High-yield continuous production of nicotinic acid via nitrile hydratase-amidase cascade reactions using cascade CSMRs

High yields of nicotinic acid from 3-cyanopyridine bioconversion were obtained by exploiting the in situ nitrile hydratase-amidase enzymatic cascade system of Microbacterium imperiale CBS 498-74. Experiments were carried out in continuously stirred tank UF-membrane bioreactors (CSMRs) arranged in series. This reactor configuration enables both enzymes, involved in the cascade reaction, to work with optimized kinetics, without any purification, exploiting their differing temperature dependences. To this end, the first CSMR, optimized for the properties of the NHase, was operated (i) at low temperature (5°C), limiting inactivation of the more fragile enzyme, nitrile hydratase, (ii) with a high residence time (24 h) to overcome reaction rate limitation. The second CSMR, optimized for the properties of the AMase, was operated (i) at a higher temperature (50°C), (ii) with a lower residence time (6h), and (iii) with a lower substrate (3-cyanopyridine) concentration to control excess substrate inhibition. The appropriate choice of operational conditions enabled total conversion of 3-cyanpyridine (up to 200 mM) into nicotinic acid to be achieved at steady-state and for long periods. Higher substrate concentrations required two CSMRs optimized for the properties of the NHase arranged in series to drive the first reaction to completion.     

Kinetic analysis of enzyme reactions with slow-binding inhibition.

In this paper we present a general kinetic study of slow-binding inhibition processes, i.e. enzyme reactions that do not respond instantly to the presence of a competitive inhibitor. The analysis that we present is based on the equation that describes the formation of products with time in each case on the experimental progress curve. It is carried out under the condition of limiting enzyme concentration and allows the discrimination between the different cases of slow-binding inhibition. The mechanism in which the formation of complex enzyme-inhibitor is a single or two slow steps or follow a rapid equilibrium, has been considered. The corresponding explicit equations of each case have been obtained and checked by numerical integration. A kinetic data analysis to evaluate the corresponding kinetic parameters is suggested. We illustrate the method, numerically by computer simulation, of the reaction and present some numerical examples that demonstrate the applicability of our procedure.     

Gene expression data analysis using a novel approach to biclustering combining discrete and continuous data

Many different methods exist for pattern detection in gene expression data. In contrast to classical methods, biclustering has the ability to cluster a group of genes together with a group of conditions (replicates, set of patients or drug compounds). However, since the problem is NP-complex, most algorithms use heuristic search functions and therefore might converge towards local maxima. By using the results of biclustering on discrete data as a starting point for a local search function on continuous data, our algorithm avoids the problem of heuristic initialization. Similar to OPSM, our algorithm aims to detect biclusters whose rows and columns can be ordered such that row values are growing across the bicluster's columns and vice-versa. Results have been generated on the yeast genome (Saccharomyces cerevisiae), a human cancer dataset and random data. Results on the yeast genome showed that 89% of the one hundred biggest non-overlapping biclusters were enriched with Gene Ontology annotations. A comparison with OPSM and ISA demonstrated a better efficiency when using gene and condition orders. We present results on random and real datasets that show the ability of our algorithm to capture statistically significant and biologically relevant biclusters.     

Affinity analysis of non-steady-state data obtained under mass transport limited conditions using BIAcore technology

Binding data obtained with Biacore instrumentation is often evaluated using a kinetic transport model where reaction rate constants and a mass transport coefficient are used to describe the interaction. Here the use of a simplified model, an affinity transport model, for determination of the affinity (K(D)) but not the kinetics (k(a), k(d)) has been investigated. When binding rates were highly governed by mass transport effects the two models returned the same affinity and gave similar residuals, but k(a) and k(d) values found with the kinetic transport model were unreliable. On the other hand the affinity transport model failed to describe the data when binding curves were less influenced by mass transport effects. Under such circumstances the kinetic transport model returned correct k(a) and k(d) values. Depending on the outcome of the analysis the affinity transport model can therefore be used to reduce uncertainties of the kinetic parameters or as an easy way to determine K(D) values from non-steady-state data. The use of the affinity transport model is illustrated with simulated data and with binding data obtained for the interaction between a 439 Da thrombin inhibitor and immobilized thrombin. Since it is more difficult to resolve high k(a) values for low molecular weight analytes, the affinity transport model may be particularly useful for affinity analysis involving fast reactions between such analytes and immobilized protein targets.     

Optimal temperature and concentration profiles in a cascade of CSTR's performing Michaelis-Menten reactions with first order enzyme deactivation

A necessary condition is found for the intermediate temperatures and substrate concentrations in a series of CSTR's performing an enzyme-catalyzed reaction which leads to the minimum overall volume of the cascade for given initial and final temperatures and substrate concentrations. The reaction is assumed to occur in a single phase under steady state conditions. The common case of Michaelis-Menten kinetics coupled with first order deactivation of the enzyme is considered. This analysis shows that intermediate stream temperatures play as important a role as intermediate substrate concentrations when optimizing in the presence of nonisothermal conditions. The general procedure is applied to a practical example involving a series of two reactors with reasonable values for the relevant five operating parameters. These parameters are defined as dimensionless ratios involving activation energies (or enthalpy changes of reaction), preexponential factors, and initial temperature and substrate concentration. For negligible rate of deactivation, the qptimality condition corresponds to having the ratio of any two consecutive concentrations as a single-parameter increasing function of the previous ratio of consecutive concentrations.List of Symbols C _E,0 mol.m^–3 Initial concentration of active enzyme - C _E,i mol.m^–3 Concentration of active enzyme at the outlet of the i-th reactor - C _S,0 mol.m^–3 Initial concentration of substrate - C _S,i mol.m^–3 Concentration of substrate at the outlet of the i-th reactor - Da _i Damköhler number associated with the i-th reactor ((V _i.k_v,0.C_E,0)/(Q.C_S,0)) - Da _min Minimum value of the overall Damköhler number - Da _tot Overall Damköhler number - E _d J.mol^–1 Activation energy of the step of deactivation of the enzyme - E _m J.mol^–1 Standard enthalpy change of the step of binding of substrate to the enzyme - E _v J.mol^–1 Activation energy of the step of enzymatic transformation of substrate - i Integer variable - j Dummy integer variable - k Dummy integer variable - k _d,i s^–1 Kinetic constant associated with the deactivation of enzyme in the i-th reactor (k _d,o·exp{–E _d/(R.T _i}) - k _d,0 s^–1 Preexponential factor of the kinetic constant associated with the deactivation of the enzyme - K _m,i mol.m^–3 Equilibrium constant associated with the binding of substrate to the enzyme in the i-th reactor, (k _m,o·exp{–E _m}(R.T _i}) - K _m,0 mol.m^–3 Preexponential factor of the Michaelis-Menten constant associated with the binding of substrate to the enzyme - k _v,i s^–1 Kinetic constant associated with the transformation of the substrate by the enzyme in the i-th reactor (k _v,o·exp{–E _v/(R.T _i})) - k _v,0 s^–1 Preexponential factor of the kinetic constant associated with the transformation of the substrate by the enzyme - N Number of reactors in the series - Q m³.s^–1 Volumetric flow rate of reacting liquid through the reactor network - R J.K^–1.mol^–1 Ideal gas constant - T _i K Absolute temperature at the outlet of the i-th reactor - T ₀ K Initial absolute temperature - V _i m³ Volume of the i-th reactor - v _max mol.m^–3.s^–1 Maximum rate of reaction under saturation conditions of substrate - x _i Normalized concentration of substrate (C_S,i/C_{S, 0}) - x _i,opt Optimum value of the normalized concentration of substrate - y _i Dimensionless temperature (exp{–T ₀/T _i}) - y _i,opt Optimum value of the dimensionless temperature Greek Symbols Dimensionless preexponential factor associated with the Michaelis-Menten constant (K _m,0/C_s,0) - Dimensionless activation energy of the step of enzymatic transformation of substrate (E _v/R.T₀)) - Dimensionless standard enthalpy change of the step of binding of substrate to the enzyme (E _m/(R.T₀)) - Dimensionless activation energy of the step of deactivation of the enzyme (E _d/(R.T₀)) - Dimensionless deactivation preexponential factor ((k _d,0.C_S,0)/(k_v,0.C_E,0)     

Molecular mechanism of pyruvate-ferredoxin oxidoreductases based on data obtained with the Clostridium pasteurianum enzyme

Pyruvate-ferredoxin oxidoreductase oxidises pyruvate in many fermentative microorganisms. The enzyme from Clostridium pasteurianum is an air-sensitive homodimer of 2×120000 daltons, for which pyruvate is the best substrate found among several -ketoacids. Each subunit contains eight iron atoms in two [4Fe---4S] clusters. Two distinct EPR signals, possibly associated with two ligand environments, arise from one of these clusters. Binding of pyruvate does not generate a radical. The results reported suggest a scheme for the electron flow in pyruvate ferredoxin oxidoreductases according to which the detailed reaction mechanism depends on the number (even or odd) of [4Fe---4S] clusters present in a given enzyme.     

Variogram analysis of the spatial genetic structure of continuous populations using multilocus microsatellite data

A geostatistical perspective on spatial genetic structure may explain methodological issues of quantifying spatial genetic structure and suggest new approaches to addressing them. We use a variogram approach to (i) derive a spatial partitioning of molecular variance, gene diversity, and genotypic diversity for microsatellite data under the infinite allele model (IAM) and the stepwise mutation model (SMM), (ii) develop a weighting of sampling units to reflect ploidy levels or multiple sampling of genets, and (iii) show how variograms summarize the spatial genetic structure within a population under isolation-by-distance. The methods are illustrated with data from a population of the epiphytic lichen Lobaria pulmonaria, using six microsatellite markers. Variogram-based analysis not only avoids bias due to the underestimation of population variance in the presence of spatial autocorrelation, but also provides estimates of population genetic diversity and the degree and extent of spatial genetic structure accounting for autocorrelation.     

A slide rule for deriving the rate equations of enzyme catalysed reactions with unbranched mechanisms

It has been proposed that mRNA stability in Escherichia coli is enhanced by association with ribosomes and that failure of ribosome initiation into polysomes results in message inactivation. This hypothesis is examined with the aid of a simple steady queuing model from which mRNA lifetimes and other cell parameters may be calculated. Agreement with experimentally determined lifetimes is good.     

Application of theoretical considerations to the analysis of ELISA data

Solid-phase immunoassays such as the ELISA are in routine use in many areas of biological research. Data from these assays are analyzed in a variety of ways, frequently without taking into account the immunochemical principles of the assay. The Reference Standard Method is often used and is suitable and convenient for obtaining concentration (or activity) values from the antigen-specific ELISA or spRIA, sandwich assays, and inhibition assays. The standard curve required for this method may be obtained by simple linear regression analysis of logarithmic or logitlogarithmic transformed data obtained from titration of the reference standard. The shape of the logarithmic plot of the reference standard provides information on the performance of the assay. Examining data from multiple dilutions of the samples is essential to assure that each titrates with the same slope as does the reference standard; the analysis routine must permit this comparison to be made. ELISANALYSIS is a program for the IBM PC which was developed to perform such analyses. It is presented here as a model, with sufficient information provided for the development of similar analytical routines by interested users. This approach to ELISA data analysis is presented as an alternative to complicated empirical curve-fitting systems and simple endpoint methods, which can be immunochemically misleading or, in some cases, even invalid. The consistent use of the described routines would encourage greater uniformity in the means of data interpretation and thereby enhance our understanding of immunobiology.     

Use of an enzyme thermistor in continuous measurements and enzyme reactor control.

The enzyme thermistor measures the heat produced by the action of an immobilized enzyme on a substrate present in the sample. Its application in analysis of discrete samples, e.g., in clinical chemistry, is well documented, but it has not been used so far for continuous measurements. We decribe here the application of the enzyme thermistor for continuous monitoring and control of enzyme reactors. An enzyme thermistor filled with coimmobilized glucose oxidase and catalase was used to measure the amount of glucose in the outflow from a column reactor containing immobilized lactase acting on a lactose solution pumped through the reactor. The lactose conversion was kept on a constant level, irrespective of the actual enzymatic activity in the reactor, by regulating the flow through the reactor. The experiments were carried out with aqueous solutions of lactose as well as with whey from cow's milk.     

Modeling biological systems with uncertain kinetic data using fuzzy continuous Petri nets

Background

Uncertainties exist in many biological systems, which can be classified as random uncertainties and fuzzy uncertainties. The former can usually be dealt with using stochastic methods, while the latter have to be handled with such approaches as fuzzy methods.

Results

In this paper, we focus on a special type of biological systems that can be described using ordinary differential equations or continuous Petri nets (CPNs), but some kinetic parameters are missing or inaccurate. For this, we propose a class of fuzzy continuous Petri nets (FCPNs) by combining CPNs and fuzzy logics. We also present and implement a simulation algorithm for FCPNs, and illustrate our method with the heat shock response system.

Conclusions

This approach can be used to model biological systems where some kinetic parameters are not available or their values vary due to some environmental factors.

     

Influence of pH on enzyme stabilization: an analysis using a series-type mechanism

A series-type model is utilized to show the influence of pH on enzyme inactivation kinetics and stability. Examples of enzyme inactivations involving both single-step and series-type mechanisms are presented. Empirical relations for the inactivation rate constant for the first step and the residual activity as a function of pH are presented. This provides physical insights into the enzyme inactivation processes. The analysis forms the beginning of a framework within which one could quantitatively manipulate the inactivation rate constants and the residual activity for enzymes in desired directions as a function of pH.     

Flavensomycin, an inhibitor of enzyme reactions involving hydrogen transfer

The antifungal antibiotic flavensomycin inhibited the oxidation of amino acids and of glucose by Penicillium oxalicum. The compound inhibited l-amino acid oxidase (EC 1.4.3.2) activity for l-leucine and l-phenylalanine, and also d-amino acid oxidase (EC 1.4.3.3) in the oxidation for dl-alanine. The addition of flavin adenine dinucleotide, which is a cofactor for this enzyme, antagonized the action of the antibiotic. Glucose oxidase (EC 1.1.3.4) was also inhibited. The antibiotic inhibited the reduced nicotinamide adenine dinucleotide (NADH(2)) cytochrome c reductase (EC 1.6.2.1) as well as the much slower nonenzymatic reduction of this cytochrome by the nucleotide. Reduced cytochrome c was also oxidized nonenzymatically by flavensomycin. The antibiotic completely inhibited the action of rabbit muscle lactic dehydrogenase (EC 1.1.1.27) in promoting the reduction of pyruvate by NADH(2) but only slightly affected the reverse reaction. Alcohol dehydrogenase (EC 1.1.1.1) was also similarly inhibited. Flavensomycin prevented the reduction of nicotinamide adenine dinucleotide phosphate by isocitrate in the presence of isocitrate dehydrogenase (EC 1.1.1.42). The hexokinase (EC 2.7.1.1)-catalyzed phosphorylation of glucose, in which the adenosine triphosphate acts as a phosphate donor, was only slightly affected. Flavensomycin also inhibited the action of yeast lactate dehydrogenase (EC 1.1.2.3) on the reduction of cytochrome c. High concentrations of cytochrome c were antagonistic to this reaction. The results point to an interference with enzymatically controlled hydrogen or electron transfer as the mechanism of the antifungal activity of flavensomycin.