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.

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.

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.

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.

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.

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.

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).

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.

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.

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.