Predicting network activity from high throughput metabolomics.

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.

xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data.

BACKGROUND: Detection 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. RESULTS: xMSanalyzer 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. CONCLUSIONS: xMSanalyzer 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.

A practical approach to detect unique metabolic patterns for personalized medicine.

Information-rich technologies have advanced personalized medicine, yet obstacles limit measurement of large numbers of chemicals in human samples. Current laboratory tests measure hundreds of chemicals based upon existing knowledge of exposures, metabolism and disease mechanisms. Practical issues of cost and throughput preclude measurement of thousands of chemicals. Additionally, individuals are genetically diverse and have different exposures and response characteristics; some have disease mechanisms that have not yet been elucidated. Consequently, methods are needed to detect unique metabolic characteristics without presumption of known pathways, exposures or disease mechanisms, i.e., using a top-down approach. In this report, we describe profiling of human plasma with liquid chromatography (LC) coupled to Fourier-transform mass spectrometry (FTMS). FTMS is a high-resolution mass spectrometer providing mass accuracy and resolution to discriminate thousands of m/z features, which are peaks defined by m/z, retention time and intensity. We demonstrate that LC-FTMS detects 2000 m/z features in 10 min. These features include known and unidentified chemicals with m/z between 85 and 850, most with <10% coefficient of variation. Comparison of metabolic profiles for 4 healthy individuals showed that 62% of the m/z features were common while 10% were unique and 770 discriminated the individuals. Because the simple one-step extraction and automated analysis is rapid and cost-effective, the approach is practical for personalized medicine. This provides a basis to rapidly characterize novel metabolic patterns which can be linked to genetics, environment and/or lifestyle.

Mitochondrial metabolomics using high-resolution Fourier-transform mass spectrometry.

High-resolution Fourier-transform mass spectrometry (FTMS) provides important advantages in studies of metabolism because more than half of common intermediary metabolites can be measured in 10 min with minimal pre-detector separation and without ion dissociation. This capability allows unprecedented opportunity to study complex metabolic systems, such as mitochondria. Analysis of mouse liver mitochondria using FTMS with liquid chromatography shows that sex and genotypic differences in mitochondrial metabolism can be readily distinguished. Additionally, differences in mitochondrial function are readily measured, and many of the mitochondria-related metabolites are also measurable in plasma. Thus, application of high-resolution mass spectrometry provides an approach for integrated studies of complex metabolic processes of mitochondrial function and dysfunction in disease.

High-performance metabolic profiling with dual chromatography-Fourier-transform mass spectrometry (DC-FTMS) for study of the exposome.

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.

Serum metabolomics of slow vs. rapid motor progression Parkinson's disease: a pilot study.

Progression of Parkinson's disease (PD) is highly variable, indicating that differences between slow and rapid progression forms could provide valuable information for improved early detection and management. Unfortunately, this represents a complex problem due to the heterogeneous nature of humans in regards to demographic characteristics, genetics, diet, environmental exposures and health behaviors. In this pilot study, we employed high resolution mass spectrometry-based metabolic profiling to investigate the metabolic signatures of slow versus rapidly progressing PD present in human serum. Archival serum samples from PD patients obtained within 3 years of disease onset were analyzed via dual chromatography-high resolution mass spectrometry, with data extraction by xMSanalyzer and used to predict rapid or slow motor progression of these patients during follow-up. Statistical analyses, such as false discovery rate analysis and partial least squares discriminant analysis, yielded a list of statistically significant metabolic features and further investigation revealed potential biomarkers. In particular, N8-acetyl spermidine was found to be significantly elevated in the rapid progressors compared to both control subjects and slow progressors. Our exploratory data indicate that a fast motor progression disease phenotype can be distinguished early in disease using high resolution mass spectrometry-based metabolic profiling and that altered polyamine metabolism may be a predictive marker of rapidly progressing PD.

Detailed mitochondrial phenotyping by high resolution metabolomics.

Mitochondrial phenotype is complex and difficult to define at the level of individual cell types. Newer metabolic profiling methods provide information on dozens of metabolic pathways from a relatively small sample. This pilot study used "top-down" metabolic profiling to determine the spectrum of metabolites present in liver mitochondria. High resolution mass spectral analyses and multivariate statistical tests provided global metabolic information about mitochondria and showed that liver mitochondria possess a significant phenotype based on gender and genotype. The data also show that mitochondria contain a large number of unidentified chemicals.

High-performance metabolic profiling of plasma from seven mammalian species for simultaneous environmental chemical surveillance and bioeffect monitoring.

High-performance metabolic profiling (HPMP) by Fourier-transform mass spectrometry coupled to liquid chromatography gives relative quantification of thousands of chemicals in biologic samples but has had little development for use in toxicology research. In principle, the approach could be useful to detect complex metabolic response patterns to toxicologic exposures and to detect unusual abundances or patterns of potentially toxic chemicals. As an initial study to develop these possible uses, we applied HPMP and bioinformatics analysis to plasma of humans, rhesus macaques, marmosets, pigs, sheep, rats and mice to determine: (1) whether more chemicals are detected in humans living in a less controlled environment than captive species and (2) whether a subset of plasma chemicals with similar inter-species and intra-species variation could be identified for use in comparative toxicology. Results show that the number of chemicals detected was similar in humans (3221) and other species (range 2537-3373). Metabolite patterns were most similar within species and separated samples according to family and order. A total of 1485 chemicals were common to all species; 37% of these matched chemicals in human metabolomic databases and included chemicals in 137 out of 146 human metabolic pathways. Probability-based modularity clustering separated 644 chemicals, including many endogenous metabolites, with inter-species variation similar to intra-species variation. The remaining chemicals had greater inter-species variation and included environmental chemicals as well as GSH and methionine. Together, the data suggest that HPMP provides a platform that can be useful within human populations and controlled animal studies to simultaneously evaluate environmental exposures and biological responses to such exposures.