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Metabolomics

Metabolomics. Metabolome Reflects the State of the Cell, Organ or Organism. Change in the metabolome is a direct consequence of protein activity changes Not necessarily true for genomic, proteomic or transcriptomic changes

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Metabolomics

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  1. Metabolomics Metabolome Reflects the State of the Cell, Organ or Organism • Change in the metabolome is a direct consequence of protein activity changes • Not necessarily true for genomic, proteomic or transcriptomic changes • Disease, environmental factors, Drugs, etc., perturbs the state of the metabolome • Provides a system-wide view of the organism or cell’s response

  2. NMR Metabolomics Overview Prepare the cells, tissue or biofluids Harvest the metabolome Collect the NMR data Analyze the NMR data Analyze the metabolite changes

  3. NMR Metabolomics Data One-dimensional 1H NMR spectrum Two-dimensional NMR spectra

  4. 2D 1H-13C HSQC Experiment – workhorse of metabolomics • Correlates all directly bonded 13C-1H pairs • generally requires 13C-labeling (1.1% natural abundance)

  5. 2D 1H-1H TOCSY Experiment – workhorse of metabolomics • Correlates all 3-bonded 1H-1H pairs in a molecules

  6. NMR Metabolomics Process

  7. Differential NMR Metabolomics Inactive Drug Active & Selective Drug Active Against Wrong Protein Active & Not Selective Monitor in vivo protein and drug activity Forgueet al. (2006) J. Proteome Res. 5(8):1916-1923 Halouska & Powers (2006) J. Mag. Res. 178:88-95

  8. NMR and Multivariate Statistics Extreme Sensitivity to Experimental Differences • Want PCA Clustering to Result from Metabolome Change NOT Experimental Variability • EVERYTHINGshould be a CONSTANT between samples or the study is invalid

  9. Differential NMR Metabolomics ATP #2 ATP-glucose ATP-glucose ATP ATP Remove Noise ATP #9 Negative Impact of Noise in NMR PCA Clustering Single NMR Sample with repeat data collection Higher PC2 dispersion (-10 to 10) and an outlier ATP #2 ATP #9 lower PC2 dispersion (-4 to 2)

  10. Differential NMR Metabolomics The Role of NMR Signal-to-Noise in PCA Clustering Increasing Number of NMR Scans (S/N)

  11. Differential NMR Metabolomics How to Quantify the Statistical Significance of Cluster Separations? • Analyze Metabolomic Data Using Tree Diagrams • Calculate distances between cluster centers  distance matrix • Apply Standard Boot-Strapping Methods • Randomize selection of cluster members to determine cluster center • Generate 100 different distance matrices  100 different trees  consensus tree • Bootstrap number -> how many times the consensus node appears in the set of 100 trees

  12. Differential NMR Metabolomics Bootstrap Number and Statistical Significance of Cluster Separations • Larger the Distance Between Clusters More Significant • Larger bootstrap • or smaller p-value • > 50% is significant • More Data Points Easier to Distinguish Between Clusters • more data points (solid line)

  13. Sample Replicates Affects Class Distinction 6 8 10 Increasing number of replicates • Significant increase in statistical significance of cluster from a modest increase in number or replicates

  14. Ellipses and Tree Diagrams Define Classes • P-value on each node identifies statistical significance (< 0.001) of cluster • Ellipses represent 95% confidence limits from a normal distribution

  15. Differential NMR Metabolomics Metabolite Identification S-plots loadings • Orthogonal partial least squares discriminant analysis (OPLS-DA) • a non-linear variant of PCA that minimizes class (group) variations • S-plots and loadings identify which “bins” (NMR chemical shifts – metabolites) are strongly correlated with class separation

  16. Differential NMR Metabolomics Metabolite Identification Grow cells in the presence of a 13C-labeled metabolite Only observe metabolites derived from the 13C-labeled metabolite provided to the cells Overlay of 2D 1H-13C HSQC spectra for wild-type (red) and aconitase mutant (black)

  17. Convert Peak Intensities to Concentrations (HSQC0) Our 2D 1H-13C HSQC calibration curve Hu et al. (2011) J. Am. Chem. Soc. 133:1662-1665

  18. Convert Peak Intensities to Concentrations (HSQC0) Can now compare changes between metabolites

  19. Convert Concentrations to Heatmap • Provides two-levels of hierarchal clustering • Identifies replicates with same overall changes • Identifies metabolites with correlated changes • between replicates • Provides a simple view of a large amount of data • Calculated with a statistical package, like R http://www.r-project.org/

  20. Differential NMR Metabolomics Metabolite Network Mapping (Cytoscape) Metabolites increased (red), decreased (green) or unperturbed/undetected (grey)

  21. Differential NMR Metabolomics Traditional Metabolic Pathway

  22. Some Final thoughts • A number of different analytical methods can be used to analyze the metabolome • NMR, GC-MS, LC-MS, CE-MS, FTIR, etc. • A variety of statistical techniques can be used to analyze metabolomics data • PCA, PLS, OPLS-DA, HCM, SOM, SVN, etc. • Can combine multiple datasets (NMR and MS) for multivariate statistical analysis • Can incorporate proteomics, genomics and any other data source with metabolomics data to generate system-wide view of the organism or cell response

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