The intelligent design of evolution.
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Directed Molecular Evolution↗
Biomedical subjects
Gregory N Stephanopoulos
Publications and source records attributed to Gregory N Stephanopoulos.
High-throughput screen for poly-3-hydroxybutyrate in Escherichia coli and Synechocystis sp. strain PCC6803.
A novel, quantitative method for detecting poly-3-hydroxybutyrate (PHB) amounts in viable cells was developed to allow for high-throughput screening of mutant libraries. The staining technique was demonstrated and optimized for the cyanobacterium Synechocystis sp. strain PCC6803 and the eubacterium Escherichia coli to maximize the fluorescence difference between PHB-accumulating and control cells by flow cytometry. In Synechocystis, the level of nonspecific dye binding was reduced by using nonionic stain buffer that allowed quantitation of fluorescence levels. In E. coli, the use of a mild sucrose shock facilitated uptake of Nile red without significant loss of viability. The optimized staining protocols yielded a linear response for the mean fluorescence against (chemically measured) PHB. The staining protocols are novel methods useful in the high-throughput evaluation of combinatorial libraries of Synechocystis and E. coli using fluorescence-activated cell sorting to identify mutants with increased PHB-accumulating properties.
Escherichia coli↗
A generic motif discovery algorithm for sequential data.
MOTIVATION: Motif discovery in sequential data is a problem of great interest and with many applications. However, previous methods have been unable to combine exhaustive search with complex motif representations and are each typically only applicable to a certain class of problems. RESULTS: Here we present a generic motif discovery algorithm (Gemoda) for sequential data. Gemoda can be applied to any dataset with a sequential character, including both categorical and real-valued data. As we show, Gemoda deterministically discovers motifs that are maximal in composition and length. As well, the algorithm allows any choice of similarity metric for finding motifs. Finally, Gemoda's output motifs are representation-agnostic: they can be represented using regular expressions, position weight matrices or any number of other models for any type of sequential data. We demonstrate a number of applications of the algorithm, including the discovery of motifs in amino acids sequences, a new solution to the (l,d)-motif problem in DNA sequences and the discovery of conserved protein substructures. AVAILABILITY: Gemoda is freely available at http://web.mit.edu/bamel/gemoda
Algorithms↗
Identification of optimal classification functions for biological sample and state discrimination from metabolic profiling data.
MOTIVATIONS: Classification of biological samples for diagnostic purposes is a difficult task because of the many decisions involved on the number, type and functional manipulations of the input variables. This study presents a generally applicable strategy for systematic formulation of optimal diagnostic indexes. To this end, we develop a novel set of computational tools by integrating regression optimization, stepwise variable selection and cross-validation algorithms. RESULTS: The proposed discrimination methodology was applied to plasma and tissue (liver) metabolic profiling data describing the time progression of liver dysfunction in a rat model of acute hepatic failure generated by d-galactosamine (GalN) injection. From the plasma data, our methodology identified seven (out of a total of 23) metabolites, and the corresponding transform functions, as the best inputs to the optimal diagnostic index. This index showed better time resolution and increased noise robustness compared with an existing metabolic index, Fischer's BCAA/AAA molar ratio, as well as indexes generated using other commonly used discriminant analysis tools. Comparison of plasma and liver indexes found two consensus metabolites, lactate and glucose, which implicate glycolysis and/or gluconeogenesis in mediating the metabolic effects of GalN.
Algorithms↗
Metabolic and transcriptional patterns accompanying glutamine depletion and repletion in mouse hepatoma cells: a model for physiological regulatory networks.
An important objective in postgenomic biology is to link gene expression to function by developing physiological networks that include data from the genomic and functional levels. Here, we develop a model for the analysis of time-dependent changes in metabolites, fluxes, and gene expression in a hepatic model system. The experimental framework chosen was modulation of extracellular glutamine in confluent cultures of mouse Hepa1-6 cells. The importance of glutamine has been demonstrated previously in mammalian cell culture by precipitating metabolic shifts with glutamine depletion and repletion. Our protocol removed glutamine from the medium for 24 h and returned it for a second 24 h. Flux assays of glycolysis, the tricarboxylic acid (TCA) cycle, and lipogenesis were used at specified intervals. All of these fluxes declined in the absence of glutamine and were restored when glutamine was repleted. Isotopomer spectral analysis identified glucose and glutamine as equal sources of lipogenic carbon. Metabolite measurements of organic acids and amino acids indicated that most metabolites changed in parallel with the fluxes. Experiments with actinomycin D indicated that de novo mRNA synthesis was required for observed flux changes during the depletion/repletion of glutamine. Analysis of gene expression data from DNA microarrays revealed that many more genes were anticorrelated with the glycolytic flux and glutamine level than were correlated with these indicators. In conclusion, this model may be useful as a prototype physiological regulatory network where gene expression profiles are analyzed in concert with changes in cell function.
Animals↗
An extension and novel solution to the (l,d)-motif challenge problem.
The (l,d)-motif challenge problem, as introduced by Pevzner and Sze, is a mathematical abstraction of the DNA functional site discovery task. Here we expand the (l,d)-motif problem to more accurately model this task and present a novel algorithm to solve this extended problem. This algorithm is guaranteed to find all (l,d)-motifs in a set of input sequences with unbounded support and length. We demonstrate the performance of the algorithm on publicly available datasets and show that the algorithm deterministically enumerates the optimal motifs.
Algorithms↗
Profiling of dynamic changes in hypermetabolic livers.
The liver plays an important role in the overall negative nitrogen balance leading to muscle wasting commonly observed in patients following many conditions, including severe injury, cancer, and diabetes. In order to study changes in liver metabolism during the establishment of such catabolic states, we used a rat skin burn injury model that induces hypermetabolism and muscle wasting. At various times during the first week following the injury, livers were isolated and perfused in a recirculating system under well-defined conditions. We applied a steady-state metabolic flux analysis model of liver metabolism and then used k-means clustering to objectively group together reaction flux time profiles. We identified six distinct groups of reactions that were differentially responsive: (1) pentose phosphate pathway (PPP); (2) amino acid oxidation reactions leading to the formation of tricarboxylic acid (TCA) cycle intermediates; (3) gluconeogenesis; (4) TCA-cycle and mitochondrial oxidation; (5) lipolysis, beta-oxidation, and ketone body formation; and (6) urea-cycle. Burn injury sequentially upregulated the urea-cycle, the PPP, and the TCA-cycle, in order, while beta-oxidation and gluconeogenesis remained unchanged. The upregulation of the PPP was transient, whereas the rise in urea- and TCA-cycle fluxes was sustained. An ATP balance predicted an increased production of ATP and energy expenditure starting on day 3 post-burn, which correlated with the induction of the oxidative phosphorylation uncoupler uncoupling protein-2. We conclude that metabolic profiling using flux analysis and clustering analysis is a useful methodology to characterize the differential activation of metabolic pathways in perfused organs and to identify specific key pathways that are sensitive to a stimulus or insult without making a priori assumptions.
Adaptation, Physiological↗
Induction of a hypermetabolic state in cultured hepatocytes by glucagon and H2O2.
Stress hormones and pro-inflammatory cytokines are putative signals triggering increased energy expenditure or "hypermetabolism" commonly observed in inflammatory states. Cytokines also cause the release of reactive oxidants by immune cells resident in tissues in vivo. Therefore, we hypothesized that oxidative stress plays a role in the induction of hypermetabolism. We examined the effect of glucagon (1.0 nM), a catabolic stress hormone, and the oxidant H(2)O(2) (1.0 mM) on the metabolism of stable hepatocyte cultures for 4 days. Combined H(2)O(2) and glucagon treatment, but not H(2)O(2) or glucagon used alone, increased the hepatocyte oxygen uptake rate 25% above control untreated cells after a lag-time of 72 h. The same treatment also increased the expression of mitochondrial uncoupling protein-2 (UCP2). These effects were significantly inhibited by the antioxidant N-acetylcysteine (5mM) and the pentose phosphate pathway (PPP) inhibitor dehydroepianderosterone (200 microM). Glucagon alone induced urea synthesis and H(2)O(2) alone induced the PPP. These findings show, for the first time, that oxidative stress, in combination with glucagon, increases metabolic energy expenditure in cultured cells, and that this effect may be mediated by UCP-2. Furthermore, the results implicate the PPP in the induction of the hypermetabolic response.
Animals↗
Thermodynamic and kinetic characterization of antisense oligodeoxynucleotide binding to a structured mRNA.
Antisense oligonucleotides act as exogenous inhibitors of gene expression by binding to a complementary sequence on the target mRNA, preventing translation into protein. Antisense technology is being applied successfully as a research tool and as a molecular therapeutic. However, a quantitative understanding of binding energetics between short oligonucleotides and longer mRNA targets is lacking, and selecting a high-affinity antisense oligonucleotide sequence from the many possibilities complementary to a particular RNA is a critical step in designing an effective antisense inhibitor. Here, we report measurements of the thermodynamics and kinetics of hybridization for a number of oligodeoxynucleotides (ODNs) complementary to the rabbit beta-globin (RBG) mRNA using a binding assay that facilitates rapid separation of bound from free species in solution. A wide range of equilibrium dissociation constants were observed, and association rate constants within the measurable range correlated strongly with binding affinity. In addition, a significant correlation was observed of measured binding affinities with binding affinity values predicted using a thermodynamic model involving DNA and RNA unfolding, ODN hybridization, and RNA restructuring to a final free energy minimum. In contrast to the behavior observed for hybridization of short strands, the association rate constant increased with temperature, suggesting that the kinetics of association are related to disrupting the native structure of the target RNA. The rate of cleavage of the RBG mRNA in the presence of ribonuclease H and ODNs of varying association kinetics displayed apparent first-order kinetics, with the rate constant exhibiting binding-limited behavior at low association rates and reaction-limited behavior at higher rates. Implications for the rational design of effective antisense reagents are discussed.
Base Sequence↗
Application of multivariate analysis to optimize function of cultured hepatocytes.
Understanding the metabolic and regulatory pathways of hepatocytes is important for biotechnological applications involving liver cells, including the development of bioartificial liver (BAL) devices. To characterize intermediary metabolism in the hepatocytes, metabolic flux analysis (MFA) was applied to elucidate the changes in intracellular pathway fluxes of primary rat hepatocytes exposed to human plasma and to provide a comprehensive snapshot of the hepatic metabolic profile. In the current study, the combination of preconditioning and plasma supplementation produced distinct metabolic states. Combining the metabolic flux distribution obtained by MFA with methodologies such as Fisher discriminant analysis (FDA) and partial least squares or projection to latent structures (PLS) provided insights into the underlying structure and causal relationship within the data. With the aid of these analyses, patterns in the cellular response of the hepatocytes that contributed to the separation of the different hepatic states were identified. Of particular interest was the recognition of distal pathways that strongly correlated with a particular hepatic function. The hepatic functions investigated were intracellular triglyceride accumulation and urea production. This study illustrates a framework for optimizing hepatic function and a possibility of identifying potential targets for improving hepatic functions.
Algorithms↗