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Mapping metabolic data to genetic information “ Metabolomics” “Metabonomics” Simon C Thain

Mapping metabolic data to genetic information “ Metabolomics” “Metabonomics” Simon C Thain. A practical tool for trait discovery & analysis ?. How can Metabonomics help in trait analysis?. “calibrations” “fingerprint”. Model species to crops.

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Mapping metabolic data to genetic information “ Metabolomics” “Metabonomics” Simon C Thain

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  1. Mapping metabolic data to genetic information “Metabolomics” “Metabonomics” Simon C Thain A practical tool for trait discovery & analysis ?

  2. How can Metabonomics help in trait analysis? “calibrations” “fingerprint” • Model species to crops. • Better germplasm ID & trait definition (tools for breeding). • Mapping metabolite patterns to genetic information can provide direct cause and effect data.

  3. Using metabolomic data for trait identification and mapping • Quantification and qualification of “Phenotype” /complex traits and QTLs; reduces non-parametric descriptions e.g. “Vigour” “tolerance”. • Statistical association of multiple metabolite changes or “fingerprints” to alleles, point mutations (Tilling), markers, introgressed DNA etc.

  4. Chemometric/Statistics Using the right tools Reproducibility

  5. High-throughput

  6. Circadian Metabolomics Infrared fingerprinting FT-IC-MS Infrared imaging Environmental variables and sampling scales ?? Cycles Weather/Season Tissue 70% Look for vectors/patters; modulate conditions to “stimulate” the metabolomic consequences genotype >1% Under grant application Under trial with Varian UK

  7. DFA analysis identified the chemical fingerprints 14 forage grasses 1 Factor 2 Are they different ? Metabolomic fingerprinting of grass varieties by FT-IR

  8. Metabonomics relationships between forage grass varieties.e.g. Cell wall carbohydrates What's different ? Genotypes clusters – rapid, quantitative cheep!

  9. Complex trait analysis via “Reverse data modelling” Factor loadings plotted from Calibration model Tools for breeding e.g. dry matter digestibility • Py-GC-MS • TIC data. Types of Lignin G-lignin fragments PLS-2 modelling of Py-GC-MS (TIC) data for DMD..

  10. FT-IR metabonomic fingerprinting of Wheatnullisomic/tetrasomic lines • Wheat contains 3 genome sets (A, B, C) • of 14 chromosome each. • Group 1 chromosome are syntenic • (carry the same genes or alleles in the • Same order) • Metabolomic fingerprinting could detect • the loss of each alternate Chromosome 1 pair. What changes? If we know then new breeding targets can be identified Roy Goodacre, Lunned Roberts, David Ellis, Danny Thorogood, Stephen Reader Ian King

  11. Metabolomic mapping in Lolium/Festuca Chromosome 3 substitution lines

  12. 18  11 3/26  6   3  3/10  36 17   83 92  3/2 99   83 19  56   3/23 2/3  Parental Only polar fraction has currently been analysed

  13. Single gene effects have global consequencesdetectable by Metabolomics. Monocot seedling screening ?? Primary metabolite fingerprinting via NMR Metabolite mapping to SSR, SNP AFLP in isogenic/inbred lines. • Less likely to miss “invisible” phenotypes. • Large numbers of false positives

  14. Perspectives • Metabonomic trait analysis approaches can be rapid sensitive and informative of genotype & function. • Metabolomic analysis methods, need not always be confined to controlled environments.

  15. Acknowledgements: IGER Iain Donnison Phillip Morris Sarah Hawkins Cathy Morris Collaborators & matterials: MeTRo Romani Fahime (Aston) Deri Tomos (Bangor) Ian King (IGER) EPSRC, BBSRC

  16. Fourier-Transform InfraredSpectroscopy (“FT-IR”) IR light

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