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

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

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

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

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

NMR Metabolomics Process

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

NMR and Multivariate Statistics Extreme Sensitivity to Experimental Differences Want PCA Clustering to Result from Metabolome Change NOT Experimental Variability EVERYTHING should be a CONSTANT between samples or the study is invalid NMR experimental parameters temperatureBuffer (pH)shimmingTuning & matching lock90 o pulse acquisition parameters Spectral width Data pointsRecycle time Acquisition time Solvent removal Receiver gain processing parameter Zero fillingBaseline correction Window function Linear prediction Solvent removal phasing

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

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

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

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)

Sample Replicates Affects Class Distinction Increasing number of replicates Significant increase in statistical significance of cluster from a modest increase in number or replicates

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

Metabolite Identification Differential NMR Metabolomics  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 S-plots loadings

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

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

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

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

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

Differential NMR Metabolomics Traditional Metabolic Pathway

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