The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells.

BackgroundDetection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation.ResultsxMSanalyzer is a set of utilities for automated processing of metabolomics data. The utilites can be classified into four main modules to: 1) improve feature detection for replicate analyses by systematic re-extraction with multiple parameter settings and data merger to optimize the balance between sensitivity and reliability, 2) evaluate sample quality and feature consistency, 3) detect feature overlap between datasets, and 4) characterize high-resolution m/z matches to small molecule metabolites and biological pathways using multiple chemical databases. The package was tested with plasma samples and shown to more than double the number of features extracted while improving quantitative reliability of detection. MS/MS analysis of a random subset of peaks that were exclusively detected using xMSanalyzer confirmed that the optimization scheme improves detection of real metabolites.ConclusionsxMSanalyzer is a package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. The program was designed to integrate with existing packages such as apLCMS and XCMS, but the framework can also be used to enhance data extraction for other LC/MS data software.

The exposome is the cumulative measure of environmental influences and associated biological responses throughout the lifespan, including exposures from the environment, diet, behavior, and endogenous processes. A major challenge for exposome research lies in the development of robust and affordable analytic procedures to measure the broad range of exposures and associated biologic impacts occurring over a lifetime. Biomonitoring is an established approach to evaluate internal body burden of environmental exposures, but use of biomonitoring for exposome research is often limited by the high costs associated with quantification of individual chemicals. High-resolution metabolomics (HRM) uses ultra-high resolution mass spectrometry with minimal sample preparation to support high-throughput relative quantification of thousands of environmental, dietary, and microbial chemicals. HRM also measures metabolites in most endogenous metabolic pathways, thereby providing simultaneous measurement of biologic responses to environmental exposures. The present research examined quantification strategies to enhance the usefulness of HRM data for cumulative exposome research. The results provide a simple reference standardization protocol in which individual chemical concentrations in unknown samples are estimated by comparison to a concurrently analyzed, pooled reference sample with known chemical concentrations. The approach was tested using blinded analyses of amino acids in human samples and was found to be comparable to independent laboratory results based on surrogate standardization or internal standardization. Quantification was reproducible over a 13-month period and extrapolated to thousands of chemicals. The results show that reference standardization protocol provides an effective strategy that will enhance data collection for cumulative exposome research. In principle, the approach can be extended to other types of mass spectrometry and other analytical methods.

Studies of gene–environment (G × E) interactions require effective characterization of all environmental exposures from conception to death, termed the exposome. The exposome includes environmental exposures that impact health. Improved metabolic profiling methods are needed to characterize these exposures for use in personalized medicine. In the present study, we compared the analytic capability of dual chromatography-Fourier-transform mass spectrometry (DC-FTMS) to previously used liquid chromatography-FTMS (LC-FTMS) analysis for high-throughput, top-down metabolic profiling. For DC-FTMS, we combined data from sequential LC-FTMS analyses using reverse phase (C18) chromatography and anion exchange (AE) chromatography. Each analysis was performed with electrospray ionization in the positive ion mode and detection from m/z 85 to 850. Run time for each column was 10 min with gradient elution; 10 µl extracts of plasma from humans and common marmosets were used for analysis. In comparison to analysis with the AE column alone, addition of the second LC-FTMS analysis with the C18 column increased m/z feature detection by 23–36%, yielding a total number of features up to 7,000 for individual samples. Approximately 50% of the m/z matched to known chemicals in metabolomic databases, and 23% of the m/z were common to analyses on both columns. Database matches included insecticides, herbicides, flame retardants, and plasticizers. Modularity clustering algorithms applied to MS-data showed the ability to detection clusters and ion interactions. DC-FTMS thus provides improved capability for high-performance metabolic profiling of the exposome and development of personalized medicine.

Nitric oxide (NO) induces mitochondrial biogenesis in skeletal muscle cells via upregulation of the peroxisome proliferator-activated receptor-γ coactivator 1α (PGC-1α). Further, we have shown that nitric oxide interacts with the metabolic sensor enzyme, AMPK. Therefore, we tested the hypothesis that nitric oxide and AMPK act synergistically to upregulate PGC-1α mRNA expression and stimulate mitochondrial biogenesis in culture. L6 myotubes treated with nitric oxide donors, S-nitroso-N -penicillamine (SNAP, 25 μm) or diethylenetriamine-NONO (DETA-NO, 50 μm), exhibited elevated AMPK phosphorylation, PGC-1α mRNA and protein, and basal and uncoupled mitochondrial respiration (P < 0.05). Pre-treatment of cultures with the AMPK inhibitor, Compound C, prevented these effects. Knockdown of AMPKα1 in L6 myotubes using siRNA reduced AMPKα protein content and prevented upregulation of PGC-1α mRNA by DETA-NO. Meanwhile, siRNA knockdown of AMPKα2 had no effect on total AMPKα protein content or PGC-1α mRNA. These results suggest that NO effects on PGC-1α expression are mediated by AMPKα1. Paradoxically, we found that the AMPK-activating compound, AICAR, induced NO release from L6 myotubes, and that AICAR-induced upregulation of PGC-1α mRNA was prevented by inhibition of NOS with N G -nitro-l-arginine methyl ester (l-NAME, 1 mm). Additionally, incubation of isolated mouse extensor digitorum longus (EDL) muscles with 2 mm AICAR for 20 min or electrical stimulation (10 Hz, 13 V) for 10 min induced phosphorylation of AMPKα (P < 0.05), which was completely prevented by pre-treatment with the NOS inhibitor, l-N G -monomethyl arginine (l-NMMA, 1 mm). These data identify the AMPKα1 isoform as the mediator of NO-induced effects in skeletal muscle cells. Further, this study supports a proposed model of synergistic interaction between AMPK and NOS that is critical for maintenance of metabolic function in skeletal muscle cells.

Nitric oxide (NO) and 5'-AMP-activated protein kinase (AMPK) are involved in glucose transport and mitochondrial biogenesis in skeletal muscle. Here, we examined whether NO regulates the expression of the major glucose transporter in muscle (GLUT4) and whether it influences AMPK-induced upregulation of GLUT4. At low levels, the NO donor S-nitroso-N-penicillamine (SNAP, 1 and 10 microM) significantly increased GLUT4 mRNA ( approximately 3-fold; P < 0.05) in L6 myotubes, and cotreatment with the AMPK inhibitor compound C ablated this effect. The cGMP analog 8-bromo-cGMP (8-Br-cGMP, 2 mM) increased GLUT4 mRNA by approximately 50% (P < 0.05). GLUT4 protein expression was elevated 40% by 2 days treatment with 8-Br-cGMP, whereas 6 days treatment with 10 microM SNAP increased GLUT4 expression by 65%. Cotreatment of cultures with the guanylyl cyclase inhibitor 1H-[1,2,4]oxadiazolo[4,3,-a]quinoxalin-1-one prevented the SNAP-induced increase in GLUT4 protein. SNAP (10 microM) also induced significant phosphorylation of alpha-AMPK and acetyl-CoA carboxylase and translocation of phosphorylated alpha-AMPK to the nucleus. Furthermore, L6 myotubes exposed to 5-aminoimidazole-4-carboxamide-1-beta-d-ribofuranoside (AICAR) for 16 h presented an approximately ninefold increase in GLUT4 mRNA, whereas cotreatment with the non-isoform-specific NOS inhibitor N(G)-nitro-l-arginine methyl ester, prevented approximately 70% of this effect. In vivo, GLUT4 mRNA was increased 1.8-fold in the rat plantaris muscle 12 h after AICAR injection, and this induction was reduced by approximately 50% in animals cotreated with the neuronal and inducible nitric oxide synthases selective inhibitor 1-(2-trifluoromethyl-phenyl)-imidazole. We conclude that, in skeletal muscle, NO increases GLUT4 expression via a cGMP- and AMPK-dependent mechanism. The data are consistent with a role for NO in the regulation of AMPK, possibly via control of cellular activity of AMPK kinases and/or AMPK phosphatases.

Researchers have used whole-genome sequencing and gene expression profiling to identify genes associated with age, in the hope of understanding the underlying mechanisms of senescence. But there is a substantial gap from variation in gene sequences and expression levels to variation in age or life expectancy. In an attempt to bridge this gap, here we describe the effects of age, sex, genotype, and their interactions on high-sensitivity metabolomic profiles in the fruit fly, Drosophila melanogaster. Among the 6800 features analyzed, we found that over one-quarter of all metabolites were significantly associated with age, sex, genotype, or their interactions, and multivariate analysis shows that individual metabolomic profiles are highly predictive of these traits. Using a metabolomic equivalent of gene set enrichment analysis, we identified numerous metabolic pathways that were enriched among metabolites associated with age, sex, and genotype, including pathways involving sugar and glycerophospholipid metabolism, neurotransmitters, amino acids, and the carnitine shuttle. Our results suggest that high-sensitivity metabolomic studies have excellent potential not only to reveal mechanisms that lead to senescence, but also to help us understand differences in patterns of aging among genotypes and between males and females.

COX activity is important for in vivo muscle hypertrophy, and plantaris overload is associated with NOS activity-dependent COX-2 expression.