Application of NMR and MS based Metabolomics in Natural Product Science

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Comparative and non-targeted analysis of metabolome using various analytical methods -Choi, Hyung-Kyoon, presentation February 2010 at CPMB, TNAU

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  • Another key feature of metabolomics is that the metabolomics is generally performed with multivariate statistical analysis, such as principal component analysis or partial least squares-discriminant analysis. PCA is ---
  • Another key feature of metabolomics is that the metabolomics is generally performed with multivariate statistical analysis, such as principal component analysis or partial least squares-discriminant analysis. PCA is ---
  • I also conducted the metabolomic profiling and developed prediction model using NMR data for antioxidative andivities of Citrus fruits.
  • 5 to 10 meters in height, with long, sharp, solitary spines. The leaflets are entire or nearly so, sparingly hairy beneath and on margins, ovate oblong to elliptic, and 8 to 12 centimeters long. The obovate petioles are broadly winged. The flowers are white, very fragrant, and crowded in axillary, short racemes. The fruit is large, nearly spherical or obovoid, up to 20 centimeters or more in diameter. The rind, which is very thick and spongy, is fairly easily removed from the segments of the fruit. The pulp is pale yellow to pink or red, and sweet or acid, with large distinct vehicles.
  • This is the representative spectrum of Citrus fruit.
  • This is the comparison between observed FRSA predicted FRSA derived from the prediction model I developed.
  • It was possible to confirm the predictibility of the developed model using test set validation. It shows good correlation between observed and predicted FRSA values.
  • I also conducted the metabolomic profiling and developed prediction model using NMR data for antioxidative andivities of Citrus fruits.
  • Application of NMR and MS based Metabolomics in Natural Product Science

    1. 1. Application of NMR and MS based Metabolomics in Natural Product Science February, 2010 Choi, Hyung-Kyoon hykychoi@cau.ac.kr College of Pharmacy, Chung-Ang University Republic of Korea
    2. 2. MetabolomicsMetabolomics ○ Metabolome - total low molecular weight compounds in biofluid, cells, and tissue in living organism ○ Metabolomics - comparative and non-targeted analysis of metabolome using various analytical methods
    3. 3. Tools for metabolomicsTools for metabolomics Tools Pros Cons Robustness and Metabolite NMR reproducibility overlapping GC-MS Excellent sensitivity Need to derivatize GC X GC TOF Lower reproducibility LC-MS Excellent sensitivity than GC
    4. 4. Statistical methods (1)Statistical methods (1) ○ Principal component analysis (PCA) - Oldest and most widely used non-supervised multivariate statistical technique - Reduce the dimension of the original data set ○ Partial least squares-discriminant analysis (PLS-DA) - Supervised method rendering class to each sample - Clearer differentiation of each class and easier investigation of marker compounds
    5. 5. Statistical methods (2)Statistical methods (2) ○ Partial least squares-regression (PLS-R) - Correlate the X variables (eg. NMR spectra data) with Y variables (eg. Antioxidative activity) - Prediction model can be developed
    6. 6. Schematic overview of NMR –based metabolomicsHolmes et al. (2006) Planta medica 72:771-785
    7. 7. Timeline of major plant metabolomics papers
    8. 8. NMR spectra of tobacco in 50% MeOH fractionWild leafCSA leafWild veinCSA vein * There was no difference in CHCl3 fractions.
    9. 9. PC1 and PC2 scores of MeOH/water fractionPC1 and PC2 scores of MeOH/water fraction 20 WNL leaf 10 WIL leaf WSL leaf CNL leaf PC2 (38.2%) CIL leaf CSL leaf 0 WNL vein WIL vein WSL vein CNL vein CIL vein -10 CSL vein -20 -20 -10 0 10 20 PC1 (51.4%) * W: wild type plant, C: transgenic plant NL: non-inoculated leaf, IL: inoculated leaf, SL: systemic leaf
    10. 10. Loadingplot of all 1H-NMR signalsLoading plot of all 1H-NMR signals Sucrose 0.150 Glucose Chlorogenic acid 0.100 0.050 PC2 0.000 Alanine -0.050 SA Malic acid SAG -0.100 -0.140 -0.120 -0.100 -0.080 -0.060 -0.040 -0.020 0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140 PC1
    11. 11. w IS 1. Leu (a) 2. Lactate 2 3. Ala 4. Acetic acid 5 5. Choline 6. Gly 6 3 7. Val 7 4 1 8. Tyr 9 8 7 6 5 4 3 2 1 0 9. Phe 10. Formic acid 9 (b) 10 8 9.0 8.8 8.6 8.4 8.2 8.0 7.8 7.6 7.4 7.2 7.0 6.8 6.6 6.4 6.2 6.0 5.8Fig. 1
    12. 12. 0.02 RTPC3 (9.1%) NT, NM 0.00 RM -0.02 CT, CM -0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 PC1 (51.1%)
    13. 13. Fig 1
    14. 14. Metabolomic profiling and prediction modelMetabolomic profiling and prediction model development of Citrus Fruit using NMR anddevelopment of Citrus Fruit using NMR and MVAMVA NMR and antioxidative activity analysis  Mature and immature fruit  Peel and flesh
    15. 15. Citrus grandis OsbeckFamily : Rutaceae Immature stage Mature stage
    16. 16. Application of Metabolomics (1)Application of Metabolomics (1)•Biomarker development  Early biomarkers  Prognostic biomarkers  Diagnostic biomarkers  Late biomarkers of diseases such as cancers, diabetes, Alzheimers etc.
    17. 17. Pharma perspective on metabolomics• Looking for disease markersDisease Conventional Ideal scenario Animal Metabolic biomarker model profiling toolsDiabetes Increased Earlier marker High fat diet Lipid-MS, plasma/urinary pre-disease mice NMR/MS glucose onset profilingAtherosclerosis Lipoprotein Earlier marker Watanabe Lipid-MS, profiles pre-disease rabbits NMR/MS onset profilingAlzheimer Cognitive Markers of PS1 mice NMR/MS function test disease onset, profiling progressionSchizophrenia Behavioural Markers of Coloboma NMR/MS test disease onset, mice profiling progression
    18. 18. Consideration for Right Samples!Consideration for Right Samples!• Getting the right sample - plasma, serum, urine, tissue, saliva - Correlation with the disease• Control group - Gender - Ethnic - Age - Lifestyle - Nutritional and medical condition
    19. 19. Effect of acute dietary standardization on the urinary, plasma, and salivary metabolomic profiles of healthy humans Urine Saliva PlasmaMarianne et al. Am J Clin Nutr 2006;84:531–9.
    20. 20. Application of Metabolomics (2) Application of Metabolomics (2) Enhanced production of useful secondary metabolites by M/O, plant cell and tissue culture  Metabolic engineering 의 기초자료로 metabolomics 이 용  stress 에 의해 유도된 metabolic change 의 monitoring  대사경로 중 rate-limiting step 의 규명 유전체 기능 연구 (functional genomics)  외래유전자 도입에 의해 유발된 metabolic changes 의 규명  knockout mutation 에 의한 metabolic effects 의 규명
    21. 21. Application of Metabolomics (3) Application of Metabolomics (3)• 천연물 신약개발 : 지표성분 탐색 , 원료 및제품 표준화elucidation of bioactivity correlated biomarker 약용식물 개체별 원산지 구분 천연물 함유 제품의 quality control (batch to batch variation)• 건강기능식품의 efficacy 조사  dietary effects
    22. 22. Publication
    23. 23. Introduction The prevalence of obesity is increasing rapidly worldwide. To reduce the associated risks, it is necessary to investigate the causes ofweight gain (e.g., lifestyle and behavior). To prevent obesity, early diagnosis and treatment of obesity are important. Obesity studies involving the administration of a high-fat diet (HFD) in animal models are known to be applicable to human obesity.
    24. 24. Materials & Methods Experimental Design SD Male Rats (n=20, 110-120 g) Normal diet group (ND, n=10)ND low gainers ND high gainers HFD low gainers HFD high gainers (n=5) (n=4) (n=5) (n=5) visceral fat-pad urine serum 1 H-NMR liver multivariate statistical Biological analysis Analaysis
    25. 25. ResultsTable 2. Biochemical Parameters
    26. 26. Results Fig. 1. 1H-NMR spectra and assignment of urine metabolites The signals assigned based on comparisons with the chemical shifts of standard compounds using the Chenomx NMR software suite (version 5.1, Chenomx, USA).
    27. 27. ResultsFig. 2. PLS-DA score plots of urine metabolites • The PLS-DA score plot showed a separation between ND low gainers and ND high gainers • Although each rat of the two groups comsumed the same normal diet, it was possible to metabolically discriminate rat groups with different physical constitutions. • The PLS-DA score plot showed a separation between ND low gainers and HFD high gainers • The various endogenous metabolites changed in rats comsuming the high-fat diet.
    28. 28. ResultsValidation of PLS-DA models Cross-validation • Plastic cage • R2: the goodness of fit (0<R2<1) - 1 means perfect fit • Q2: the goodness of prediction - >0.5 means good prediction - >0.9 means excellent prediction • Plastic cage testing Permutation • Provided the statistical significance of the estimated predicted power of the models • Comparing R2Y and Q2Y values of original model with them of re-ordered model • Valid model : R2Y intercept <0.3-0.4 & Q2Y intercept <0.05
    29. 29. Results Table 4. The VIP values of the compoundsGenerally, a cutoff for VIP around 0.7-0.8 works well. The compounds with VIP>0.75 : influential compounds for separating each samples in PLS-DA models.
    30. 30. ResultsFig. 4. Intensity of the metabolites  Normalized relative to the creatinine l cons titution intensityPhysica  An independent t test (*p < 0.025) was performed to assess the statistical significance between each group  The relative intensities of betaine, taurine, acetone/acetoacetate, t diet High-fa phenylacetylglycine, pyruvate, lactate, and citrate differed significantly between ND low gainers and ND high gainers/HFD high gainers.
    31. 31. VIP in Metabolomics Dr.Nicholson Imperial Dr. Col.    Verpoorte Leiden Dr. Gonzalez Univ. Dr. Tomita NIH/NCI  Keio Univ.     Dr. Kopka Max-Planck Institute Dr. Fiehn Dr. Sumner UC Davis Samuel Roberts Noble Foundation
    32. 32. SWOT of MetabolomicsStrength Weakness Robust and stable  Analytical sensitivity analytical platforms  Analytical dynamic range Minimally invasive  Complexity of data sets Real biological endpoint  High capital cost Whole system integrationOppurtinities Threats Much experience from  Skepticism of non- mammalian system studies hypothesis led studies (e.g. pathways)  Conservatism Potential of multi-omics  Lack of well trained scientists integration Web-based diagnotics
    33. 33. AcknowledgementProf. Rob. Verpoorte, Leiden UniversityDr. Younghae Choi, Leiden UniversityDr. Dae Young Kwon, KFRIProf. Young-Suk Kim, Ewha Womans UniversityProf. Somi Cho, Kim, Cheju National UniversityProf. Taesun Park, Yonsei UniversityProf. Yeon-Soo Cha, Chunbuk National UniversityProf. Jung-Hyun Kim, Chung-Ang UniversityPh.D studentsSeung-Ok Yang, Sun-Hee HyunMS studentsSo-Hyun Kim, Hee-su Kim, Yujin Kim
    34. 34. What is now provedwas once onlyimagined.- William Blake

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