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2. Large Scale Approaches to the Study of Metabolite Levels Prof:Rui Alves ralves@cmb.udl.es 973702406 Dept Ciencies Mediques Basiques, 1st Floor, Room 1.08 Website of the Course:http://web.udl.es/usuaris/pg193845/Courses/Bioinformatics_2007/ Course: http://10.100.14.36/Student_Server/

3. Genome, Proteome, now what? Metabolites!!!

4. Why Studying Metabolites Directly? • Just because a protein is changing its activity do levels of product/substrate change? • What happens with non-covalently bound regulators? • What about the levels of the different metabolites? • Which metabolites do cell regulate for each response? • How can we know what to change in the cell for biotechnological purposes of producing some metabolite (e.g. antibiotics) if we don’t know how the levels of these metabolites change?

5. From metabolites to metabolomics • Metabolite is an intermediate of metabolism • Metabolome is the metabolic complement (metabolite pool) of a cell, tissue or organism under a given set of conditions • Metabolomics is the study of the metabolome

6. The Metabolome • The Metabolome • Metabolite complement of a proteome • Variable • In different cell and tissue types in same organism • In different growth and developmental stages of organism • Dynamic • Depends on response of genome & proteome to environmental factors • Disease state • Drug challenge • Growth conditions • Stress

7. What can we do with Metabolomics • Metabolomics enables: • Qualitative and quantitative display of metabolite concentration patterns • Assessment of global changes • Comparative analysis of samples • Provides information from which biological hypotheses may be developed

8. The procedure Tissue or biofluid sample 1. Mass spectrometry 2. 1H NMR spectroscopy Bioanalytical tools Measure the metabolite profile Statistical bioinformatic tools Explore profile to gain mechanistic insight into the biological response Treat profile as ‘fingerprint’ for classification purposes (applied/clinical) (basic research)

9. Which metabolites can be observed by NMR? • Low molecular weight organic metabolites: • Amino acids • Organic acids and bases • Nucleotides • Carbohydrates • Osmolytes • Lipids (broad non-specific resonances)

10. NMR is possible because of Nuclear Spin • All nuclei that contain odd numbers of protons or neutrons have an intrinsic magnetic moment and angular momentum • Nuclear spin angular momentum is a quantized property of the nucleus in each atom, which arises from the sub-atomic properties of neutrons and protons • The nuclear spin angular momentum of each atom is represented by a nuclear spin quantum number (I) • All nuclei with even mass numbers have I=0,1,2… • All nuclei with odd mass numbers have I=1/2,3/2... • NMR is possible with all nuclei except I=0, but I=1/2 has simplest physics Biomolecular NMR  primarily1H, 13C, 15N (31P)

11. Magnetic field generator (how much) (frequency: what compound) The experiment Marked metabolite Organism Organism Chemical shift is how much the spectrum changes with respect to a specific well known ground state

12. What the hell is chemical shift? • All nuclei have a specific resonance spectrum • This spectrum changes depending on the environment of an atom • Thus the 1H spectrum in CH4 is different from that in 1H2

13. A few simple 1H spectra

14. What about more complicated molecules? • Things get very messy • Subspectra become entangled 1H NMR Spectrum of Ubiquitin

15. Magnetic field generator Magnetic field generator What to do about this? • Use a different magnetic pulse to measure another spectrum! 2D NMR!!!! Cs ppm (pulse 1) Cs ppm (pulse 2)

16. Use 2D NMR to Resolve Overlapping Signals Sub-spectra overlapped 1D ppm (pulse 2) Concept can be extended to N-dimensional NMR!!! ppm (pulse 1) Crosspeaks resolved! 2D ppm (pulse 2) Coupled spins ppm (pulse 1)

17. Hb Ha HN Multi-Dimensional NMR If 2D cross peaks overlap go to 3D or 4D …..

18. Rule of thumb • If two groups are different then you can always resolve the spectrum by applying a sufficiently high magnetic field

19. Data Analysis • Fitting 5-10 rounded peaks is trivial, fitting 1000+ sharp peaks is not, i.e. dense matrix problem with very high probability of cumulative rounding errors and singularities(LLSOL - Stanford) • Peak positions & shapes dependent on salt, pH, temperature, ligands, ligand/ion interactions, shimming, signal-to-noise digital resolution, phasing, field strength, etc. etc.

20. Metabolome Pipeline • Multi-disciplinary teams required • Meta-data (data about data) extremely important • Data storage (database) important for large datasets • Brown et al, Metabolomics, 2005, 1, 39-51 • Spectrum identification can be made using for example Fourier Transforms • Problems similar to those for “ID”ing mass spec spectra for proteins

21. Example • Metabolomic changes due to polution in fresh water japanese fish

22. late stage embryos 0.4 Tyrosine Creatine Histidine Lactate Alanine ATP 0.2 PC 1 loadings 0.0 -0.2 early stage embryos -0.4 10 9 8 7 6 5 4 3 2 1 Chemical shift (ppm) PCA scores plot: Summarizes changes in NMR-visible metabolome throughout embryogenesis in Japanese medaka 5 4 6 3 7 PC2 score 2 8 Developmental trajectory Day 1 Fertilization Hatch PC1 score

23. Developmental toxicity of trichloroethylene (TCE) in Japanese medaka • Expose medaka embryos to TCE throughout embryogenesis. • Preserved replicates of ~100 eggs on day 7 of development.

24. PCA scores plot: Dose-dependent effects of TCE on medaka metabolome 5 4 6 3 7 PC2 score 2 Trajectory? 3 ppm TCE 46 ppm TCE Day 7 controls 8 Day 1 PC1 score

25. Perturbations to normal developmental trajectory Normal development PC2 score stage specific toxicity identified for targeted gene expression studies Permanent toxicant-induced perturbation PC1 score C. A. Pincetich, et al, Comp. Biochem. Physiol. C140, 103-113 (2005).

26. Advantages of metabolomics • Changes in the levels of individual enzymes: • expected to have little effect on measured metabolic fluxes • do have significant effects on the concn of metabolites • ‘Downstream’ result of gene expression • changes in metabolome are amplified relative to changes in the transcriptome and the proteome.

27. Advantages of metabolomics • Metabolomics is complementary to transcriptomics and proteomics, but closer to the phenotype • Number of metabolites expected to be smaller than number of genes or proteins (S. cerevisiae 6000 genes and 600 metabolites) • Metabolomic analyses can cost up to two thirds less than other ‘omic’ analyses (more appropriate for high-throughput/large sample number studies than proteomics and transcriptomics) Plants have hundreds of thousands different chemical compounds, many still unknown!!!

28. Disadvantages • Sometimes chemistry changes, depending on isotope composition • Less sensitive than Mass spec

29. Central repositories • No central repository that i know of.

30. Post-genomic Era of Biology Genotype Genome Transcriptomics Metabolomics Gene expression Genomics Metabolism Proteins Environmental stressors Molecular Phenotype Proteomics