Project description:Stable isotope-resolved metabolomics (SIRM) provides information regarding the relative activity of numerous metabolic pathways and the contribution of nutrients to specific metabolite pools; however, SIRM experiments can be difficult to execute, and data interpretation is challenging. Furthermore, standardization of analytical procedures and workflows remain significant obstacles for widespread reproducibility. Here, we demonstrate the workflow of a typical SIRM experiment and suggest experimental controls and measures of cross-validation that improve data interpretation. Inhibitors of glycolysis and oxidative phosphorylation as well as mitochondrial uncouplers serve as pharmacological controls, which help define metabolic flux configurations that occur under well-controlled metabolic states. We demonstrate how such controls and time course labeling experiments improve confidence in metabolite assignments as well as delineate metabolic pathway relationships. Moreover, we demonstrate how radiolabeled tracers and extracellular flux analyses integrate with SIRM to improve data interpretation. Collectively, these results show how integration of flux methodologies and use of pharmacological controls increase confidence in SIRM data and provide new biological insights.
Project description:New metabolomics applications of ultra-high resolution and accuracy mass spectrometry can provide thousands of detectable isotopologues, with the number of potentially detectable isotopologues increasing exponentially with the number of stable isotopes used in newer isotope tracing methods like stable isotope-resolved metabolomics (SIRM) experiments. This huge increase in usable data requires software capable of correcting the large number of isotopologue peaks resulting from SIRM experiments in a timely manner. We describe the design of a new algorithm and software system capable of handling these high volumes of data, while including quality control methods for maintaining data quality. We validate this new algorithm against a previous single isotope correction algorithm in a two-step cross-validation. Next, we demonstrate the algorithm and correct for the effects of natural abundance for both (13)C and (15)N isotopes on a set of raw isotopologue intensities of UDP-N-acetyl-D-glucosamine derived from a (13)C/(15)N-tracing experiment. Finally, we demonstrate the algorithm on a full omics-level dataset.
Project description:Fourier transform-ion cyclotron resonance-mass spectrometry (FTICR-MS) is capable of acquiring unmatched quality of isotopologue data for stable isotope resolved metabolomics (SIRM). This capability drives the need for a continuous ion introduction for obtaining optimal isotope ratios. Here we report the simultaneous analysis of mono and dinucleotides from crude polar extracts by FTICR-MS by adapting an ion-pairing sample preparation method for LC-MS analysis. This involves a rapid cleanup of extracted nucleotides on pipet tips containing a C(18) stationary phase, which enabled global analysis of nucleotides and their (13)C isotopologues at nanomolar concentrations by direct infusion nanoelectrospray FTICR-MS with 5 minutes of data acquisition. The resolution and mass accuracy enabled computer-assisted unambiguous assignment of most nucleotide species, including all phosphorylated forms of the adenine, guanine, uracil and cytosine nucleotides, NAD(+), NADH, NADP(+), NADPH, cyclic nucleotides, several UDP-hexoses, and all their (13)C isotopologues. The method was applied to a SIRM study on human lung adenocarcinoma A549 cells grown in [U-(13)C] glucose with or without the anti-cancer agent methylseleninic acid. At m/z resolving power of 400,000, (13)C-isotopologues of nucleotides were fully resolved from all other elemental isotopologues, thus allowing their (13)C fractional enrichment to be accurately determined. The method achieves both high sample and high information throughput analysis of nucleotides for metabolic pathway reconstruction in SIRM investigations.
Project description:Stable isotope resolved metabolomics (SIRM) experiments use stable isotope tracers to provide superior metabolomics datasets for metabolic flux analysis and metabolic modeling. Since assumptions of model correctness can seriously compromise interpretation of metabolic flux results, we have developed a metabolic modeling software package specifically designed for moiety model comparison and selection based on the metabolomics data provided. Here, we tested the effectiveness of model selection with two time-series mass spectrometry (MS) isotopologue datasets for uridine diphosphate N-acetyl-d-glucosamine (UDP-GlcNAc) generated from different platforms utilizing direct infusion nanoelectrospray and liquid chromatography. Analysis results demonstrate the robustness of our model selection methods by the successful selection of the optimal model from over 40 models provided. Moreover, the effects of specific optimization methods, degree of optimization, selection criteria, and specific objective functions on model selection are illustrated. Overall, these results indicate that over-optimization can lead to model selection failure, but combining multiple datasets can help control this overfitting effect. The implication is that SIRM datasets in public repositories of reasonable quality can be combined with newly acquired datasets to improve model selection. Furthermore, curation efforts of public metabolomics repositories to maintain high data quality could have a huge impact on future metabolic modeling efforts.
Project description:We have determined the time course of [U-(13)C]-glucose utilization and transformations in SCID mice via bolus injection of the tracer in the tail vein. Incorporation of (13)C into metabolites extracted from mouse blood plasma and several tissues (lung, heart, brain, liver, kidney, and skeletal muscle) were profiled by NMR and GC-MS, which helped ascertain optimal sampling times for different target tissues. We found that the time for overall optimal (13)C incorporation into tissue was 15-20 min but with substantial differences in (13)C labeling patterns of various organs that reflected their specific metabolism. Using this stable isotope resolved metabolomics (SIRM) approach, we have compared the (13)C metabolite profile of the lungs in the same mouse with or without an orthotopic lung tumor xenograft established from human PC14PE6 lung adenocarcinoma cells. The (13)C metabolite profile shows considerable differences in [U-(13)C]-glucose transformations between the two lung tissues, demonstrating the feasibility of applying SIRM to investigate metabolic networks of human cancer xenograft in the mouse model.
Project description:The production of the oncometabolite 2-hydroxyglutarate (2-HG) has been associated with c-MYC overexpression. c-MYC also regulates glutamine metabolism and drives progression of asymptomatic precursor plasma cell (PC) malignancies to symptomatic multiple myeloma (MM). However, the presence of 2-HG and its clinical significance in PC malignancies is unknown. By performing 13C stable isotope resolved metabolomics (SIRM) using U[13C6]Glucose and U[13C5]Glutamine in human myeloma cell lines (HMCLs), we show that 2-HG is produced in clonal PCs and is derived predominantly from glutamine anaplerosis into the TCA cycle. Furthermore, the 13C SIRM studies in HMCLs also demonstrate that glutamine is preferentially utilized by the TCA cycle compared with glucose. Finally, measuring the levels of 2-HG in the BM supernatant and peripheral blood plasma from patients with precursor PC malignancies such as smoldering MM (SMM) demonstrates that relatively elevated levels of 2-HG are associated with higher levels of c-MYC expression in the BM clonal PCs and with a subsequent shorter time to progression (TTP) to MM. Thus, measuring 2-HG levels in BM supernatant or peripheral blood plasma of SMM patients offers potential early identification of those patients at high risk of progression to MM, who could benefit from early therapeutic intervention.
Project description:We have implemented a linear ion trap (LIT)-based SIM-stitching method for ultra-high-resolution Fourier transform mass spectrometry (FTMS) that increases the S/N over a wide m/z range compared to non-segmented wide full-scan (WFS) spectra. Here we described an improved segmented spectral scan stitching method that was based on quadrupole mass filter (QMF)-SIM, which overcame previous limitations of ion signal loss in LIT. This allowed for accurate representation of isotopologue distributions, both at natural abundance and in stable isotope-resolved metabolomics (SIRM)-based experiments. We also introduced a new spectral binning method that provided more precise and resolution-independent bins for irreversibly noise-suppressed FTMS spectra. We demonstrated a substantial improvement in S/N and sensitivity (typically?>?10-fold) for 13C labeled lipid extracts of human macrophages grown as three-dimensional (3D) cell culture, with detection of an increased number of 13C isotopologue ions. The method also enabled analysis of extracts from very limited biological samples.
Project description:Stable isotope tracing is a powerful technique for following the fate of individual atoms through metabolic pathways. Measuring isotopic enrichment in metabolites provides quantitative insights into the biosynthetic network and enables flux analysis as a function of external perturbations. NMR and mass spectrometry are the techniques of choice for global profiling of stable isotope labeling patterns in cellular metabolites. However, meaningful biochemical interpretation of the labeling data requires both quantitative analysis and complex modeling. Here, we demonstrate a novel approach that involved acquiring and modeling the timecourses of (13)C isotopologue data for UDP-N-acetyl-D-glucosamine (UDP-GlcNAc) synthesized from [U-(13)C]-glucose in human prostate cancer LnCaP-LN3 cells. UDP-GlcNAc is an activated building block for protein glycosylation, which is an important regulatory mechanism in the development of many prominent human diseases including cancer and diabetes.We utilized a stable isotope resolved metabolomics (SIRM) approach to determine the timecourse of (13)Cincorporation from [U-(13)C]-glucose into UDP-GlcNAc in LnCaP-LN3 cells. (13)CPositional isotopomers and isotopologues of UDP-GlcNAc were determined by high resolution NMR and Fourier transform-ion cyclotron resonance-mass spectrometry. A novel simulated annealing/genetic algorithm, called 'Genetic Algorithm for Isotopologues in Metabolic Systems' (GAIMS) was developed to find the optimal solutions to a set of simultaneous equations that represent the isotopologue compositions, which is a mixture of isotopomer species. The best model was selected based on information theory. The output comprises the timecourse of the individual labeled species, which was deconvoluted into labeled metabolic units, namely glucose, ribose, acetyl and uracil. The performance of the algorithm was demonstrated by validating the computed fractional 13C enrichment in these subunits against experimental data. The reproducibility and robustness of the deconvolution were verified by replicate experiments, extensive statistical analyses, and cross-validation against NMR data.This computational approach revealed the relative fluxes through the different biosynthetic pathways of UDP-GlcNAc, which comprises simultaneous sequential and parallel reactions, providing new insight into the regulation of UDP-GlcNAc levels and O-linked protein glycosylation. This is the first such analysis of UDP-GlcNAc dynamics, and the approach is generally applicable to other complex metabolites comprising distinct metabolic subunits, where sufficient numbers of isotopologues can be unambiguously resolved and accurately measured.
Project description:Most microorganisms remain uncultivated, and typically their ecological roles must be inferred from diversity and genomic studies. To directly measure functional roles of uncultivated microbes, we developed Chip-stable isotope probing (SIP), a high-sensitivity, high-throughput SIP method performed on a phylogenetic microarray (chip). This approach consists of microbial community incubations with isotopically labeled substrates, hybridization of the extracted community rRNA to a microarray and measurement of isotope incorporation--and therefore substrate use--by secondary ion mass spectrometer imaging (NanoSIMS). Laboratory experiments demonstrated that Chip-SIP can detect isotopic enrichment of 0.5 atom % (13)C and 0.1 atom % (15)N, thus permitting experiments with short incubation times and low substrate concentrations. We applied Chip-SIP analysis to a natural estuarine community and quantified amino acid, nucleic acid or fatty acid incorporation by 81 distinct microbial taxa, thus demonstrating that resource partitioning occurs with relatively simple organic substrates. The Chip-SIP approach expands the repertoire of stable isotope-enabled methods available to microbial ecologists and provides a means to test genomics-generated hypotheses about biogeochemical function in any natural environment.
Project description:Breast cancers vary by their origin and specific set of genetic lesions, which gives rise to distinct phenotypes and differential response to targeted and untargeted chemotherapies. To explore the functional differences of different breast cell types, we performed Stable Isotope Resolved Metabolomics (SIRM) studies of one primary breast (HMEC) and three breast cancer cells (MCF-7, MDAMB-231, and ZR75-1) having distinct genotypes and growth characteristics, using 13C6-glucose, 13C-1+2-glucose, 13C5,15N2-Gln, 13C3-glycerol, and 13C8-octanoate as tracers. These tracers were designed to probe the central energy producing and anabolic pathways (glycolysis, pentose phosphate pathway, Krebs Cycle, glutaminolysis, nucleotide synthesis and lipid turnover). We found that glycolysis was not associated with the rate of breast cancer cell proliferation, glutaminolysis did not support lipid synthesis in primary breast or breast cancer cells, but was a major contributor to pyrimidine ring synthesis in all cell types; anaplerotic pyruvate carboxylation was activated in breast cancer versus primary cells. We also found that glucose metabolism in individual breast cancer cell lines differed between in vitro cultures and tumor xenografts, but not the metabolic distinctions between cell lines, which may reflect the influence of tumor architecture/microenvironment.