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? 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. “calibrations” “fingerprint”

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.

Using the right tools Chemometric/Statistics FT-MS LC/GC-MS NMR X-ray TOF-MS FT-IR DI-MS Reproducibility

High-throughput

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

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

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

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

FT-IR metabonomic fingerprinting of Wheat nullisomic/tetrasomic lines Roy Goodacre, Lunned Roberts, David Ellis, Danny Thorogood, Stephen Reader Ian King 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

Metabolomic mapping in Lolium/Festuca Chromosome 3 substitution lines

Parental Only polar fraction has currently been analysed

Single gene effects have global consequences detectable 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

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.

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

IR light Fourier-Transform Infrared Spectroscopy (“FT-IR”)