Sirt3 promotes the urea cycle and fatty acid oxidation during dietary restriction.

Emerging evidence suggests that protein acetylation is a broad-ranging regulatory mechanism. Here we utilize acetyl-peptide arrays and metabolomic analyses to identify substrates of mitochondrial deacetylase Sirt3. We identified ornithine transcarbamoylase (OTC) from the urea cycle, and enzymes involved in ß-oxidation. Metabolomic analyses of fasted mice lacking Sirt3 (sirt3(-/-)) revealed alterations in ß-oxidation and the urea cycle. Biochemical analysis demonstrated that Sirt3 directly deacetylates OTC and stimulates its activity. Mice under caloric restriction (CR) increased Sirt3 protein levels, leading to deacetylation and stimulation of OTC activity. In contrast, sirt3(-/-) mice failed to deacetylate OTC in response to CR. Inability to stimulate OTC under CR led to a failure to reduce orotic acid levels, a known outcome of OTC deficiency. Thus, Sirt3 directly regulates OTC activity and promotes the urea cycle during CR, and the results suggest that under low energy input, Sirt3 modulates mitochondria by promoting amino acid catabolism and ß-oxidation.

Semiautomated device for batch extraction of metabolites from tissue samples.

Metabolomics has become a mainstream analytical strategy for investigating metabolism. The quality of data derived from these studies is proportional to the consistency of the sample preparation. Although considerable research has been devoted to finding optimal extraction protocols, most of the established methods require extensive sample handling. Manual sample preparation can be highly effective in the hands of skilled technicians, but an automated tool for purifying metabolites from complex biological tissues would be of obvious utility to the field. Here, we introduce the semiautomated metabolite batch extraction device (SAMBED), a new tool designed to simplify metabolomics sample preparation. We discuss SAMBED's design and show that SAMBED-based extractions are of comparable quality to extracts produced through traditional methods (13% mean coefficient of variation from SAMBED versus 16% from manual extractions). Moreover, we show that aqueous SAMBED-based methods can be completed in less than a quarter of the time required for manual extractions.

Role of aminotransferases in glutamate metabolism of human erythrocytes.

Human erythrocytes require a continual supply of glutamate to support glutathione synthesis, but are unable to transport this amino acid across their cell membrane. Consequently, erythrocytes rely on de novo glutamate biosynthesis from α-ketoglutarate and glutamine to maintain intracellular levels of glutamate. Erythrocytic glutamate biosynthesis is catalyzed by three enzymes, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and glutamine aminohydrolase (GA). Although the presence of these enzymes in RBCs has been well documented, the relative contributions of each pathway have not been established. Understanding the relative contributions of each biosynthetic pathway is critical for designing effective therapies for sickle cell disease, hemolytic anemia, pulmonary hypertension, and other glutathione-related disorders. In this study, we use multidimensional (1)H-(13)C nuclear magnetic resonance (NMR) spectroscopy and multiple reaction mode mass spectrometry (MRM-MS) to measure the kinetics of de novo glutamate biosynthesis via AST, ALT, and GA in intact cells and RBC lysates. We show that up to 89% of the erythrocyte glutamate pool can be derived from ALT and that ALT-derived glutamate is subsequently used for glutathione synthesis.

Deconvolution of two-dimensional NMR spectra by fast maximum likelihood reconstruction: application to quantitative metabolomics.

We have developed an algorithm called fast maximum likelihood reconstruction (FMLR) that performs spectral deconvolution of 1D-2D NMR spectra for the purpose of accurate signal quantification. FMLR constructs the simplest time-domain model (e.g., the model with the fewest number of signals and parameters) whose frequency spectrum matches the visible regions of the spectrum obtained from identical Fourier processing of the acquired data. We describe the application of FMLR to quantitative metabolomics and demonstrate the accuracy of the method by analysis of complex, synthetic mixtures of metabolites and liver extracts. The algorithm demonstrates greater accuracy (0.5-5.0% error) than peak height analysis and peak integral analysis with greatly reduced operator intervention. FMLR has been implemented in a Java-based framework that is available for download on multiple platforms and is interoperable with popular NMR display and processing software. Two-dimensional (1)H-(13)C spectra of mixtures can be acquired with acquisition times of 15 min and analyzed by FMLR in the range of 2-5 min per spectrum to identify and quantify constituents present at concentrations of 0.2 mM or greater.

Measurement of absolute concentrations of individual compounds in metabolite mixtures by gradient-selective time-zero 1H-13C HSQC with two concentration references and fast maximum likelihood reconstruction analysis.

Time-zero 2D (13)C HSQC (HSQC(0)) spectroscopy offers advantages over traditional 2D NMR for quantitative analysis of solutions containing a mixture of compounds because the signal intensities are directly proportional to the concentrations of the constituents. The HSQC(0) spectrum is derived from a series of spectra collected with increasing repetition times within the basic HSQC block by extrapolating the repetition time to zero. Here we present an alternative approach to data collection, gradient-selective time-zero (1)H-(13)C HSQC(0) in combination with fast maximum likelihood reconstruction (FMLR) data analysis and the use of two concentration references for absolute concentration determination. Gradient-selective data acquisition results in cleaner spectra, and NMR data can be acquired in both constant-time and non-constant-time mode. Semiautomatic data analysis is supported by the FMLR approach, which is used to deconvolute the spectra and extract peak volumes. The peak volumes obtained from this analysis are converted to absolute concentrations by reference to the peak volumes of two internal reference compounds of known concentration: DSS (4,4-dimethyl-4-silapentane-1-sulfonic acid) at the low concentration limit (which also serves as chemical shift reference) and MES (2-(N-morpholino)ethanesulfonic acid) at the high concentration limit. The linear relationship between peak volumes and concentration is better defined with two references than with one, and the measured absolute concentrations of individual compounds in the mixture are more accurate. We compare results from semiautomated gsHSQC(0) with those obtained by the original manual phase-cycled HSQC(0) approach. The new approach is suitable for automatic metabolite profiling by simultaneous quantification of multiple metabolites in a complex mixture.

Scaffold-fused riboregulators for enhanced gene activation in Synechocystis sp. PCC 6803.

Cyanobacteria are an attractive host for biofuel production because they can produce valuable chemical compounds from CO2 fixed by photosynthesis. However, the available genetic tools that enable precise gene regulation for the applications of synthetic biology are insufficient. Previously, we engineered an RNA-based posttranscriptional regulator, termed riboregulator, for the control of target gene expression in cyanobacterium Synechocystis sp. PCC 6803. Moreover, we enhanced the gene regulation ability of the riboregulators in Escherichia coli by fusing and engineering a scaffold sequence derived from naturally occurring E. coli noncoding small RNAs. Here, we demonstrated that the scaffold sequence fused to the riboregulators improved their gene regulation ability in Synechocystis sp. PCC 6803. To further improve gene regulation, we expressed an exogenous RNA chaperone protein that is responsible for noncoding small RNA-mediated gene regulation, which resulted in higher target gene expression. The scaffold sequence derived from natural E. coli noncoding small RNAs is effective for designing RNA-based genetic tools and scaffold-fused riboregulators are a strong RNA-tool to regulate gene expression in cyanobacteria.

NMRbot: Python scripts enable high-throughput data collection on current Bruker BioSpin NMR spectrometers.

To facilitate the high-throughput acquisition of nuclear magnetic resonance (NMR) experimental data on large sets of samples, we have developed a simple and straightforward automated methodology that capitalizes on recent advances in Bruker BioSpin NMR spectrometer hardware and software. Given the daunting challenge for non-NMR experts to collect quality spectra, our goal was to increase user accessibility, provide customized functionality, and improve the consistency and reliability of resultant data. This methodology, NMRbot, is encoded in a set of scripts written in the Python programming language accessible within the Bruker BioSpin TopSpin™ software. NMRbot improves automated data acquisition and offers novel tools for use in optimizing experimental parameters on the fly. This automated procedure has been successfully implemented for investigations in metabolomics, small-molecule library profiling, and protein-ligand titrations on four Bruker BioSpin NMR spectrometers at the National Magnetic Resonance Facility at Madison. The investigators reported benefits from ease of setup, improved spectral quality, convenient customizations, and overall time savings.

Databases and Software for NMR-Based Metabolomics.

New software and increasingly sophisticated NMR metabolite spectral databases are advancing the unique abilities of NMR spectroscopy to identify and quantify small molecules in solution for studies of metabolite biomarkers and metabolic flux. Public and commercial databases now contain experimental 1D 1H, 13C and 2D 1H-13C spectra and extracted spectral parameters for over a thousand compounds and theoretical data for thousands more. Public databases containing experimental NMR data from complex metabolic studies are emerging. These databases are providing information vital for the construction and testing of new computational algorithms for NMR-based chemometric and quantitative metabolomics studies. In this review we focus on database and software tools that support a quantitative NMR approach to the analysis of 1D and 2D NMR spectra of complex biological mixtures.

Tracing Metabolite Footsteps of Escherichia coli Along the Time Course of Recombinant Protein Expression by Two-Dimensional NMR Spectroscopy.

The recombinant expression of proteins has been the method of choice to meet the demands from proteomics and structural genomics studies. Despite its successful production of many heterologous proteins, Escherichia coli failed to produce many other proteins in their native forms. This may be related to the fact that the stresses resulting from the overproduction interfere with cellular processes. To better understand the physiological change during the overproduction phase, we profiled the metabolites along the time course of the recombinant protein expression. We identified 32 metabolites collected from different time points in the protein production phase. The stress induced by protein production can be characterized by (A) the increased usage of aspartic acid, choline, glycerol, and N-acetyllysine; and (B) the accumulation of adenosine, alanine, oxidized glutathione, glycine, N-acetylputrescine, and uracil. We envision that this work can be used to create a strategy for the production of usable proteins in large quantities.