulation. Specifically, we applied random mutagenesis and a multi-stage flow cytometry screen to isolate mutants of the oxygen-responsive Saccharomyces cerevisiae DAN1 promoter. Two mutants were isolated which were induced under less-stringent anaerobiosis than the wild-type promoter enabling induction of gene expression in yeast fermentations simply by oxygen depletion during cell growth. Moreover, the engineered promoters showed a markedly higher maximal expression than the unmutated DAN1 promoter, under both fastidious anaerobiosis and microaerobisois.

Aerobiosis↗
PubMed2007

L-tyrosine production by deregulated strains of Escherichia coli.

The excretion of the aromatic amino acid L: -tyrosine was achieved by manipulating three gene targets in the wild-type Escherichia coli K12: The feedback-inhibition-resistant (fbr) derivatives of aroG and tyrA were expressed on a low-copy-number vector, and the TyrR-mediated regulation of the aromatic amino acid biosynthesis was eliminated by deleting the tyrR gene. The generation of this L: -tyrosine producer, strain T1, was based only on the deregulation of the aromatic amino acid biosynthesis pathway, but no structural genes in the genome were affected. A second tyrosine over-producing strain, E. coli T2, was generated considering the possible limitation of precursor substrates. To enhance the availability of the two precursor substrates phosphoenolpyruvate and erythrose-4-phosphate, the ppsA and the tktA genes were over-expressed in the strain T1 background, increasing L: -tyrosine production by 80% in 50-ml batch cultures. Fed-batch fermentations revealed that L: -tyrosine production was tightly correlated with cell growth, exhibiting the maximum productivity at the end of the exponential growth phase. The final L: -tyrosine concentrations were 3.8 g/l for E. coli T1 and 9.7 g/l for E. coli T2 with a yield of L: -tyrosine per glucose of 0.037 g/g (T1) and 0.102 g/g (T2), respectively.

Biotechnology↗
PubMed2006

Engineering yeast transcription machinery for improved ethanol tolerance and production.

Global transcription machinery engineering (gTME) is an approach for reprogramming gene transcription to elicit cellular phenotypes important for technological applications. Here we show the application of gTME to Saccharomyces cerevisiae for improved glucose/ethanol tolerance, a key trait for many biofuels programs. Mutagenesis of the transcription factor Spt15p and selection led to dominant mutations that conferred increased tolerance and more efficient glucose conversion to ethanol. The desired phenotype results from the combined effect of three separate mutations in the SPT15 gene [serine substituted for phenylalanine (Phe(177)Ser) and, similarly, Tyr(195)His, and Lys(218)Arg]. Thus, gTME can provide a route to complex phenotypes that are not readily accessible by traditional methods.

Amino Acid Substitution↗
PubMed2006

A linguistic model for the rational design of antimicrobial peptides.

Antimicrobial peptides (AmPs) are small proteins that are used by the innate immune system to combat bacterial infection in multicellular eukaryotes. There is mounting evidence that these peptides are less susceptible to bacterial resistance than traditional antibiotics and could form the basis for a new class of therapeutic agents. Here we report the rational design of new AmPs that show limited homology to naturally occurring proteins but have strong bacteriostatic activity against several species of bacteria, including Staphylococcus aureus and Bacillus anthracis. These peptides were designed using a linguistic model of natural AmPs: we treated the amino-acid sequences of natural AmPs as a formal language and built a set of regular grammars to describe this language. We used this set of grammars to create new, unnatural AmP sequences. Our peptides conform to the formal syntax of natural antimicrobial peptides but populate a previously unexplored region of protein sequence space.

Amino Acid Sequence↗
PubMed2006

Elementary metabolite units (EMU): a novel framework for modeling isotopic distributions.

Metabolic flux analysis (MFA) has emerged as a tool of great significance for metabolic engineering and mammalian physiology. An important limitation of MFA, as carried out via stable isotope labeling and GC/MS and nuclear magnetic resonance (NMR) measurements, is the large number of isotopomer or cumomer equations that need to be solved, especially when multiple isotopic tracers are used for the labeling of the system. This restriction reduces the ability of MFA to fully utilize the power of multiple isotopic tracers in elucidating the physiology of realistic situations comprising complex bioreaction networks. Here, we present a novel framework for the modeling of isotopic labeling systems that significantly reduces the number of system variables without any loss of information. The elementary metabolite unit (EMU) framework is based on a highly efficient decomposition method that identifies the minimum amount of information needed to simulate isotopic labeling within a reaction network using the knowledge of atomic transitions occurring in the network reactions. The functional units generated by the decomposition algorithm, called EMUs, form the new basis for generating system equations that describe the relationship between fluxes and stable isotope measurements. Isotopomer abundances simulated using the EMU framework are identical to those obtained using the isotopomer and cumomer methods, however, require significantly less computation time. For a typical (13)C-labeling system the total number of equations that needs to be solved is reduced by one order-of-magnitude (100s EMUs vs. 1000s isotopomers). As such, the EMU framework is most efficient for the analysis of labeling by multiple isotopic tracers. For example, analysis of the gluconeogenesis pathway with (2)H, (13)C, and (18)O tracers requires only 354 EMUs, compared to more than two million isotopomers.

Carbon↗
PubMed2006

Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway.

This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U-(13)C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown.

Journal Article↗
PubMed2006

Determination of confidence intervals of metabolic fluxes estimated from stable isotope measurements.

Metabolic fluxes, estimated from stable isotope studies, provide a key to quantifying physiology in fields ranging from metabolic engineering to the analysis of human metabolic diseases. A serious drawback of the flux estimation method in current use is that it does not produce confidence limits for the estimated fluxes. Without this information it is difficult to interpret flux results and expand the physiological significance of flux studies. To address this shortcoming we derived analytical expressions of flux sensitivities with respect to isotope measurements and measurement errors. These tools allow the determination of local statistical properties of fluxes and relative importance of measurements. Furthermore, we developed an efficient algorithm to determine accurate flux confidence intervals and demonstrated that confidence intervals obtained with this method closely approximate true flux uncertainty. In contrast, confidence intervals approximated from local estimates of standard deviations are inappropriate due to inherent system nonlinearities. We applied these methods to analyze the statistical significance and confidence of estimated gluconeogenesis fluxes from human studies with [U-13C]glucose as tracer and found true limits for flux estimation in specific human isotopic protocols.

Blood Glucose↗
PubMed2006

Characterization of lycopene-overproducing E. coli strains in high cell density fermentations.

Previous work identified two recombinant strains of Escherichia coli capable of significant lycopene overproduction. These strains were constructed by superimposing the deletion of three genes, selected through combinatorial and systematic searches of the metabolic landscape, onto a previously engineered strain over-expressing critical genes in the lycopene biosynthesis pathway. In this paper, we characterize the performance of these two strains in comparison to the parental, pre-engineered strain. Specifically, high cell density fermentations were performed after identifying optimized putative operating parameters. High oxygen levels and increased pH values were found to be critical for increasing both specific and volumetric product titers. Carbon balances suggest linkages between glutamate, NADPH, formate, and alanine levels with lycopene overproduction. Furthermore, lycopene production reached nearly 220 mg/l from approximately 27 g dry cell weight/l in these reactors, which is the highest value reported to date for E. coli.

Carbon↗
PubMed2006

Engineering of promoter replacement cassettes for fine-tuning of gene expression in Saccharomyces cerevisiae.

The strong overexpression or complete deletion of a gene gives only limited information about its control over a certain phenotype or pathway. Gene function studies based on these methods are therefore incomplete. To effect facile manipulation of gene expression across a full continuum of possible expression levels, we recently created a library of mutant promoters. Here, we provide the detailed characterization of our yeast promoter collection comprising 11 mutants of the strong constitutive Saccharomyces cerevisiae TEF1 promoter. The activities of the mutant promoters range between about 8% and 120% of the activity of the unmutated TEF1 promoter. The differences in reporter gene expression in the 11 mutants were independent of the carbon source used, and real-time PCR confirmed that these differences were due to varying levels of transcription (i.e., caused by varying promoter strengths). In addition to a CEN/ARS plasmid-based promoter collection, we also created promoter replacement cassettes. They enable genomic integration of our mutant promoter collection upstream of any given yeast gene, allowing detailed genotype-phenotype characterizations. To illustrate the utility of the method, the GPD1 promoter of S. cerevisiae was replaced by five TEF1 promoter mutants of different strengths, which allowed analysis of the impact of glycerol 3-phosphate dehydrogenase activity on the glycerol yield.

Base Sequence↗
PubMed2006

Identifying functionally important mutations from phenotypically diverse sequence data.

Here we present a simple statistical method to determine the phenotypic contribution of a single mutation from libraries of mutants with diverse phenotypes in which each mutant contains a multitude of mutations. The central premise of this method is that, given M phenotypic classes, mutations that do not affect the phenotype should partition among the M classes according to a multinomial distribution. Deviations from this distribution are indicative of a link between specific mutations and phenotypes. We suggest that this method will aid the engineering of functional nucleic acids, proteins, and other biomolecules by uncovering target sites for rational mutagenesis. As a proof of the principle, we show how the method can be used to deduce the individual effects of mutations in a set of 69 P(L)-lambda promoter variants. Each of these promoters was generated by error-prone PCR and incorporated numerous mutations. The activity of the promoters was assayed using flow cytometry to measure the fluorescence of a green fluorescent protein reporter gene. Our analysis of the sequences of these mutants revealed seven positions having a statistically significant correlation with promoter activity. Using site-directed mutagenesis, we constructed point mutations for several sites, both statistically significant and insignificant, and combinations of these sites. Our results show that the statistical method correctly elucidated the phenotypic manifestations of these mutations. We suggest that this method may be useful for expediting directed evolution experiments by allowing both desired and undesired mutations to be identified and incorporated between rounds of mutagenesis.

Base Sequence↗
PubMed2005

Tuning genetic control through promoter engineering.

Gene function is typically evaluated by sampling the continuum of gene expression at only a few discrete points corresponding to gene knockout or overexpression. We argue that this characterization is incomplete and present a library of engineered promoters of varying strengths obtained through mutagenesis of a constitutive promoter. A multifaceted characterization of the library, especially at the single-cell level to ensure homogeneity, permitted quantitative assessment correlating the effect of gene expression levels to improved growth and product formation phenotypes in Escherichia coli. Integration of these promoters into the chromosome can allow for a quantitative accurate assessment of genetic control. To this end, we used the characterized library of promoters to assess the impact of phosphoenolpyruvate carboxylase levels on growth yield and deoxy-xylulose-P synthase levels on lycopene production. The multifaceted characterization of promoter strength enabled identification of optimal expression levels for ppc and dxs, which maximized the desired phenotype. Additionally, in a strain preengineered to produce lycopene, the response to deoxy-xylulose-P synthase levels was linear at all levels tested, indicative of a rate-limiting step, unlike the parental strain, which exhibited an optimum expression level, illustrating that optimal gene expression levels are variable and dependent on the genetic background of the strain. This promoter library concept is illustrated as being generalizable to eukaryotic organisms (Saccharomyces cerevisiae) and thus constitutes an integral platform for functional genomics, synthetic biology, and metabolic engineering endeavors.

Escherichia coli↗
PubMed2005

Regulation of mouse hepatic genes in response to diet induced obesity, insulin resistance and fasting induced weight reduction.

BACKGROUND: Obesity is associated with insulin resistance that can often be improved by caloric restriction and weight reduction. Although many physiological changes accompanying insulin resistance and its treatment have been characterized, the genetic mechanisms linking obesity to insulin resistance are largely unknown. We used DNA microarrays and RT-PCR to investigate significant changes in hepatic gene transcription in insulin resistant, diet-induced obese (DIO)-C57/BL/6J mice and DIO-C57/BL/6J mice fasted for 48 hours, whose weights returned to baseline levels during these conditions. RESULTS: Transcriptional profiling of hepatic mRNA revealed over 1900 genes that were significantly perturbed between control, DIO, and fasting/weight reduced DIO mice. From this set, our bioinformatics analysis identified 41 genes that rigorously discriminate these groups of mice. These genes are associated with molecular pathways involved in signal transduction, and protein metabolism and secretion. Of particular interest are genes that participate in pathways responsible for modulating insulin sensitivity. DIO altered expression of genes in directions that would be anticipated to antagonize insulin sensitivity, while fasting/weight reduction partially or completely normalized their levels. Among these discriminatory genes, Sh3kbp1 and RGS3, may have special significance. Sh3kbp1, an endogenous inhibitor of PI-3-kinase, was upregulated by high-fat feeding, but normalized to control levels by fasting/weight reduction. Because insulin signaling occurs partially through PI-3-kinase, increased expression of Sh3kbp1 by DIO mice may contribute to hepatic insulin resistance via inhibition of PI-3-kinase. RGS3, a suppressor of G-protein coupled receptor generation of cAMP, was repressed by high-fat feeding, but partially normalized by fasting/weight reduction. Decreased expression of RGS3 may augment levels of cAMP and thereby contribute to increased, cAMP-induced, hepatic glucose output via phosphoenolpyruvate carboxykinase (PCK1), whose mRNA levels were also elevated. CONCLUSION: These findings demonstrate that hepatocytes respond to DIO and weight reduction by controlling gene transcription in a variety of important molecular pathways. Future studies that characterize the physiological significance of the identified genes in modulating energy homeostasis could provide a better understanding of the mechanisms linking DIO with insulin resistance.

Journal Article↗
PubMed2005

High-throughput metabolic state analysis: the missing link in integrated functional genomics of yeasts.

The lack of comparable metabolic state assays severely limits understanding the metabolic changes caused by genetic or environmental perturbations. The present study reports the application of a novel derivatization method for metabolome analysis of yeast, coupled to data-mining software that achieve comparable throughput, effort and cost compared with DNA arrays. Our sample workup method enables simultaneous metabolite measurements throughout central carbon metabolism and amino acid biosynthesis, using a standard GC-MS platform that was optimized for this purpose. As an implementation proof-of-concept, we assayed metabolite levels in two yeast strains and two different environmental conditions in the context of metabolic pathway reconstruction. We demonstrate that these differential metabolite level data distinguish among sample types, such as typical metabolic fingerprinting or footprinting. More importantly, we demonstrate that this differential metabolite level data provides insight into specific metabolic pathways and lays the groundwork for integrated transcription-metabolism studies of yeasts.

Aerobiosis↗
PubMed2005

Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene knockout targets.

Identification of genes that affect the product accumulation phenotype of recombinant strains is an important problem in industrial strain construction and a central tenet of metabolic engineering. We have used systematic (model-based) and combinatorial (transposon-based) methods to identify gene knockout targets that increase lycopene biosynthesis in strains of Escherichia coli. We show that these two search strategies yield two distinct gene sets, which affect product synthesis either through an increase in precursor availability or through (largely unknown) kinetic or regulatory mechanisms, respectively. Exhaustive exploration of all possible combinations of the above gene sets yielded a unique set of 64 knockout strains spanning the metabolic landscape of systematic and combinatorial gene knockout targets. This included a global maximum strain exhibiting an 8.5-fold product increase over recombinant K12 wild type and a twofold increase over the engineered parental strain. These results were further validated in controlled culture conditions.

Carotenoids↗
PubMed2005

Application of genome-wide expression analysis to human health and disease.

The application of genome-wide expression analysis to a large-scale, multicentered program in critically ill patients poses a number of theoretical and technical challenges. We describe here an analytical and organizational approach to a systematic evaluation of the variance associated with genome-wide expression analysis specifically tailored to study human disease. We analyzed sources of variance in genome-wide expression analyses performed with commercial oligonucleotide arrays. In addition, variance in gene expression in human blood leukocytes caused by repeated sampling in the same subject, among different healthy subjects, among different leukocyte subpopulations, and the effect of traumatic injury, were also explored. We report that analytical variance caused by sample processing was acceptably small. Blood leukocyte gene expression in the same individual over a 24-h period was remarkably constant. In contrast, genome-wide expression varied significantly among different subjects and leukocyte subpopulations. Expectedly, traumatic injury induced dramatic changes in apparent gene expression that were greater in magnitude than the analytical noise and interindividual variance. We demonstrate that the development of a nation-wide program for gene expression analysis with careful attention to analytical details can reduce the variance in the clinical setting to a level where patterns of gene expression are informative among different healthy human subjects, and can be studied with confidence in human disease.

Cluster Analysis↗
PubMed2005

Metabolic engineering.

Metabolic engineering is a powerful methodology aimed at intelligently designing new biological pathways, systems, and ultimately phenotypes through the use of recombinant DNA technology. Built largely on the theoretical and computational analysis of chemical systems, the field has evolved to incorporate a growing number of genome scale experimental tools. This combination of rigorous analysis and quantitative molecular biology methods has endowed metabolic engineering with an effective synergism that crosses traditional disciplinary bounds. As such, there are a growing number of applications for the effective employment of metabolic engineering, ranging from the initial industrial fermentation applications to more recent medical diagnosis applications. In this review we highlight many of the contributions metabolic engineering has provided through its history, as well as give an overview of new tools and applications that promise to have a large impact on the field's future.

Biotechnology↗
PubMed2005

Strain improvement by metabolic engineering: lysine production as a case study for systems biology.

A central goal of systems biology is the elucidation of cell function and physiology through the integrated use of broad based genomic and physiological data. Such systemic approaches have been employed extensively in the past, as they are a central element of metabolic flux analysis, the distribution of kinetic control in pathways, and the key differentiating characteristic of metabolic engineering. In one case study, these tools have been applied to the improvement of lysine-producing strains of Corynebacterium glutamicum. The systematic study of the physiology of this organism allowed the identification of specific metabolic targets and subsequently led to significant improvements in product yield and productivity. This case study can serve as a guide for the development of systems biology tools for the utilization of large volumes of cell- and genome-wide transcriptional and physiological data.

Corynebacterium glutamicum↗