Metabolomics by NMR at CERM...Tenori L 1, Hu X , Pantaleo P, Alterini B, Castelli G, Olivotto I,...

Metabolomics by

NMR at CERM

Claudio Luchinat

CERM/CIRMMP University of Florence

Molecular biology, cellular

biology and biophysics labs

GENEXPRESS Genetic expression laboratory

Mass Spectrometry

X-Ray Crystallography

*equipped with cryoprobes

Conference Room

Library

Workstations

ss700wb

600 500*

400 700*

ss850wb 950*

1200**

900* 700*

He Liquifier

Biobank

Relaxometer

(0.01-40 MHz)

600

CERM/CIRMMP Magnetic Resonance Center

Main node of the Structural Biology Instruct-ERIC infrastructure

Biobank Lab (2016)

Fully or partially for metabolomics

Competence Center “Ivano Bertini”

(July 7, 2015)

ss800

Cryo EM**

** due in 2019

2 routes to

metabolomics

Celiac Disease Metabolomics

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

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

Accuracy > 90%

Celiac – Healthy Subjects – Cross: predicted Potential Celiac

Bernini P, Bertini I, Calabrò A, la Marca G, Lami G, Luchinat C,

Renzi D, Tenori L. Are patients with potential celiac disease

really potential? The answer of metabonomics. J. Proteome

Res. 2010

There exists a metabolic fingerprint of celiac disease

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

were also altered in potential CD. Our results suggest early institution of GFD in patients

with potential CD


A metabolomic perspective on coeliac disease, A Calabrò, E Gralka, C Luchinat , E Saccenti, L

Tenori. Autoimmune Diseases, 2014

Colorectal Cancer Metabolomics

Cross-validated results on the Training Set:

Sensitivity : 79.9%

Specificity: 76.4%

Accuracy: 78.5%

Univariate Cox Regression Analysis for the Validation

Set:

HR: 3.30 95% CI: 2.02 to 5.37

P: 1.75 ∙ 10-6

PLS-CA model: long survival, in

blue; short survival, in yellow

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

Bertini I, Cacciatore S, Jensen BV, Schou JV, Johansen JS, Kruhøffer M, Luchinat C,

Nielsen DL, Turano P., Cancer Res. 2012 Jan 1;72(1):356-64.

Breast cancer metabolomics

Healthy vs

Met

Accuracy 73.44%

Healthy vs

Post-op

Accuracy 75.80%

Post vs

Met

Accuracy 74.96%

NOESY

Healthy vs

Met

Accuracy 72.67%

Healthy vs

Post-op

Accuracy 70.00%

Post-op vs

Met

Accuracy 70.00%

CPMG

Classification between

Pre-Op and Metastatic

subjects.

Accuracy ~80%

Other comparisons

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.

Ann Oncol. 2011,22,1295-1301.

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. 2014 Aug 10. pii: S1574-7891(14)00167-7

Breast cancer

Confusion matrix in the test set:

EBC no relapse EBC relapse

EBC no relapse 70.8 29.2

EBC relapse 23.8 76.2

Test set: 42 EBC women without relapse

192 EBC women with relapse

Training set: 85 EBC women without relapse

109 MBC women

Samples from a multicentric study in South

East Asia

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. 2017 Mar 15;23(6):1422-1431

Accuracy: 73.5%

Sensitivity Specificity Accuracy

CMD vs CMS 45.52% 68.29% 61.19%

NYHA1 vs NYHA 2 61.88% 71.42% 67.71%

NYHA2 vs NYHA 3/4 73.62% 56.44% 68.04%

NYHA 1 vs NYHA 3/4 74.83% 68.55% 72.15%

Classification between different subgroups of Heart failure

patients (1D CPMG spectra).

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.

Heart failure metabolomics

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. 2013 9, 168, 113-5.

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

(Gender)

LVEDD

(mm) IVSd

(mm)

LVEDD

Ind.

(mm/m2)

LVPWd

(mm)

LVM

ind

(gr/m2)

Aortic

root

(mm)

LA

(mm)

RV

(mm)

EF

(%)

2 routes to

metabolomics

No. of quantified metabolites

Sta

tist

ica

l

dis

trim

ina

tin

g p

ow

er

Fingerprinting vs. profiling

Fingerprinting

Profiling

So, why profiling?

• Portability (different NMR fields, NMR vs. MS, etc.)

• Biological insight

• Less prone to artifacts

• ...

B.I-LISA in Hepatitis

Diagnosis N°

HCV 67

HBV 50

HS 43

Defining the serum molecular fingerprint of

patients with liver disease of viral etiology

“Metabolic fingerprints between HCV and HBV patients: a possible interference of the two major hepatitis viruses in basal

metabolism pathways”

NMR

HCV HBV HS

HCV 83.1 14.1 2.8

HBV 10 58.6 31.4

HS 0 6.7 93.3

C-V 78.2 % predictive accuracy

OPLS-DA

OPLS-DA

OPLS-DA HCV HBV HS

HCV 69.3 15.4 15.3

HBV 13.9 58.8 27.3

HS 1.9 21.1 77

C-V 68% predictive accuracy

C-V 77% predictive accuracy

HCV HBV HS

HCV 81 10.8 8.2

HBV 15.8 64.4 19.8

HS 6.3 10.5 83.1

34 identified

metabolites in serum

samples

114 lipid sub-fractions

451 binning from NOESY

spectra

B.I-LISA in Hepatitis

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


Low High

Low 61.5 38.5

High 36.3 63.7

Accuracy: 62.6%

P-value: <0.05

DIFFUSION spectra

Best discrimination using

diffusion spectra, due to the

contribution of lipoproteins.

BILISA found differences in TG,

especially in VLDL fractions.


(High Density/Low Density)

B.I.-LISA in Pathway-27

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

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).

B.I.-LISA in Pathway-27 p-value

T0

(median) T1

(median)

Tendency

in T1

Triglycerides 0.0198 193.56 153.63 ↓ mg/dL

Cholesterol 0.5144 252.825 250.905 mg/dL

LDL-Cholesterol 0.0456 136.435 147.095 ↑ mg/dL

HDL-Cholesterol 0.3288 50.415 51.505 mg/dL

Apo-A1 0.095 146.04 142.07 mg/dL

Apo-A2 0.0231 35.42 34.985 ↑ mg/dL

Apo-B100 0.9663 114.74 113.235 mg/dL

TRIGLYCERIDES

DISTRIBUTION p-value

T0

(median) T1

(median) Tendency

in T1

Triglycerides-VLDL 0.0198 130.675 112.03 ↓ mg/dL

Triglycerides-IDL 0.022 23.675 16.52 ↓ mg/dL

Triglycerides-LDL 0.3045 24.55 22.805 mg/dL

Triglycerides-HDL 0.0231 11.695 10.105 ↓ mg/dL

Triglycerides-VLDL-1 0.022 62.565 55.9 ↓ mg/dL





Triglycerides-LDL-1 0.0446 6.845 6.1 ↓ mg/dL

Triglycerides-LDL-2 0.9693 2.3 2.145 mg/dL



Triglycerides-LDL-5 1 3.94 4.015 mg/dL


Triglycerides-HDL-1 0.3101 2.46 2.695 mg/dL

Triglycerides-HDL-2 0.0345 1.67 1.415 ↓ mg/dL



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.

METCOLON study

Accuracy: 80.0% Accuracy: 90.0%

Quantified Metabolites

(25 by Bruker IVDr) CPMG spectra (buckets)

METCOLON study

Higher at T1

T1/T0

METCOLON study

Accuracy: 80.0% Accuracy: 100.0%

T0

T1

T0

T1

Quantified Lipids (114) Diffusion spectra (buckets)

METCOLON study

LDL-Chol/HDL-Chol

Apo-B100/Apo-A1

Triglycerides LDL-6

Triglycerides LDL-5

VLDL Particle Number

Apo-B, VLDL

Free Cholesterol, HDL-2

Cholesterol, HDL-3

Apo-A1, HDL-3

Apo-A1, HDL-2

Phospholipids, HDL-3

Apo-A2, HDL-3

Higher at T1

T1/T0

Metabolomics at CERM Paola

Alessia

Cristina

Panteleimon Claudio

Veronica

Gaia

Leonardo

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

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Metabolomics by NMR at CERM...Tenori L 1, Hu X , Pantaleo P, Alterini B, Castelli G, Olivotto I,...

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