Use of metabonomics in human health

1. Use of metabonomics in human health Inbal Dangoor

2. Introduction • Since the completion of the human genome sequencing project, the main goals of functional genomics have been to determine the function of the products of newly identified genes, as well as to determine those that might be therapeutically targeted. • Combined effort of different ‘omics’ approaches. • Traditional medicine measure metabolites because of their fundamental regulatory importance in biochemical pathways. • However, metabonomics leave behind the reductionist method of investigating single component effect on a biological system for a more holistic approach of exploring the molecular details of multiple factors on an entire biological organism.

3. Our knowledge on various cellular conditions during disease situations can be translated to promote human health through the use of metabolomics. • For instance, Tumor cells possess the potential for proliferation, differentiation, cell cycle arrest, and apoptosis. There is a specific metabolic phenotype associated with each of these processes that is characterized by the production of energy and special substrates necessary for the cells to function in that particular state.

4. The future (hopefully…) • Personalized medicine throughout the life time of each individual. • Protect health and prevent disease by prediction of future problems, rather than solely diagnosing and reversing existing disease. • The main tools: identify biomarkers and evaluate metabolic disorders early enough. • The single biomarker approach to disease diagnosis (e.g., cholesterol for potential heart disease) will be replaced by direct assessment of health and, most importantly, assessment will be comprehensive.

5. Definitions • Metabolomics: “the complete set of metabolites/low-molecular-weight intermediates, which are context dependent, varying according to the physiology, developmental or pathological state of the cell, tissue, organ or organism”. • Metabonomics: “the quantitative measurement of the multivariate metabolic responses of multicellular systems to pathophysiological stimuli or genetic modification”.

6. Applications and achievements of metabonomics in health so far • Diagnosis and classification of diseases (tumor types) • Disease state (time course progression) • Learn pathological mechanisms and identify new biomarkers • Responses to treatment (efficiency, toxicity), and drug design (decrease development time) • Generating (partial) databases: HumanCyc,NIST, tumor metabolome database, METLIN (human biofluid), Interpret NMR database (metabolic profiles of tumors).

7. Metabolic biomarkers of tumors Griffin et al., Nature reviews 4, 2004.

8. The major metabolic disorders screened for in newborns using MS Want et al., ChemBioChem 6, 2005.

9. Advantages of metabonomics in human health • Cheap (1$ apiece ??) • Fast • High-throughput • Fully automated • Ideal for following rapidly changing phenotype • Minimally invasive screening • Broad view of a patient status

10. Methods • The biological material: • Blood serum • Blood plasma • Urine • Tissue extracts or biopsy - invasive • CSF (cerebrospinal fluid) - invasive • Milk • Saliva • Techniques: • -NMR (very common due to many pioneering works) • -GCMS, LCMS (increased sensitivity) • -HRMAS 1H NMR (High resolution magic angle spinning, intact tissues) • -MRS (magnetic resonance spectroscopy, in tissues within living organisms) • -FT-IR • -TLC • -Metabolite array • Data mining: • Pattern recognition software is needed to associate specific profiles with different cell types, tumor types or a stage of treatment.

11. Example 1 - Diagnosis • Screening human populations for coronary artery disease, a common metabolic disorder, using NMR of serum, instead of expensive and invasive techniques such as angiography. • NMR spectra of individuals who have been extensively characterized by angiography and algorithms based on conventional risk factors. This allowed the direct comparison of pattern recognition techniques with standard means.

12. Discrimination between healthy and sick samples healthy sick Pattern recognition model was generated using multivariate classification, PLS-DA (Partial least squares-discriminant analysis) Without (d) or with (f) OSC (orthogonal signal correction, used to optimize the separation by subtracting variation) The spectral regions that contribute to the discriminations were lipids (LDL, HDL, VLDL) and choline. The derived model predicted correctly 92% of unknown samples. Brindle et al., nature medicine 8, 2002.

13. Comparison of patients with different severity of coronary atherosclerosis Brindle et al., nature medicine 8, 2002.

14. Example 2 - metabolic profiles of cancer cells • HIF-1 (hypoxia inducible factor) is a heterodimeric transcription factor that is upregulated in several cancer types. It cause an increase in glycolysis rate, growth factor production and angiogenesis induction. • Studying the effect of HIF-1deficiency on tumor metabolism and growth using. • Observed reduced rates of growth in HIF-1 deficiency cells. • Results: increase of phosphorylated cell membrane constituents and reduction of ATP content because of reduced rate of glycolysis. • This is a good example for the use of metabolomics in studying metabolic pathways that could be targeted therapeutically.

15. Catabolism of phospholipid membrane components is elevated in vivo in deficient Hepa c4 tumors PDE phosphodiester PDE/Pi Higher ratio Griffiths and Stubbs, Advan Enzyme Regul 43, 2003.

16. choline glycolysis betaine glycine lipids membranes ATP Alteration in metabolic pathways in HIF- deficient tumors 3-phosphoglycerate Nucleotide synthesis PDE/Pi

17. Polyunsaturated lipids Example 3 - NMR spectra at different time points after gene therapy induced apoptosis in tumors • Gene therapy approach was carried in order to induce apoptosis. • Apoptosis in glioma cells might be specifically associated with an increase in the amount of unsaturated lipids. • Investigate changes in lipid biochemistry: distinguishes the chemical groups present — for example, the identity and amount ofspecific lipids. • Lipids that are released as part of the apoptotic process can be monitored and provide non-invasive way to monitor effects of cancer treatment in patients in vivo. Griffin et al., Nature reviews 4, 2004

18. Example 4 - personalized medicine • Administration of galactosamine hydrochloride to a group of ten rats. • Using NMR they observed liver effects that were very variable, enough that the rats could be classified as either ‘responders’ or ‘non-responders’. • PCA analysis showed some discrimination between responder and non-responder groups in terms of their pre-dose metabolite profiles. PCA (predose) Andrew et al., Nature letters 440, April 2006.

19. NMR spectra R<0 R>0 R<0 Information on individual responses to xenobiotics might be contained in the metabolite patterns of pre-dose biofluids • Mole ratio of paracetamol glucuronide to paracetamol (G/P) was found to be the most convincingly predicted of the various postdose metabolite quantities. • PLS model showed positive correlation (r = 0.48) between G/P and the integral of the 5.06–5.14 region of the pre-dose NMR spectra, and negative correlations (r = -0.56 and -0.54) between G/P and the integrals of the 8.98–9.10 and 0.50–0.86 regions of the pre-dose spectra. • The predictive model for G/P is statistically significant. PLS model predose postdose Andrew et al., Nature letters 440, April 2006.

20. Metabolomics in human nutrition • Nutrients are traditionally known as chemical substances that are consumed from food and needed by the body for growth, maintenance, and repair of tissue. • Essential nutrients are not formed metabolically within the cell and must be present in food that is ingested, whereas nonessential nutrients can be synthesized by the cell. • Nutrients can modulate many pathways, for example: DNA repair, altering carcinogen activation, angiogenesis and signal transduction. • They can cause diseases or prevent them!

21. Diseases of modern civilization, such as diabetes, heart disease, and cancer, are known to be influenced from dietary patterns. • Experimental evidence indicates that dietary constituents, particularly phytochemicals and some minerals and vitamins, can modulate the complex multistep, multistage carcinogenesis process at the initiation, promotion, and progression phases of neoplasia. • Examples: flavons (heart disease), stannols (cholesterol metabolism), soy-based estrogen analogues (cancer). • The study of global metabolic profiles is only starting, thus current reports are mostly theoretical.

22. Cancer prevention using appropriate diet in children Abnormal cell growth Liang et al., American Society for Nutritional Sciences, 2003

23. Developing a protocol for metabolite extraction from human serum for LC-MS studies

24. Deproteinization • Some metabolites are noncovalently bound to proteins, in addition, proteins can dominate the LC analysis and cause signal suppression of less abundant metabolites. Therefore, the applicability of an extraction method may be determined by the number of metabolites recovered as well as the efficiency of protein removal. • Deproteinization can be achieved using organic solvents such as methanol and acetonitrile, lowering pH with acid, or denaturation using heat. • Strong acids adversely affect chemically unstable compounds, and too much salt in samples can impair the MS analysis.

25. 6 kD protein Missing metabolites Organic solvents were the most efficient and reproducible for both metabolite recovery and protein precipitation Organic solvents Heat Acid • LC-MS chromatograms of serum samples obtained by different extraction methods. • Avg. % RSD represents average RSD value (relative standard deviation) from six replicates for all detected features within each method. • RSD is an average of several thousand metabolite features, and individual metabolite RSD values can vary greatly within each method. Want et al., Anal Chem 78, 2006.

26. Number of Reproducible Features Heat+Acid MeOH A reproducible Feature is defined as a metabolite feature that was detected in at least five out of six LC-MS runs for a given method. Want et al., Anal Chem 78, 2006.

27. Current limitations of metabonomics in health studies • Variations between samples due to extraction methods, diet, sex, stresses, age, tissue type, natural microflora and infections, genetic background etc… Therefore it is not possible to add isotope-labeled internal standard for all detected compounds, which would increase the accuracy and reproducibility of the analysis. • The number of metabolites that can be detected. High concentrated metabolites dominant the spectra (need more sensitivity). • Lack of comprehensive databases (in progress).

28. Technical improvements • Improved sensitivity by combined use of NMR and LC (less co-resonant peaks, detect low concentrated compounds). • 13C NMR spectroscopy, which has high resolution but low sensitivity, will be usable due to Cryoprobes (4oK). Cooling the NMR probe will allow acquisition of the spectra, and improved sensitivity. • Metabolite array: 96-well plate assay system for phenotyping (different assay mixtures). • Increased automation will allow the rapid generation of databases to assist in patient screening. • Increased use of HRMAS 1H NMR spectroscopy: ideal for following key metabolic events in the cytosol, and can be applied directly to the tissue. • Improved statistical tools. • Protocols optimization from specific biofluid for precise goals.

29. Future directions • Design different clinical experiments using whole-plant food extracts and high throughput assays to determine the mechanistic health benefits derived from fruits and vegetables. • Genetically modified food that will be designed to treat specific disease (bonus: overcome the population fear from transgenes…) • Optimal: personalized medicine that will identify for each person his health condition and predict future complications. • If an individual is assessed before the development of disease, analyzes must be able to predict the likelihood of future diseases within the context of an individual’s overall, leading to recommendation of appropriate means to avoid deleterious health directions, with minimal side effects.

30. Conclusions • Metabolic analysis is a promising approach to identify biomarkers that could be used in the non-invasive monitoring of the human health, by detecting early stages of illness and applying the right treatment. • The advantage of metabonomics: get a global view of multiple effectors that are related to specific physiological status. • Protect health and prevent disease by prediction of future problems, rather than solely diagnosing and reversing existing disease. • Personalized medicine throughout the life time of each individual, that will eventually improve the health of the whole population.

31. THANK YOU FOR YOUR PATIENCE AND TAKE CARE OF YOUR HEALTH!!!