Pat J. Unkefer

Biotransformation of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) by a prospective consortium and its most effective isolate Serratia marcescens

The biotransformation of hexahydro-1,3,5-trinitro-1,3,5 triazine (RDX) has been observed in liquid culture by a consortium of bacteria found in horse manure. Five types of bacteria were found to predominate in the consortium and were isolated. The most effective of these isolates at transforming RDX was Serratia marcescens. The biotransformation of RDX by all of these bacteria was found to occur only in the anoxic stationary phase. The process of bacterial growth and RDX biotransformation was quantified for the purpose of developing a predictive type model. Cell growth was assumed to follow Monod kinetics. All of the aerobic and anoxic growth parameters were determined: micro(max), K(s), and Y(x/s). RDX was found to competitively inhibit cell growth in both atmospheres. Degradation of RDX by Serratia marcescens was found to proceed through the stepwise reduction of the three nitro groups to nitroso groups. Each of these reductions was found to be first order in both component and cell concentrations. The degradation rate constant for the first step in this reduction process by the consortium was 0.022 L/g cells . h compared to 0.033 L/g cells . h for the most efficient isolate. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 53: 515-522, 1997.

Type I nitroreductases in soil enterobacteria reduce TNT (2,4,6-trinitrotoluene) and RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine)

Many enteric bacteria express a type I oxygen-insensitive nitroreductase, which reduces nitro groups on many different nitroaromatic compounds under aerobic conditions. Enzymatic reduction of nitramines was also documented in enteric bacteria under anaerobic conditions. This study indicates that nitramine reduction in enteric bacteria is carried out by the type I, or oxygen-insensitive nitroreductase, rather than a type II enzyme. The enteric bacterium Morganella morganii strain B2 with documented hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) nitroreductase activity, and Enterobacter cloacae strain 96-3 with documented 2,4,6-trinitrotoluene (TNT) nitroreductase activity, were used here to show that the explosives TNT and RDX were both reduced by a type I nitroreductase. Morganella morganii and E. cloacae exhibited RDX and TNT nitroreductase activities in whole cell assays. Type I nitroreductase, purified from E. cloacae, oxidized NADPH with TNT or RDX as substrate. When expression of the E. cloacae type I nitroreductase gene was induced in an Escherichia coli strain carrying a plasmid, a simultaneous increase in TNT and RDX nitroreductase activities was observed. In addition, neither TNT nor RDX nitroreductase activity was detected in nitrofurazone-resistant mutants of M. morganii. We conclude that a type I nitroreductase present in these two enteric bacteria was responsible for the nitroreduction of both types of explosive.

Modulation of glutamine synthetase gene expression in tobacco by the introduction of an alfalfa glutamine synthetase gene in sense and antisense orientation: molecular and biochemical analysis

SummaryA glutamine synthetase (GS) cDNA isolated from an alfalfa cell culture cDNA library was found to represent a cytoplasmic GS. The full-length alfalfa GS1 coding sequence, in both sense and antisense orientation and under the transcriptional control of the cauliflower mosaic virus 35S promoter, was introduced into tobacco. Leaves of tobacco plants transformed with the sense construct contained greatly elevated levels of GS transcript and GS polypeptide which assembled into active enzyme. Leaves of the plants transformed with the antisense GS1, construct showed a significant decrease in the level of both GS1, and GS2, polypeptides and GS activity, but did not show any significant decrease in the level of endogenous GS mRNA. We have proposed that antisense inhibition using a heterologous antisense GS RNA occurs at the level of translation. Our results also suggest that the post-translational assembly of GS subunits into a holoenzyme requires an additional factor(s) and is under regulatory control.

Biological breakdown of RDX in slurry reactors proceeds with multiple kinetically distinguishable paths

Biotransformation of RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine) in slurry reactors was studied to determine the importance of supplementation of known biodegraders and the type of nutrient source required. Although addition of bacteria to the system increased the biotransformation rates, the increase may not justify the additional work and cost needed to grow the organisms in a laboratory and mix them into the soil. An inexpensive, rich nutrient source, corn steep liquor, was shown to provide sufficient nutrients to allow for the cometabolic biotransformation of RDX. The rate of RDX transformation was not constant throughout the course of the experiment due to the heterogeneous microbial population. Three kinetically distinct phases were observed. Regardless of the process, RDX biotransformation in slurry reactors was reaction rate limited under the test conditions. Model simulations based on experimental results demonstrate that, at cell densities of 5 g/L, bioremediation of RDX-contaminated soil is an attractive clean-up alternative.

Using Sequence-Specific Chemical and Structural Properties of DNA to Predict Transcription Factor Binding Sites

An important step in understanding gene regulation is to identify the DNA binding sites recognized by each transcription factor (TF). Conventional approaches to prediction of TF binding sites involve the definition of consensus sequences or position-specific weight matrices and rely on statistical analysis of DNA sequences of known binding sites. Here, we present a method called SiteSleuth in which DNA structure prediction, computational chemistry, and machine learning are applied to develop models for TF binding sites. In this approach, binary classifiers are trained to discriminate between true and false binding sites based on the sequence-specific chemical and structural features of DNA. These features are determined via molecular dynamics calculations in which we consider each base in different local neighborhoods. For each of 54 TFs in Escherichia coli, for which at least five DNA binding sites are documented in RegulonDB, the TF binding sites and portions of the non-coding genome sequence are mapped to feature vectors and used in training. According to cross-validation analysis and a comparison of computational predictions against ChIP-chip data available for the TF Fis, SiteSleuth outperforms three conventional approaches: Match, MATRIX SEARCH, and the method of Berg and von Hippel. SiteSleuth also outperforms QPMEME, a method similar to SiteSleuth in that it involves a learning algorithm. The main advantage of SiteSleuth is a lower false positive rate.

Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds

MOTIVATION Our knowledge of the metabolites in cells and their reactions is far from complete as revealed by metabolomic measurements that detect many more small molecules than are documented in metabolic databases. Here, we develop an approach for predicting the reactivity of small-molecule metabolites in enzyme-catalyzed reactions that combines expert knowledge, computational chemistry and machine learning. RESULTS We classified 4843 reactions documented in the KEGG database, from all six Enzyme Commission classes (EC 1-6), into 80 reaction classes, each of which is marked by a characteristic functional group transformation. Reaction centers and surrounding local structures in substrates and products of these reactions were represented using SMARTS. We found that each of the SMARTS-defined chemical substructures is widely distributed among metabolites, but only a fraction of the functional groups in these substructures are reactive. Using atomic properties of atoms in a putative reaction center and molecular properties as features, we trained support vector machine (SVM) classifiers to discriminate between functional groups that are reactive and non-reactive. Classifier accuracy was assessed by cross-validation analysis. A typical sensitivity [TP/(TP+FN)] or specificity [TN/(TN+FP)] is ≈0.8. Our results suggest that metabolic reactivity of small-molecule compounds can be predicted with reasonable accuracy based on the presence of a potentially reactive functional group and the chemical features of its local environment. AVAILABILITY The classifiers presented here can be used to predict reactions via a web site (http://cellsignaling.lanl.gov/Reactivity/). The web site is freely available.

Reconstruction of Metabolic Networks from High-Throughput Metabolite Profiling Data : In Silico Analysis of Red Blood Cell Metabolism

Abstract:  We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high‐throughput metabolite profiling data. For benchmarking purposes, we generate synthetic metabolic profiles based on a well‐established model for red blood cell metabolism. A variety of data sets are generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We use ARACNE, a mainstream algorithm for reverse engineering of transcriptional regulatory networks from gene expression data, to predict metabolic interactions from these data sets. We find that the performance of ARACNE on metabolic data is comparable to that on gene expression data.

Prediction of oxidoreductase-catalyzed reactions based on atomic properties of metabolites

MOTIVATION Our knowledge of metabolism is far from complete, and the gaps in our knowledge are being revealed by metabolomic detection of small-molecules not previously known to exist in cells. An important challenge is to determine the reactions in which these compounds participate, which can lead to the identification of gene products responsible for novel metabolic pathways. To address this challenge, we investigate how machine learning can be used to predict potential substrates and products of oxidoreductase-catalyzed reactions. RESULTS We examined 1956 oxidation/reduction reactions in the KEGG database. The vast majority of these reactions (1626) can be divided into 12 subclasses, each of which is marked by a particular type of functional group transformation. For a given transformation, the local structures of reaction centers in substrates and products can be characterized by patterns. These patterns are not unique to reactants but are widely distributed among KEGG metabolites. To distinguish reactants from non-reactants, we trained classifiers (linear-kernel Support Vector Machines) using negative and positive examples. The input to a classifier is a set of atomic features that can be determined from the 2D chemical structure of a compound. Depending on the subclass of reaction, the accuracy of prediction for positives (negatives) is 64 to 93% (44 to 92%) when asking if a compound is a substrate and 71 to 98% (50 to 92%) when asking if a compound is a product. Sensitivity analysis reveals that this performance is robust to variations of the training data. Our results suggest that metabolic connectivity can be predicted with reasonable accuracy from the presence or absence of local structural motifs in compounds and their readily calculated atomic features. AVAILABILITY Classifiers reported here can be used freely for noncommercial purposes via a Java program available upon request.

Biosynthesis of the 3,4-Dihydroxybenzoate Moieties of Petrobactin by Bacillus anthracis

The biosynthesis of the 3,4-dihydroxybenzoate moieties of the siderophore petrobactin, produced by B. anthracis str. Sterne, was probed by isotopic feeding experiments in iron-deficient media with a mixture of unlabeled and D-[(13)C6]glucose at a ratio of 5:1 (w/w). After isolation of the labeled siderophore, analysis of the isotopomers was conducted via one-dimensional (1)H and (13)C NMR spectroscopy, as well as (13)C-(13)C DQFCOSY spectroscopy. Isotopic enrichment and (13)C-(13)C coupling constants in the aromatic ring of the isolated siderophore suggested the predominant route for the construction of the carbon backbone of 3,4-DHB (1) involved phosphoenol pyruvate and erythrose-4-phosphate as ultimate precursors. This observation is consistent with that expected if the shikimate pathway is involved in the biosynthesis of these moieties. Enrichment attributable to phosphoenol pyruvate precursors was observed at C1 and C6 of the aromatic ring, as well as into the carboxylate group, while scrambling of the label into C2 was not. This pattern suggests 1 was biosynthesized from early intermediates of the shikimate pathway and not through later shikimate intermediates or aromatic amino acid precursors.

Discovery of DNA operators for TetR and MarR family transcription factors from Burkholderia xenovorans

Determining transcription factor (TF) recognition motifs or operator sites is central to understanding gene regulation, yet few operators have been characterized. In this study, we used a protein-binding microarray (PBM) to discover the DNA recognition sites and putative regulons for three TetR and one MarR family TFs derived from Burkholderia xenovorans, which are common to the genus Burkholderia. We also describe the development and application of a more streamlined version of the PBM technology that significantly reduced the experimental time. Despite the genus containing many pathogenically important species, only a handful of TF operator sites have been experimentally characterized for Burkholderia to date. Our study provides a significant addition to this knowledge base and illustrates some general challenges of discovering operators on a large scale for prokaryotes.