The intermediate in a nitrate-responsive ω-amidase pathway in plants may signal ammonium assimilation status.

A metabolite of ammonium assimilation was previously theorized to be involved in the coordination of the overall nitrate response in plants. Here we show that 2-hydroxy-5-oxoproline, made by transamination of glutamine, the first product of ammonium assimilation, may be involved in signaling a plant's ammonium assimilation status. In leaves, 2-hydroxy-5-oxoproline met four foundational requirements to be such a signal. First, when it was applied to foliage, enzyme activities of nitrate reduction and ammonium assimilation increased; the activities of key tricarboxylic acid cycle-associated enzymes that help to supply carbon skeletons for amino acid synthesis also increased. Second, its leaf pools increased as nitrate availability increased. Third, the pool size of its precursor, Gln, reflected ammonium assimilation rather than photorespiration. Fourth, it was widely conserved among monocots, dicots, legumes, and nonlegumes and in plants with C3 or C4 metabolism. Made directly from the first product of ammonium assimilation, 2-hydroxy-5-oxoproline acted as a nitrate uptake stimulant. When 2-hydroxy-5-oxoproline was provided to roots, the plant's nitrate uptake rate approximately doubled. Plants exogenously provided with 2-hydroxy-5-oxoproline to either roots or leaves accumulated greater biomass. A model was constructed that included the proposed roles of 2-hydroxy-5-oxoproline as a signal molecule of ammonium assimilation status in leaves, as a stimulator of nitrate uptake by roots and nitrate downloading from the xylem. In summary, a glutamine metabolite made in the ω-amidase pathway stimulated nitrate uptake by roots and was likely to be a signal of ammonium assimilation status in leaves. A chemical synthesis method for 2-hydroxy-5-oxoproline was also developed.

Automated genomic context analysis and experimental validation platform for discovery of prokaryote transcriptional regulator functions.

BACKGROUND: The clustering of genes in a pathway and the co-location of functionally related genes is widely recognized in prokaryotes. We used these characteristics to predict the metabolic involvement for a Transcriptional Regulator (TR) of unknown function, identified and confirmed its biological activity. RESULTS: A software tool that identifies the genes encoded within a defined genomic neighborhood for the subject TR and its homologs was developed. The output lists of genes in the genetic neighborhoods, their annotated functions, the reactants/products, and identifies the metabolic pathway in which the encoded-proteins function. When a set of TRs of known function was analyzed, we observed that their homologs frequently had conserved genomic neighborhoods that co-located the metabolically related genes regulated by the subject TR. We postulate that TR effectors are metabolites in the identified pathways; indeed the known effectors were present. We analyzed Bxe_B3018 from Burkholderia xenovorans, a TR of unknown function and predicted that this TR was related to the glycine, threonine and serine degradation. We tested the binding of metabolites in these pathways and for those that bound, their ability to modulate TR binding to its specific DNA operator sequence. Using rtPCR, we confirmed that methylglyoxal was an effector of Bxe_3018. CONCLUSION: These studies provide the proof of concept and validation of a systematic approach to the discovery of the biological activity for proteins of unknown function, in this case a TR. Bxe_B3018 is a methylglyoxal responsive TR that controls the expression of an operon composed of a putative efflux system.

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.

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.

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.

Carbon-fate maps for metabolic reactions.

MOTIVATION: Stable isotope labeling of small-molecule metabolites (e.g. (13)C-labeling of glucose) is a powerful tool for characterizing pathways and reaction fluxes in a metabolic network. Analysis of isotope labeling patterns requires knowledge of the fates of individual atoms and moieties in reactions, which can be difficult to collect in a useful form when considering a large number of enzymatic reactions. RESULTS: We report carbon-fate maps for 4605 enzyme-catalyzed reactions documented in the KEGG database. Every fate map has been manually checked for consistency with known reaction mechanisms. A map includes a standardized structure-based identifier for each reactant (namely, an InChI string); indices for carbon atoms that are uniquely derived from the metabolite identifiers; structural data, including an identification of homotopic and prochiral carbon atoms; and a bijective map relating the corresponding carbon atoms in substrates and products. Fate maps are defined using the BioNetGen language (BNGL), a formal model-specification language, which allows a set of maps to be automatically translated into isotopomer mass-balance equations. AVAILABILITY: The carbon-fate maps and software for visualizing the maps are freely available (http://cellsignaling.lanl.gov/FateMaps/).

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.

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.

Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism.

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.

Identification of new ligands for the methionine biosynthesis transcriptional regulator (MetJ) by FAC-MS.

We have developed a high-throughput approach using frontal affinity chromatography coupled to mass spectrometry (FAC-MS) for the identification and characterization of the small molecules that modulate transcriptional regulator (TR) binding to TR targets. We tested this approach using the methionine biosynthesis regulator (MetJ). We used effector mixtures containing S-adenosyl-L-methionine (SAM) and S-adenosyl derivatives as potential ligands for MetJ binding. The differences in the elution time of different compounds allowed us to rank the binding affinity of each compound. Consistent with previous results, FAC-MS showed that SAM binds to MetJ with the highest affinity. In addition, adenine and 5'-deoxy-5'-(methylthio)adenosine bind to the effector binding site on MetJ. Our experiments with MetJ demonstrate that FAC-MS is capable of screening complex mixtures of molecules and identifying high-affinity binders to TRs. In addition, FAC-MS experiments can be used to discriminate between specific and nonspecific binding of the effectors as well as to estimate the dissociation constant (K(d)) for effector-TR binding.