Metabolomics by NMR at CERM. Claudio Luchinat CERM/CIRMMP University of Florence

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1 Metabolomics by NMR at CERM Claudio Luchinat CERM/CIRMMP University of Florence

2 CERM/CIRMMP Magnetic Resonance Center Main node of the Structural Biology Instruct-ERIC infrastructure Biobank Lab (2016) ss800 Biobank Competence Center Ivano Bertini (July 7, 2015) He Liquifier Conference Room Library Workstations 900* 700* 1200** *equipped with cryoprobes ** due in 2019 Fully or partially Cryo EM** Mass Spectrometry X-Ray Crystallography ss700wb GENEXPRESS 600 Genetic expression laboratory * 700* 400 ss850wb Relaxometer 950* ( MHz) Molecular biology, cellular biology and biophysics labs for metabolomics

3 2 routes to metabolomics

4 Celiac Disease Metabolomics Accuracy > 90% Clusterization of Celiac and Healthy subject serum spectra Bertini, I.; Calabrò, A.; De Carli, V.; Luchinat, C.; Nepi, S.; Porfirio, B.; Renzi, D.; Saccenti, E.; Tenori, L. The metabonomic signature of celiac disease, J. Proteome Res. 2009, 8(1), 170

5 Celiac Disease Metabolomics Accuracy > 90% Clusterization of Celiac and Healthy subject serum spectra and corresponding Follow-up Bertini, I.; Calabrò, A.; De Carli, V.; Luchinat, C.; Nepi, S.; Porfirio, B.; Renzi, D.; Saccenti, E.; Tenori, L. The metabonomic signature of celiac disease, J. Proteome Res. 2009, 8(1), 170

6 Celiac Disease Metabolomics There exists a metabolic fingerprint of celiac disease Celiac Healthy Subjects Cross: predicted Potential Celiac These alterations are present also in potential celiac subjects: so they precede the intestinal damage Potential CD largely shares the metabonomic signature of overt CD. Most metabolites found to be significantly different between control and CD subjects Bernini P, Bertini I, Calabrò A, la Marca G, Lami G, Luchinat C, were also altered in potential CD. Our results suggest early Renzi D, Tenori L. Are patients with potential celiac disease institution of GFD in patients really potential? The answer of metabonomics. J. Proteome with potential CD Res A metabolomic perspective on coeliac disease, A Calabrò, E Gralka, C Luchinat, E Saccenti, L Tenori. Autoimmune Diseases, 2014

7 Colorectal Cancer Metabolomics Serum samples from 139 HS and 155 patients with mcrc, included in a prospective phase II study of 3rd line treatment with cetuximab and irinotecan We can discriminate healthy controls from mcrc with almost 100% accuracy. More importantly, we can predict the overall survival of the patients Cross-validated results on the Training Set: PLS-CA model: long survival, in blue; short survival, in yellow Sensitivity : Specificity: Accuracy: 79.9% 76.4% 78.5% Univariate Cox Regression Analysis for the Validation Set: HR: % CI: P: 2.02 to Bertini I, Cacciatore S, Jensen BV, Schou JV, Johansen JS, Kruhøffer M, Luchinat C, Nielsen DL, Turano P., Cancer Res Jan 1;72(1):

8 Breast cancer metabolomics Classification between Pre-Op and Metastatic subjects. Accuracy ~80% Oakman C, Tenori L, Claudino WM, Cappadona S, Nepi S, Battaglia A, Bernini P, Zafarana E, Saccenti E, Fornier M, Morris PG, Biganzoli L, Luchinat C, Bertini I, Di Leo A. Other comparisons NOESY CPMG Healthy vs Met Accuracy 73.44% Healthy vs Post-op Accuracy 75.80% Post vs Met Accuracy 74.96% Healthy vs Met Ann Oncol. 2011,22, Accuracy 72.67% Healthy vs Post-op Accuracy 70.00% Post-op vs Met Accuracy 70.00%

9 MSKCC Project Random Forest was used to derive a score for relapse prediction. ROC curve for CPMG spectra is significantly better than Adjuvant on line (AUC < 0.80). Mol Oncol Aug 10. pii: S (14)

10 Breast cancer Samples from a multicentric study in South East Asia Training set: 85 EBC women without relapse 109 MBC women Test set: 42 EBC women without relapse 192 EBC women with relapse Confusion matrix in the test set: EBC no relapse EBC relapse EBC no relapse EBC relapse Accuracy: 73.5% Hart CD, Vignoli A, Tenori L, Uy GL, Van To T, Adebamowo C, Hossain SM, Biganzoli L, Risi E, LoveRR, Luchinat C, Di Leo A., Clin Cancer Res Mar 15;23(6):

11 Heart failure metabolomics Classification between different subgroups of Heart failure patients (1D CPMG spectra). Sensitivity Specificity Accuracy CMD vs CMS 45.52% 68.29% 61.19% NYHA1 vs NYHA % 71.42% 67.71% NYHA2 vs NYHA 3/ % 56.44% 68.04% NYHA 1 vs NYHA 3/ % 68.55% 72.15% Patients vs Healthy 85.11% 91.04% 87.29% Tenori L1, Hu X, Pantaleo P, Alterini B, Castelli G, Olivotto I, Bertini I, Luchinat C, Gensini GF. Int J Cardiol , 168, Patients are separated from healthy, but there is not any significant difference between the disease grading that could reflect the clinical severity of the disease. Although good discrimination between healthy and HF subjects with a severe disease, if not expected, was easy to be hypothesized, a comparable good discrimination ability between healthy and HF subjects with a mild disease was unexpected and appears rather counter-intuitive.

12 Heart failure metabolomics The model for prediction of heart failure was developed for each of the 3 kinds of available spectra: cpmg, noesy and diffusion edited 753 new healthy samples (blood donors) were tested against each model. 20 subjects were predicted as heart failure subjects in all three kinds of spectra. We were able to recall 11 of them for an echocardiographical screening 6 out of 11 showed altered parameters (to be published) Patient ID LVEDD (Gender) (mm) LVEDD Ind. (mm/m2) IVSd (mm) LVPWd (mm) LVM ind (gr/m2) Aortic root (mm) LA (mm) RV (mm) EF (%)

13 2 routes to metabolomics

14 Statistical distriminating power Fingerprinting vs. profiling Fingerprinting Profiling No. of quantified metabolites So, why profiling? Portability (different NMR fields, NMR vs. MS, etc.) Biological insight Less prone to artifacts...

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16 B.I-LISA in Hepatitis Metabolic fingerprints between HCV and HBV patients: a possible interference of the two major hepatitis viruses in basal metabolism pathways NMR Diagnosis N HCV 67 HBV 50 HS 43 Defining the serum molecular fingerprint of patients with liver disease of viral etiology

17 B.I-LISA in Hepatitis OPLS-DA OPLS-DA 451 binning from NOESY spectra HCV HBV HS HCV HBV C-V 68% predictive accuracy 34 identified metabolites in serum samples OPLS-DA HS HCV HBV HS HCV HBV HS C-V 77% predictive accuracy 114 lipid sub-fractions HCV HBV HS HCV HBV HS C-V 78.2 % predictive accuracy

18 EPIC study: breast cancer The European Prospective Investigation into Cancer and Nutrition (EPIC) study was designed to investigate the relationships between diet, nutritional status, lifestyle and environmental factors, and the incidence of cancer and other chronic diseases. 192 patients who developed breast cancer 96 High mammographic density 96 Low mammographic density

19 EPIC study: breast cancer DIFFUSION spectra Low Low 61.5 High 36.3 High Accuracy: 62.6% P-value: <0.05 Best discrimination using diffusion spectra, due to the contribution of lipoproteins. BILISA found differences in TG, especially in VLDL fractions.

20 EPIC study: breast cancer (High Density/Low Density)

21 B.I.-LISA in Pathway-27 Pathway-27 is a pan-european interdisciplinary project addresses the role and the mechanism of action of 3 bioactives (docosahexaenoic acid, β-glucan and anthocyanins), chosen for their known effectiveness in reducing some risk factors of metabolic syndrome, enriching 3 different food matrices (dairy-, bakery-, and egg products). PILOT STUDY: 22 subjects with a diet enriched in DHA+BG T0: before starting the diet T1: after 4 weeks of diet Serum samples analysed with B.I.-LISA

22 B.I.-LISA in Pathway-27 p-value Triglycerides Cholesterol LDL-Cholesterol HDL-Cholesterol Apo-A1 Apo-A2 Apo-B T0 (median) Tendency T1 in T1 (median) TRIGLYCERIDES DISTRIBUTION p-value T0 T1 Tendency (median) (median) in T1 Triglycerides-VLDL Triglycerides-IDL Triglycerides-LDL Triglycerides-HDL Triglycerides-VLDL-1 Triglycerides-VLDL-2 Triglycerides-VLDL-3 Triglycerides-VLDL-4 Triglycerides-VLDL Triglycerides-LDL-1 Triglycerides-LDL-2 Triglycerides-LDL-3 Triglycerides-LDL-4 Triglycerides-LDL-5 Triglycerides-LDL Triglycerides-HDL-1 Triglycerides-HDL-2 Triglycerides-HDL-3 Triglycerides-HDL

23 METCOLON study This project aims at developing a statistical model able to identify changes in preoperative and postoperative metabolomic serum profiles of CC patients, obtained via NMR, where loss of a cancer signal may be predictive of better prognosis. We hypothesize that the metabolomic signal of cancer presence will remain only if there is residual micrometastatic disease postoperatively.

24 METCOLON study Quantified Metabolites (25 by Bruker IVDr) CPMG spectra (buckets) Accuracy: 80.0% Accuracy: 90.0%

25 METCOLON study T1/T0 Higher at T1

26 METCOLON study Quantified Lipids (114) Diffusion spectra (buckets) T0 T0 T1 Accuracy: 80.0% T1 Accuracy: 100.0%

27 METCOLON study T1/T0 Apo-A1, HDL-3 Apo-A1, HDL-2 Phospholipids, HDL-3 Apo-A2, HDL-3 Cholesterol, HDL-3 Free Cholesterol, HDL-2 Higher at T1 VLDL Particle Number Apo-B, VLDL LDL-Chol/HDL-Chol Apo-B100/Apo-A1 Triglycerides LDL-6 Triglycerides LDL-5

28 Metabolomics at CERM Paola Alessia Cristina Veronica Leonardo Gaia Claudio Panteleimon

29 Doing research at CERM applications are welcome for: Graduate student / Post-doc Structural Biology/Metabolomics, ICT projects, NMR developments EMBO fellowships Human Frontiers Veronesi Foundation Researcher (tenure or tenure track) Accessing CERM infrastructure: INSTRUCT i-next EuroBioNMR Phenomenal e-infrastructure WeNMR GRID infrastructure

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