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.     

IDENTIFICATION OF THE HIGH‐TEMPERATURE RESPONSE GENES FROM PORPHYRA SERIATA (RHODOPHYTA) EXPRESSION SEQUENCE TAGS AND ENHANCEMENT OF HEAT TOLERANCE OF CHLAMYDOMONAS (CHLOROPHYTA) BY EXPRESSION OF THE PORPHYRA HTR2 GENE1

Temperature is one of the major environmental factors that affect the distribution, growth rate, and life cycle of intertidal organisms, including red algae. In an effort to identify the genes involved in the high‐temperature tolerance of Porphyra, we generated 3,979 expression sequence tags (ESTs) from gametophyte thalli of P. seriata Kjellm. under normal growth conditions and high‐temperature conditions. A comparison of the ESTs from two cDNA libraries allowed us to identify the high temperature response (HTR) genes, which are induced or up‐regulated as the result of high‐temperature treatment. Among the HTRs, HTR2 encodes for a small polypeptide consisting of 144 amino acids, which is a noble nuclear protein. Chlamydomonas expressing the Porphyra HTR2 gene shows higher survival and growth rates than the wild‐type strain after high‐temperature treatment. These results suggest that HTR2 may be relevant to the tolerance of high‐temperature stress conditions, and this Porphyra EST data set will provide important genetic information for studies of the molecular basis of high‐temperature tolerance in marine algae, as well as in Porphyra.     

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.     

Metabolomic Analysis Reveals Extended Metabolic Consequences of Marginal Vitamin B-6 Deficiency in Healthy Human Subjects

Marginal deficiency of vitamin B-6 is common among segments of the population worldwide. Because pyridoxal 5′-phosphate (PLP) serves as a coenzyme in the metabolism of amino acids, carbohydrates, organic acids, and neurotransmitters, as well as in aspects of one-carbon metabolism, vitamin B-6 deficiency could have many effects. Healthy men and women (age: 20-40 y; n = 23) were fed a 2-day controlled, nutritionally adequate diet followed by a 28-day low-vitamin B-6 diet (<0.5 mg/d) to induce marginal deficiency, as reflected by a decline of plasma PLP from 52.6±14.1 (mean ± SD) to 21.5±4.6 nmol/L (P<0.0001) and increased cystathionine from 131±65 to 199±56 nmol/L (P<0.001). Fasting plasma samples obtained before and after vitamin B6 restriction were analyzed by ¹H-NMR with and without filtration and by targeted quantitative analysis by mass spectrometry (MS). Multilevel partial least squares-discriminant analysis and S-plots of NMR spectra showed that NMR is effective in classifying samples according to vitamin B-6 status and identified discriminating features. NMR spectral features of selected metabolites indicated that vitamin B-6 restriction significantly increased the ratios of glutamine/glutamate and 2-oxoglutarate/glutamate (P<0.001) and tended to increase concentrations of acetate, pyruvate, and trimethylamine-N-oxide (adjusted P<0.05). Tandem MS showed significantly greater plasma proline after vitamin B-6 restriction (adjusted P<0.05), but there were no effects on the profile of 14 other amino acids and 45 acylcarnitines. These findings demonstrate that marginal vitamin B-6 deficiency has widespread metabolic perturbations and illustrate the utility of metabolomics in evaluating complex effects of altered vitamin B-6 intake.     

Multifractal Analysis for Nutritional Assessment

The concept of multifractality is currently used to describe self-similar and complex scaling properties observed in numerous biological signals. Fractals are geometric objects or dynamic variations which exhibit some degree of similarity (irregularity) to the original object in a wide range of scales. This approach determines irregularity of biologic signal as an indicator of adaptability, the capability to respond to unpredictable stress, and health. In the present work, we propose the application of multifractal analysis of wavelet-transformed proton nuclear magnetic resonance (¹H NMR) spectra of plasma to determine nutritional insufficiency. For validation of this method on ¹H NMR signal of human plasma, standard deviation from classical statistical approach and Hurst exponent (H), left slope and partition function from multifractal analysis were extracted from ¹H NMR spectra to test whether multifractal indices could discriminate healthy subjects from unhealthy, intensive care unit patients. After validation, the multifractal approach was applied to spectra of plasma from a modified crossover study of sulfur amino acid insufficiency and tested for associations with blood lipids. The results showed that standard deviation and H, but not left slope, were significantly different for sulfur amino acid sufficiency and insufficiency. Quadratic discriminant analysis of H, left slope and the partition function showed 78% overall classification accuracy according to sulfur amino acid status. Triglycerides and apolipoprotein C3 were significantly correlated with a multifractal model containing H, left slope, and standard deviation, and cholesterol and high-sensitivity C-reactive protein were significantly correlated to H. In conclusion, multifractal analysis of ¹H NMR spectra provides a new approach to characterize nutritional status.     

Metabolic Consequences of Chronic Alcohol Abuse in Non-Smokers: A Pilot Study

An alcohol use disorder (AUD) is associated with an increased susceptibility to respiratory infection and injury and, upon hospitalization, higher mortality rates. Studies in model systems show effects of alcohol on mitochondrial function, lipid metabolism and antioxidant systems. The present study applied high-resolution metabolomics to test for these changes in bronchoalveolar lavage fluid (BALF) of subjects with an AUD. Smokers were excluded to avoid confounding effects and compliance was verified by cotinine measurements. Statistically significant metabolic features, differentially expressed by control and AUD subjects, were identified by statistical and bioinformatic methods. The results show that fatty acid and acylcarnitine concentrations were increased in AUD subjects, consistent with perturbed mitochondrial and lipid metabolism. Decreased concentrations of methyl-donor compounds suggest altered one-carbon metabolism and oxidative stress. An accumulation of peptides suggests proteolytic activity, which could reflect altered epithelial barrier function. Two metabolites of possible microbial origin suggest subclinical bacterial infection. Furthermore, increased diacetylspermine suggests additional metabolic perturbations, which could contribute to dysregulated alveolar macrophage function and vulnerability to infection. Together, the results show an extended metabolic consequence of AUD in the bronchoalveolar space.     

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.

     

Metabolome-Wide Association Study of Neovascular Age-Related Macular Degeneration

Purpose

To determine if plasma metabolic profiles can detect differences between patients with neovascular age-related macular degeneration (NVAMD) and similarly-aged controls.

Methods

Metabolomic analysis using liquid chromatography with Fourier-transform mass spectrometry (LC-FTMS) was performed on plasma samples from 26 NVAMD patients and 19 controls. Data were collected from mass/charge ratio (m/z) 85 to 850 on a Thermo LTQ-FT mass spectrometer, and metabolic features were extracted using an adaptive processing software package. Both non-transformed and log2 transformed data were corrected using Benjamini and Hochberg False Discovery Rate (FDR) to account for multiple testing. Orthogonal Partial Least Squares-Discriminant Analysis was performed to determine metabolic features that distinguished NVAMD patients from controls. Individual m/z features were matched to the Kyoto Encyclopedia of Genes and Genomes database and the Metlin metabolomics database, and metabolic pathways associated with NVAMD were identified using MetScape.

Results

Of the 1680 total m/z features detected by LC-FTMS, 94 unique m/z features were significantly different between NVAMD patients and controls using FDR (q = 0.05). A comparison of these features to those found with log2 transformed data (n = 132, q = 0.2) revealed 40 features in common, reaffirming the involvement of certain metabolites. Such metabolites included di- and tripeptides, covalently modified amino acids, bile acids, and vitamin D-related metabolites. Correlation analysis revealed associations among certain significant features, and pathway analysis demonstrated broader changes in tyrosine metabolism, sulfur amino acid metabolism, and amino acids related to urea metabolism.

Conclusions

These data suggest that metabolomic analysis can identify a panel of individual metabolites that differ between NVAMD cases and controls. Pathway analysis can assess the involvement of certain metabolic pathways, such as tyrosine and urea metabolism, and can provide further insight into the pathophysiology of AMD.     

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.