Youngja Park

Nutritional Metabolomics: Progress in Addressing Complexity in Diet and Health

Nutritional metabolomics is rapidly maturing to use small-molecule chemical profiling to support integration of diet and nutrition in complex biosystems research. These developments are critical to facilitate transition of nutritional sciences from population-based to individual-based criteria for nutritional research, assessment, and management. This review addresses progress in making these approaches manageable for nutrition research. Important concept developments concerning the exposome, predictive health, and complex pathobiology serve to emphasize the central role of diet and nutrition in integrated biosystems models of health and disease. Improved analytic tools and databases for targeted and nontargeted metabolic profiling, along with bioinformatics, pathway mapping, and computational modeling, are now used for nutrition research on diet, metabolism, microbiome, and health associations. These new developments enable metabolome-wide association studies (MWAS) and provide a foundation for nutritional metabolomics, along with genomics, epigenomics, and health phenotyping, to support the integrated models required for personalized diet and nutrition forecasting.

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.

apLCMS--adaptive processing of high-resolution LC/MS data.

MOTIVATION Liquid chromatography-mass spectrometry (LC/MS) profiling is a promising approach for the quantification of metabolites from complex biological samples. Significant challenges exist in the analysis of LC/MS data, including noise reduction, feature identification/ quantification, feature alignment and computation efficiency. RESULT Here we present a set of algorithms for the processing of high-resolution LC/MS data. The major technical improvements include the adaptive tolerance level searching rather than hard cutoff or binning, the use of non-parametric methods to fine-tune intensity grouping, the use of run filter to better preserve weak signals and the model-based estimation of peak intensities for absolute quantification. The algorithms are implemented in an R package apLCMS, which can efficiently process large LC/ MS datasets. AVAILABILITY The R package apLCMS is available at www.sph.emory.edu/apLCMS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Vaccine Activation of the Nutrient Sensor GCN2 in Dendritic Cells Enhances Antigen Presentation

The Secret Life of a Vaccine Antigen-specific CD8÷ T cells play a central role in the adaptive immune response to viral infections and to cancer. Ravindran et al. (p. 313, published online 5 December) studied the successful yellow fever virus vaccine YF-17D to gain insight into its mechanism of action. The vaccine activated the nutrient deprivation sensor, GCN2 kinase, in dendritic cells. In transgenic mouse models, GCN2 activation promoted autophagy and antigen cross-presentation, enhancing the virus-specific CD8÷ T cell response. The findings suggest an important role for nutrient availability and autophagy in vaccine efficacy, which could aid more successful vaccine development. The success of the yellow fever vaccine is linked to the amino acid starvation pathway, which promotes adaptive immunity. The yellow fever vaccine YF-17D is one of the most successful vaccines ever developed in humans. Despite its efficacy and widespread use in more than 600 million people, the mechanisms by which it stimulates protective immunity remain poorly understood. Recent studies using systems biology approaches in humans have revealed that YF-17D–induced early expression of general control nonderepressible 2 kinase (GCN2) in the blood strongly correlates with the magnitude of the later CD8+ T cell response. We demonstrate a key role for virus-induced GCN2 activation in programming dendritic cells to initiate autophagy and enhanced antigen presentation to both CD4+ and CD8+ T cells. These results reveal an unappreciated link between virus-induced integrated stress response in dendritic cells and the adaptive immune response.

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.

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.

Dietary Sulfur Amino Acid Effects on Fasting Plasma Cysteine/Cystine Redox Potential in Humans

OBJECTIVE Oxidation of plasma cysteine/cystine (Cys/CySS) redox potential (E(h)CySS) has been associated with risk factors for cardiovascular disease in humans. Cys and CySS are derived from dietary sulfur amino acids (SAA), but the specific effects of SAA depletion and repletion on Cys/CySS redox indices are unknown. The present study examined the effect of dietary SAA intake level on free Cys, free CySS, and E(h)CySS in human plasma under fasting conditions. METHODS Healthy individuals aged 18-36 y (n = 13) were equilibrated to foods providing the RDA for SAA and then fed chemically defined diets without SAA (0 mg · kg(-1) · d(-1); n = 13) followed by SAA at levels approximating the mean (56 mg · kg(-1) · d(-1); n = 8) or 99th percentile (117 mg · kg(-1) · d(-1); n = 5) intake levels of Americans. Fasting plasma samples were collected daily during 4-d study periods and analyzed for free Cys, free CySS, and the E(h)CySS. RESULTS The SAA-free diet significantly (P < 0.05) decreased plasma-free Cys concentrations and oxidized E(h)CySS values after 4 d of SAA depletion. With SAA repletion at 56 mg · kg(-1) · d(-1), plasma-free Cys increased significantly and values for E(h)CySS became more reduced. Administration of a diet providing a higher dose of SAA (117 mg · kg(-1) · d(-1)) resulted in a significantly higher level of free Cys and a more reduced E(h)CySS. CONCLUSIONS These results show that free Cys and Cys/CySS redox potential (E(h)CySS) in fasting plasma are affected by dietary SAA intake level in humans. Significant changes occur slowly over 4 d with insufficient SAA intake, but rapidly (after 1 d) with repletion.

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.

Children with severe asthma have unique oxidative stress–associated metabolomic profiles

Children with severe asthma have unique metabolic derangements associated with oxidative stress-related pathways. These metabolic differences may contribute to corticosteroid insensitivity in severe asthma and may be novel targets for further study and therapeutic intervention.

Hybrid feature detection and information accumulation using high-resolution LC- MS metabolomics data

Feature detection is a critical step in the preprocessing of liquid chromatography-mass spectrometry (LC-MS) metabolomics data. Currently, the predominant approach is to detect features using noise filters and peak shape models based on the data at hand alone. Databases of known metabolites and historical data contain information that could help boost the sensitivity of feature detection, especially for low-concentration metabolites. However, utilizing such information in targeted feature detection may cause large number of false positives because of the high levels of noise in LC-MS data. With high-resolution mass spectrometry such as liquid chromatograph-Fourier transform mass spectrometry (LC-FTMS), high-confidence matching of peaks to known features is feasible. Here we describe a computational approach that serves two purposes. First it boosts feature detection sensitivity by using a hybrid procedure of both untargeted and targeted peak detection. New algorithms are designed to reduce the chance of false-positives by nonparametric local peak detection and filtering. Second, it can accumulate information on the concentration variation of metabolites over large number of samples, which can help find rare features and/or features with uncommon concentration in future studies. Information can be accumulated on features that are consistently found in real data even before their identities are found. We demonstrate the value of the approach in a proof-of-concept study. The method is implemented as part of the R package apLCMS at http://www.sph.emory.edu/apLCMS/ .