1. Metabolomics Bob Ward German Lab Food Science and Technology

2. Genome- ….All the DNA Transcriptome- ….All the mRNA Proteome- ….All the proteins Metabalome ….All the metabolites “ ” “Metabolomics is a post genomic technology which seeks to provide a comprehensive profile to all the metabolites present in a biological sample.” (Taylor et. al, 2002)

3. Limitations of “ohmics” technologies Genomics Static picture Expensive Not for individuals Transcriptomics Need Genome (annotations) Correlated with proteome? Sampling issues splicing No info on modifications Proteomics Technologically challenging Need genome?

4. Metabolome Same metabolites for all organisms ~1k for organism vs 10k(genes) or 100k(proteins) Technology exists and is not too expensive Carbohydrate and Lipid info

6. Goal: Discrimination between related genotypes of Arabidopsis between Co10 and C24 (parent strains) between Co10 x C24 and progeny (F1) between (Co10 x C24) and (C24 x Co10) - Maternal line donates both mitochondria and chloroplast -Clear-cut realization of effectiveness -Potential to uncover biologically relevant info

7. Instrumental and Informatic Tools GC/MS-Separation/identification of polar metabolites in 1200 second run time AMDIS deconvoluting software MassLab to choose target ions R for statistics WEKA (standard neural network approach) Euclidean distance Principal Component Analysis

8. Data Work-Up Selection of reference chromatogram (F1) 8 individual samples for each genotype –no replicates Selection of target peaks/analytes (433) –normalized (mg analyte/wt sample)to internal standard (ribitol) –Allows for simple 2-D matrix

9. 201 metabolites identified in some detail (92 as molecular type and 109 by chemical property) High variance in low numbers corresponds to core metabolites

10. Co10 1-8 C24 9-16 Co10 x C24 17-24 C24 x Co10 25-32

11. Neural Network Analysis } P=0.27 Lack of samples precluded use of a training subset ‘Leave one out cross’ used for training Model judged by ability to classify remaining object (repeated for all objects) Allows for maximal use of data for validation when n is low

12. Clustering by Euclidean distance Co10 1-8 C24 9-16 Co10 x C24 17-24 C24 x Co10 25-32

13. Principal Component Analysis Used to tease out role of individual metabolites in discrimination Unsupervised multivariate analysis applied to functions of many attributes Transformation of large set of related values to smaller set of uncorrelated variables Attempts to express maximum variance in data PC’s are axes in multidimensional space Object characterized by distance to axis

14. PCA algorithm from MatLab 78% of variation of data from first 3 PC’s Variance of data explained by first few principal components

15. Principal Component Analysis Co10 and C24 differentiated except outlier F1 genotypes cluster together

16. Contribution of each variable to first PC Malate and Citrate- metabolites of TCA cycle

17. Relative peak area for metabolites malate and citrate Co10 contains outlier…..may explain misclassification

18. Other significant results Parental genotype removed from PCA analysis and F1’s discriminated by glucose and fructose Inference that the first PC differentiates parental line, and 2nd and 3rd differentiate F1 Malate and Citrate from TCA, glucose and fructose from chloroplasts

19. Conclusions Advances in technology will improve detection limits and will allow characterization of metabolites Formalized ontology needed to link chemical structure with pathways Metabolite profiling is an exciting new field which complements other non-hypothesis driven global analysis technologies Large amounts of informatic support to develop field and to correlate data from genomics, microarrays, and proteomics