Wu Guanzhong Forest

1. Challenges in interpretation of serum metabolomics data Matej Orešič VTT Technical Research Centre of Finland & Institute for MolecularMedicine Finland FIMM http://sysbio.vtt.fi/ Vlaardingen, July 2, 2010 Wu Guanzhong Forest

2. Factorsinfluencinghumanmetabolome Phenotype / Metabolome Environment Microbial genome Microbial genome Microbial genome Human genome Microbial genome Microbial genome Microbial genome Microbial genome Microbial genome

3. 70 3.5 Neuro- transmitters Energy metabolites Urea Cycle Lipids and fatty acids Amino acids Sugars *** ** 60 3.0 ** ** 50 2.5 * *** *** 40 2.0 Fold change (CONV-R vs GF) ** * Fold change (CONV-R vs GF) *** * * 30 1.5 ** * ** 20 1.0 * * ** ** ** * * *** 10 0.5 0 0.0 Urea Valine TAMP Ribose Maltose Glucose L-Proline Malic acid Tyramine Citric acid Dopamine L-Ornithine Rhamnose Cholesterol Palmitic acid Pyruvic acid Fumaric acid Linoleic acid Campesterol L-Tryptophan Glucuronic acid Hydrocinnamic acid 3-hydroxyphenylpropionic acid Serum metabolome affected by gut microbiota Velagapudiet al, J. Lipid Res. (2010)

4. Regulation of lipid metabolism by gut microbiota Velagapudiet al, J. Lipid Res. (2010)

5. Molecular markers of gut microbial composition and variation We will derive the metabolomic markers related to diet, health & gut microbiota variation based on data from 8 European cohorts. www.fp7tornado.eu

6. Immune system status & metabolomeComplex interaction not to be ignored Girlwhodevelopedoverttype 1 diabetes at age 9 years M. Orešič et al., J Exp Med (2008)

7. Longitudinal metabolic profile vs. autoimmunity Girlwhodevelopedoverttype 1 diabetes at age 9 years M. Orešič et al., J Exp Med (2008)

8. Longitudinal metabolic profile vs. autoimmunity Does the immune system also help to maintain (orcorrect) metabotypes? M. Orešič et al., J Exp Med (2008)

9. Where are the metabolites coming from? Example (liver): We performed hepatic venous catheterization studies in the fasting state and during a low-dose insulin infusion in nine subjects with various degrees of hepatic steatosis. Livertakesupendocannabinoids. Kotronen et al. (in press). Splanchicbalance: Hepatic vein vs. artery (before and afterinsulininfusion)

10. Example (brain)Loss of slow-wave sleep disrupts glucose homeostasis ± SEM (n=9 subjects) E Tasali et al, PNAS (2008)

11. Compartmentalization of metabolites M Jänis et al, Expert Opin. DrugMetab. Toxicol. (2008)

12. HDL lipids in subjects with high and low HDL-C Representative abundant lipids from different lipid classes Yetukuri, et al, J. Lipid Res. (2010)

13. Spatial distribution of lipids/metabolites may also matter Coarse-grainedmodel of HDL, as derivedfrom lipidomics in high vs. low HDL-C subjects. Yetukuri et al, J. Lipid Res. (2010)

14. Lipoproteins are not only lipids…Example: Multiple metabolites in different lipoprotein fractions correlate with insulin resistance (HOMA-IR index)

15. Serum lipidomics (and metabolomics),seen as an inverse problem Sysi-Aho et al., Bioinformatics (2007)

16. Serum metabolome Compartmentalization and spatialdistribution Mathematical modelsneeded! Systemicmetabolism Tissue-specificmetabolicadaptation Host and microbialgenomes, immune status Interpretation

17. Acknowledgements • Tuulia Hyötyläinen • Laxman Yetukuri • Vidya Velagapudi • Tuulikki Seppänen-Laakso • Jing Tang • Ismo Mattila • Heli Nygren • Erno Lindfors • Tapani Suortti • Airi Hyrkäs • Ulla Lahtinen • Anna-Liisa Ruskeepää • Leena Öhrnberg • Perttu Niemelä • Brudy Han Zhao • Peddinti Gopalacharyulu • Sandra Castillo • Marko Sysi-Aho • Liver studies • Hannele Yki-Järvinen • Anna Kotronen • Jukka Westerbacka • Gut microbiota • Fredrik Bäckhed • HDL simulations • Ilpo Vattulainen • Artturi Koivuniemi • HDL lipidomics • Marja-Riitta Taskinen • Matti Jauhiainen • DIPP study • Olli Simell, Mikael Knip, Heikki Hyöty, Jorma Ilonen, Riitta Veijola, Tuula Simell, Riitta Lahesmaa • Senior research staff and nurses • DIPP parents & children Funding: HFSP, JDRF, Tekes, EU FP6 & FP7, Academy of Finland http://sysbio.vtt.fi/