Metabolomics Research & Consultancy CJ Alexander, CA Hackett & JW McNicol With collaborations below as indicated from: 1The James Hutton Institute, UK 2Teagasc, Crops Environment and Land Use Programme, Ireland 3Aarhus University, Denmark Metabolomics is a rapidly developing field and scientists at The James Hutton Institute apply various technologies in plant breeding, quality & nutrition. BioSS provides consultancy support in design & analysis. We also investigate opportunities for further developments with new statistical techniques & data types. Conventional Statistical Analysis The statistical approach BioSS uses for the analysis of metabolomics datasets involves two strands: Multivariate where a PCA is used to explore the larger sources of variation amongst the experimental samples and identify the metabolites responsible for these differences Univariate where ANOVA is performed individually on each metabolite. A False Discovery Rate calculation allows the specific limit for significance to be chosen for those metabolites showing a significant treatment effects. A Hierarchical Cluster Analysis then illustrates the relationships among this subset Developments in Statistical Analysis Techniques such as ANOVA Simultaneous Components Analysis (ASCA) may provide an improvement on our conventional approach Essentially, for each ANOVA model term (main effects & interactions), ASCA performs a PCA on the table of effects. Example: potato tuber life cycle. A single factor experiment with 11 levels through life cycle stage ASCA biplot below shows variation of metabolites (marked ‘o’) and the vectors show the factor level loadings. PC1 distinguished metabolites which are increasing from those which are decreasing over the life cycle Linking Metabolomic & Transcriptomic Datasets LVT Shepherd1, PE Hedley1, JA Morris1, D McRae1 & JA Sungurtas1 HV Davies1 In a potato tuber bruising experiment, both microarray & metabolomic responses were available for the same samples in the same designed experiment. The treatment factors were: Genotypes: 3 Storage Times: 3 durations Which genes affect which metabolites? ANOVA performed separately for each of the genes & metabolites Eight possible groupings of treatment factor significances e.g. one grouping would be those responses which were significant for Genotype & Storage Time but no significant interaction Partition the genes & metabolites into their respective significance groups For each metabolite in a significance group, predict the response using a regression tree with all the genes Prune the regression tree 0.3 PC1 0.2 0.4 1a 1b 1c 1d 2a 2b 3 4a 4b 5 6 0.1 0.2 PC2 (19%) 0.0 0.0 -0.1 -0.2 -0.2 -0.2 -0.1 0.0 0.1 0.2 0.3 Stage PC1 (31%) mQTL Analysis A Foito1, S Byrne2,3, D Stewart1 & S Barth2 BioSS have analysed mQTL experiments with JHI collaborators on several crops (blackcurrant, raspberry, oats & potatoes). Shown here is a ryegrass mapping population experiment consisting of: Parent plants (P_m, P_f); F1 plants; 325 F2 offspring Phase 1: two replicate blocks were grown in the field Phase 2: metabolites measured in laboratory with GCMS Non Polar analysis. Such multi-phase experiments are becoming more common and benefit from BioSS input for the careful design and statistical analysis required For the metabolite Octacosanol, a graph of the LOD score profile identified a large QTL on linkage group 4 Consistent differences can be seen between the recessive QTL genotype b and the dominant genotype d for each batch Linkage Group 4 LOD score profile Means for batch number at different levels of D561623: Generation F2 0.5 25 0.0 20 -0.5 15 Octacosanol -1.0 Future Work Pre-processing of spectra Evaluation of other multivariate methods such as Bayesian Independent Component Analysis Utilising metabolic pathway databases (such as KEGG) LOD Score 10 -1.5 -2.0 5 -2.5 5 10 15 | | | Average sed Batch number D561623 b The potato bruising experiment received funding from the United States Department of Agriculture The ryegrass experiment received funding from Irish Department of Agriculture, Fisheries and Marine D561623 d