Transcript IB496-May 2
Metabolomics, spring 06 Hans Bohnert ERML 196 Metabolomics Essentiality Today’s discussion topics [email protected] 265-5475 333-5574 http://www.life.uiuc.edu/bohnert/ class May 2 Whole plants Organs Tissues Cells Fluids related species Ecotypes Mutants Transgenics RILs Morgenthal et al. (2006) Metabolic Networks in Plants: Transitions from pattern recognition to biological Interpretation. BioSystems 83, 108-117. Nunes-Nesi A et al. (2005) Enhanced photosynthetic Performance and growth as a consequence of Decreasing mitochondrial malate dehydrogenase Activity in transgenic tomato plants. Plant Physiol. 137, 611-622. To find a metabolic character that can serve as the Rosetta stone explaining phenotype To find unknown signals, new pathways, better drugs Our technical ability to isolate and identify metabolites, even to obtain data on metabolic flux, is not matched by our understanding of plant metabolism, cell-specific biochemical events or the structure of metabolic pathways and their integration across cells and tissues! • The challenge is to understand in vivo metabolite dynamics in complex mixtures and to reconcile the data with the structure of metabolism. • As in “transcriptomics”, we need ways to analyze the data in a statisticallly sound way by computational methods • PCA, LDA, and other unsupervised learning algorithms • Biomarkers for disease deficiency, transgenic modification – signature events • Correlations to unravel nodes in networks • Correlations between metabolites Correlation maps for selected metabolites a fingerprint of different networks • constant conditions • make light variable • fluctuations propagate through pathway A simplified Calvin Cycle plus cytosolic SPS Ch – chloroplast M - cytosol pairwise metabolite comparisons light as a time-dependent random variable this pathway goes towards sucrose Potato tuber (43 samples), leaf (34), Arabi leaf (240, tobacco leaf (29) Divergence by species/tissue preserved correlations P – 0.001 PCA analysis of the same dataset 1st three p.c 1st component – glucose weighted A model simulation with 2 steady-state solutions sink source B inhibits its degradation dep. [B] Technical error compared with variability Technical variability of Arabi leaf material (many repeats) vs. variability in the datasets s.d./ mean Nunes-Nesi et al. (2005a) Plant Physiol. 137, 611. mMDH down biomass and yield up Simple story! Why is it important? Nunes-Nesi A, Carrari F, Lytovchenko A, Fernie AR (2005b) Enhancing crop yield in Solanaceous species through the genetic manipulation of energy metabolism. Biochem. Soc. Transactions 33, 1430 - how? (C) consequences of deficiency GLDH (l-galactono1,4-lactone DH) up (A) assimilation rate and (B) fruit yield in the antisense mitochondrial malate dehydrogenase lines (AL) of S. lycopersicum and in the Aco1 mutant of S. pennellii. Aco1 – unknown – has increased adenylate levels Nunes-Nesi et al (2005) Enhancing crop yield in Solanaceous species through the genetic manipulation of energy metabolism. Biochem. Soc. Transactions 33, 1430-1434 plants deficient in stromal adenylate kinase (ADK) Other approaches – Sedoheptulose BPase ictB (E. coli) (CO2-accumulation) TPS PARP mMDH down respiration down Metabolites lots of changes ascorbate up precursor ASC biosynthesis – L-galactono-lactone Where lies the crucial difference that leads to increases in metabolites? Interpretations? metabolite competition ?? metabolite channeling ?? protection ???? protection of what ???? PARP – the miracle enzyme? PARP functions not only in the nucleus, it regulates the activity of an increasing number of enzymes. PARP – Its activity in non-repair functions of DNA/chromatin leads to the destruction of adenine nucleotide (phosphates) which leads to a decline in NAD/NADP. This, in a photosynthetically-active organism, leads to a decline in energy production. However, there must be more to the story – there are multiple PARP-like genes in mammals – fewer, but still a family, in plants. An eclectic Syllabus • metabolomics technologies • GC-MS profiling – six steps: extraction – derivatization – separation – ionization – detection – acquisition/evaluation • relative advantages of different technologies (LC, GC, TOF, MS-MS, NMR) • challenges: automation – analytic scope – trace compound calling - reproducibility and quantitative comparisons across platforms size and complexity of metabolite libraries • plant volatiles – tri-trophic interactions • static vs. dynamic metabolite profiling; stable isotopes - flux determinations sugars to fatty acids (Rubisco in green seeds), TPs to amino acids • integration of transcriptomics and metabolomics • the cold-metabolome – certainty from highly variable datasets (ecotypes/lines) • cell-specific reactions [animal] (how can we use plant cell cultures?) • fluids, cell, tissues, organs, species – different types of information • long-distance transport metabolomics • metabolomics – transcriptomics – QTLs (tomato – wild tomato crosses) • metabolic network construction • transgenic manipulations in energy-generating pathways • towards systems understanding • some discussions developed; is metabolomics just biochemistry under a different name? Have a great summer! HJB