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Biomedical subjects

G Stephanopoulos

Publications and source records attributed to G Stephanopoulos.

At least 19 recordsLinked to original sources
PubMed2005

Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli.

The identification of genetic targets that are effective in bringing about a desired phenotype change is still an open problem. While random gene knockouts have yielded improved strains in certain cases, it is also important to seek the guidance of cell-wide stoichiometric constraints in identifying promising gene knockout targets. To investigate these issues, we undertook a genome-wide stoichiometric flux balance analysis as an aid in discovering putative genes impacting network properties and cellular phenotype. Specifically, we calculated metabolic fluxes such as to optimize growth and then scanned the genome for single and multiple gene knockouts that yield improved product yield while maintaining acceptable overall growth rate. For the particular case of lycopene biosynthesis in Escherichia coli, we identified such targets that we subsequently tested experimentally by constructing the corresponding single, double and triple gene knockouts. While such strains are suggested (by the stoichiometric calculations) to increase precursor availability, this beneficial effect may be further impacted by kinetic and regulatory effects not captured by the stoichiometric model. For the case of lycopene biosynthesis, the so identified knockout targets yielded a triple knockout construct that exhibited a nearly 40% increase over an engineered, high producing parental strain.

Carotenoids↗
PubMed2005

Genes and related proteins involved in amelogenesis imperfecta.

Dental enamel formation is a remarkable example of a biomineralization process. The exact mechanisms involved in this process remain partly obscure. Some of the genes encoding specific enamel proteins have been indicated as candidate genes for amelogenesis imperfecta. Mutational analyses within studied families have supported this hypothesis. Mutations in the amelogenin gene (AMELX) cause X-linked amelogenesis imperfecta, while mutations in the enamelin gene (ENAM) cause autosomal-inherited forms of amelogenesis imperfecta. Recent reports involve kallikrein-4 (KLK4), MMP-20, and DLX3 genes in the etiologies of some cases. This paper focuses mainly on the candidate genes involved in amelogenesis imperfecta and the proteins derived from them, and reviews current knowledge on their structure, localization within the tissue, and correlation with the various types of this disorder.

Amelogenesis Imperfecta↗
PubMed2004

Metabolic flux control analysis of branch points: an improved approach to obtain flux control coefficients from large perturbation data.

An overview of published approaches for the metabolic flux control analysis of branch points revealed that often not all fundamental constraints on the flux control coefficients have been taken into account. This has led to contradictory statements in literature on the minimum number of large perturbation experiments required to estimate the complete set of flux control coefficients C(J) for a metabolic branch point. An improved calculation procedure, based on approximate Lin-log reaction kinetics, is proposed, providing explicit analytical solutions of steady state fluxes and metabolite concentrations as a function of large changes in enzyme levels. The obtained solutions allow direct calculation of elasticity ratios from experimental data and subsequently all C(J)-values from the unique relation between elasticity ratio's and flux control coefficients. This procedure ensures that the obtained C(J)-values satisfy all fundamental constraints. From these it follows that for a three enzyme branch point only one characterised or two uncharacterised large flux perturbations are sufficient to obtain all C(J)- values. The improved calculation procedure is illustrated with four experimental cases.

Algorithms↗
PubMed2002

Metabolic flux redistribution in Corynebacterium glutamicum in response to osmotic stress.

Osmotic stress constitutes a major bacterial stress factor in the soil and during industrial fermentation. In this paper, we quantified the metabolic response, in terms of metabolic flux redistribution, of a lysine-overproducing strain of Corynebacterium glutamicum grown under continuous culture, to gradually increasing osmolality. Oxygen and carbon dioxide evolution rates, and the changes in concentration of extracellular, as well as intracellular, metabolites were measured throughout the osmotic gradient. The metabolic fluxes were estimated from these measurements and from the mass balance constraints at each metabolite-node of the assumed metabolic reaction network. Our results show that formation rates of compatible solutes--trehalose first and proline at a later stage of the gradient--increased with osmotic stress to equilibrate the external osmotic pressure. Estimated flux distributions indicate that the observed increase in the glucose specific uptake rate with osmotic stress is channeled through the main energy generating pathways-- glycolysis and the tricarboxylic acid cycle--while the flux through the pentose phosphate pathway remains constant throughout the gradient. This results in a significant increase in the net specific ATP production rate, which may possibly be used to support the higher energy requirements required for cellular maintenance at high osmolalities. Finally, nodal analysis confirmed that the PEP/pyruvate node is essentially rigid and that the glucose-6-phosphate, oxaloacetate and alpha-ketoglutarate nodes are flexible and therefore adaptable to changes in osmotic pressure in C. glutamicum.

Adenosine Triphosphate↗
PubMed2002

Genome-wide screening for trait conferring genes using DNA microarrays.

We report a DNA microarray-based method for genome-wide monitoring of competitively grown transformants to identify genes whose overexpression confers a specific cellular phenotype. Whereas transcriptional profiling identifies differentially expressed genes that are correlated with particular aspects of the cellular phenotype, this functional genomics approach determines genes that result in a specific physiology. This parallel gene-trait mapping method consists of transforming a strain with a genomic library, enriching the cell population in transformants containing the trait conferring gene(s), and finally using DNA microarrays to simultaneously isolate and identify the enriched gene inserts. Various methods of enrichment can be used; here, genes conferring low-level antibiotic resistance were identified by growth in selective media. We demonstrated the method by transforming Escherichia coli cells with a genomic E. coli library and selecting for transformants exhibiting a growth advantage in the presence of the anti-microbial agent Pine-Sol. Genes conferring Pine-Sol tolerance (19 genes) or sensitivity (27 genes) were identified by hybridizing, on DNA microarrays containing 1,160 E. coli gene probes, extra-chromosomal DNA isolated from transformed cells grown in the presence of various levels of Pine-Sol. Results were further validated by plating and sequencing of individual colonies, and also by assessing the Pine-Sol resistance of cells transformed with enriched plasmid library or individual resistance genes identified by the microarrays. Applications of this method beyond antibiotic resistance include identification of genes resulting in resistance to chemotherapeutic agents, genes yielding resistance to toxic products (recombinant proteins, chemical feedstocks) in industrial fermentations, genes providing enhanced growth in cell culture or high cell density fermentations, genes facilitating growth on unconventional substrates, and others.

Chromosome Mapping↗
PubMed2001

A compendium of gene expression in normal human tissues.

This study creates a compendium of gene expression in normal human tissues suitable as a reference for defining basic organ systems biology. Using oligonucleotide microarrays, we analyze 59 samples representing 19 distinct tissue types. Of approximately 7,000 genes analyzed, 451 genes are expressed in all tissue types and designated as housekeeping genes. These genes display significant variation in expression levels among tissues and are sufficient for discerning tissue-specific expression signatures, indicative of fundamental differences in biochemical processes. In addition, subsets of tissue-selective genes are identified that define key biological processes characterizing each organ. This compendium highlights similarities and differences among organ systems and different individuals and also provides a publicly available resource (Human Gene Expression Index, the HuGE Index, http://www.hugeindex.org) for future studies of pathophysiology.

Cluster Analysis↗
PubMed2001

Rapamycin reduces hybridoma cell death and enhances monoclonal antibody production.

Rapamycin was used as a medium additive to slow the progression of CRL 1606 hybridomas through the cell cycle, under the hypothesis that such a modulation might reduce cell death. Cell cycle distributions for CRL hybridomas in the G1 phase of the cell cycle ranged from 20% to 35% during batch, fed-batch, and continuous culture experiments, independent of culture time, dilution rate, growth rates, or death rates. Rapamycin, an mTOR signaling inhibitor, immunosuppressant, and G1-phase arresting agent, was identified and tested for efficacy in restraining cell cycle progression in CRL 1606 hybridoma cultures. However, in the presence of 100 nM rapamycin, the percentage of cells in the G1 phase of the cell cycle during fed-batch cultures was only increased from 28% to 31% in control cultures to 37% to 48% for those with rapamycin. Accordingly, rapamycin only slightly reduced culture growth rate. Instead, the use of rapamycin more notably kept viability higher than that of control cultures by delaying cell death for 48 h, thereby enabling viable proliferation to higher maximum viable cell densities. Furthermore, rapamycin enhanced specific monoclonal antibody production by up to 100% during high-viability growth. Thus, over the course of 6-day fed-batch cultivations, the beneficial effects of rapamycin on viable cell density and specific productivity resulted in an increase in final monoclonal antibody titer from 0.25 to 0.56 g/L (124%). As rapamycin is reported to influence a much broader range of cellular functions than cell cycle alone, these findings are more illustrative of the influence that signal transduction pathways related to mTOR can have on overall cell physiology and culture productivity.

Animals↗
PubMed2001

After a decade of progress, an expanded role for metabolic engineering.

Over the past decade, metabolic engineering has emerged as an active and distinct discipline characterized by its over-arching emphasis on integration. In practice, metabolic engineering is the directed improvement of cellular properties through the application of modern genetic methods. Although it was applied on an ad hoc basis for several years following the introduction of recombinant techniques [1,2], metabolic engineering was formally defined as a new field approximately a decade ago [3]. Since that time, many creative applications, directed primarily to metabolite overproduction, have been reported [4]. In parallel, recent advances in the resolution and acquisition time of biological data, especially structural and functional genomics, has amplified interest in the systemic view of biology that metabolic engineering provides. To facilitate the burgeoning scientific exchange in this area on a more regular and convenient basis, a new conference series was launched in 1996 followed by a new journal in 1999.

Base Sequence↗
PubMed2001

Metabolic engineering of indene bioconversion in Rhodococcus sp.

We have applied the methodology of metabolic engineering in the investigation of the enzymatic bioreaction network in Rhodococcus sp. that catalyzes the bioconversion of indene to (2R)-indandiol suitable for the synthesis of cis-1-amino-2-indanol, a precursor of the HIV protease inhibitor, Crixivan. A chemostat with a novel indene air delivery system was developed to facilitate the study of steady state physiology of Rhodococcus sp. 124. Prolonged cultivation of this organism in a continuous flow system led to the evolution of a mutant strain, designated KY1, with improved bioconversion properties, in particular a twofold increase in yield of (2R)-indandiol relative to 124. Induction studies with both strains indicated that KY1 lacked a toluene-inducible dioxygenase activity present in 124 and responsible for the formation of undesired byproducts. Flux analysis of indene bioconversion in KY1 performed using steady state metabolite balancing and labeling with [14C]-tracers revealed that at least 94% of the indene is oxidized by a monooxygenase to indan oxide that is subsequently hydrolyzed to trans-(1R,2R)-indandiol and cis-(1S,2R)-indandiol. This analysis identified several targets in KY1 for increasing (2R)-indandiol product yield. Most promising among them is the selective hydrolysis of indan oxide to trans-(1R,2R)-indandiol through expression of an epoxide hydrolase or modification of culture conditions.

Catalysis↗
PubMed2001

Metabolic engineering as an integrating platform for strain development.

Integration of the analytical framework and experimental tools of metabolic engineering with emerging technologies such as DNA microarrays and directed evolution stands to dramatically improve the approaches by which strain improvement and biocatalyst design are pursued in the future. Progress in genomics and applied molecular biology, together with increasing emphasis on renewable resource utilization for chemical production, has advanced metabolic engineering to the forefront of biotechnological interest.

Industrial Microbiology↗
PubMed2001

Application of radiolabeled tracers to biocatalytic flux analysis.

Radiolabeled tracers can provide valuable information about the structure of and flux distributions in biocatalytic reaction networks. This method derives from prior studies of glucose metabolism in mammalian systems and is implemented by pulsing a culture with a radiolabeled metabolite that can be transported into the cells and subsequently measuring the radioactivity of all network metabolites following separation by liquid chromatography. Intracellular fluxes can be directly determined from the transient radioactivity count data by tracking the depletion of the radiolabeled metabolite and/or the accompanying accumulation of any products formed. This technique differs from previous methods in that it is applied within a systems approach to the problem of flux determination. It has been used for the investigation of the indene bioconversion network expressed in Rhodococcus sp. KY1. Flux estimates obtained by radioactive tracers were confirmed by macroscopic metabolite balancing and showed that indene oxidation in steady state chemostat cultures proceeds primarily through a monooxygenase activity forming (1S,2R)-indan oxide, with no dehydrogenation of trans-(1R,2R)-indandiol. These results confirmed the significance of indan oxide formation and identified the hydrolysis of indan oxide as a key step in maximizing the production of (2R)-indandiol, a chiral precursor of the HIV protease inhibitor, Crixivan.

Carbon Radioisotopes↗
PubMed2000

Effect of insulin stimulation on the proliferation and death of Chinese hamster ovary cells.

The effect of environmental and genetic parameters on cell death was studied in Chinese hamster ovary cell cultures. Experiments were performed using an anchorage-dependent CHO cell line expressing gamma-IFN, and a second cell line obtained by transfection of the previous one with bcl-2. In serum-free medium the two cell lines showed a considerable degree of growth control entering quiescence while maintaining high viabilities. The addition of transferrin did not have any effect but insulin addition allowed cells arrested by serum withdrawal to reenter the cell cycle. However, insulin supplementation also resulted in cell death, which was possible to avoid through bcl-2 overexpression or in the presence of serum. We propose that the expression of c-myc, shown to be induced by insulin, plays an important role in the cell death observed after insulin addition in an inappropriate environment, deficient in protective factors. This hypothesis is supported by measurements of c-myc expression and cell cycle distribution.

Animals↗
PubMed2000

Metabolic flux distributions in Corynebacterium glutamicum during growth and lysine overproduction. Reprinted from Biotechnology and Bioengineering, Vol. 41, Pp 633-646 (1993).

The two main contributions of this article are the solidification of Corynebacterium glutamicum biochemistry guided by bioreaction network analysis, and the determination of basal metabolic flux distributions during growth and lysine synthesis. Employed methodology makes use of stoichiometrically based mass balances to determine flux distributions in the C. glutamicum metabolic network. Presented are a brief description of the methodology, a thorough literature review of glutamic acid bacteria biochemistry, and specific results obtained through a combination of fermentation studies and analysis-directed intracellular assays. The latter include the findings of the lack of activity of glyoxylate shunt, and that phosphoenolpyruvate carboxylase (PPC) is the only anaplerotic reaction expressed in C. glutamicum cultivated on glucose minimal media. Network simplifications afforded by the above findings facilitated the determination of metabolic flux distributions under a variety of culture conditions and led to the following conclusions. Both the pentose phosphate pathway and PPC support significant fluxes during growth and lysine overproduction, and that flux partitioning at the glucosa-6-phosphate branch point does not appear to limit lysine synthesis.

Corynebacterium↗
PubMed2000

Mining of biological data I: identifying discriminating features via mean hypothesis testing.

Large volumes of data are routinely collected during bioprocess operations and, more recently, in basic biological research using genomics-based technologies. While these data often lack sufficient detail to be used for mechanism identification, it is possible that the underlying mechanisms affecting cell phenotype or process outcome are reflected as specific patterns in the overall or temporal sensor logs. This raises the possibility of identifying outcome-specific fingerprints that can be used for process or phenotype classification and the identification of discriminating characteristics, such as specific genes or process variables. The aim of this work is to provide a systematic approach to identifying and modeling patterns in historical records and using this information for process classification. This approach differs from others in that emphasis is placed on analyzing the data structure first and thereby extracting potentially relevant features prior to model creation. The initial step in this overall approach is to first identify the discriminating features of the relevant measurements and time windows, which can then be subsequently used to discriminate among different classes of process behavior. This is achieved via a mean hypothesis testing algorithm. Next, the homogeneity of the multivariate data in each class is explored via a novel cluster analysis technique called PC1 Time Series Clustering to ensure that the data subsets used accurately reflect the variability displayed in the historical records. This will be the topic of the second paper in this series. We present here the method for identifying discriminating features in data via mean hypothesis testing along with results from the analysis of case studies from industrial fermentations

Algorithms↗
PubMed2000

Mining of biological data II: assessing data structure and class homogeneity by cluster analysis.

An important step in data analysis is class assignment which is usually done on the basis of a macroscopic phenotypic or bioprocess characteristic, such as high vs low growth, healthy vs diseased state, or high vs. low productivity. Unfortunately, such an assignment may lump together samples, which when derived from a more detailed phenotypic or bioprocess description are dissimilar, giving rise to models of lower quality and predictive power. In this paper we present a clustering algorithm for data preprocessing which involves the identification of fundamentally similar lots on the basis of the extent of similarity among the system variables. The algorithm combines aspects of cluster analysis and principal component analysis by applying agglomerative clustering methods to the first principal component of the system data matrix. As part of a rational strategy for developing empirical models, this technique selects lots (samples) which are most appropriate for inclusion in a training set by analyzing multivariate data homogeneity. Samples with similar data structures are identified and grouped together into distinct clusters. This knowledge is used in the formation of potential training sets. Additionally, this technique can identify atypical lots, i.e., samples that are not simply outliers but exhibit the general properties of one class but have been given the assignment of the other. The method is presented along with examples from its application to fermentation data sets.

Algorithms↗
PubMed2000

Targeted, PCR-based gene disruption in cyanobacteria: inactivation of the polyhydroxyalkanoic acid synthase genes in Synechocystis sp. PCC6803.

A PCR-based method is described for the efficient construction of targeted gene disruptions and gene fusions in the cyanobacterium Synechocystis sp. PCC6803. In a simple two-step PCR approach, a gene conversion cassette was synthesized targeting the polyhydroxyalkanoic acid (PHA) synthase genes. Upon transformation, PHA production in Synechocystis under normal as well as high production culture conditions was undetectable. The application of this method to the genetic inactivation of the phaE-C(Syn) gene cluster demonstrates its potential for genetic engineering of cyanobacteria and the study of functional genomics in Synechocystis.

Acyltransferases↗