Metabolomics - CERM

Metabolomics
Leonardo Tenori
FiorGen Fundation and
CERM

Systems Biology
and the rise of the “-omics”
Omics technologies such as genomics and high-throughput DNA sequencing were introduced in parallel to the
Human Genome Project since 1990s. According to one etymological analysis, the suffix 'ome' is derived from the
Sanskrit OM ("completeness and fullness") (Lederberg and McCray, 2001). Omics technologies and various
neologisms that define their application contexts, however, are more than a simple play on words. They substantially
transformed both the throughput and the design of scientific experiments. The omics technologies allow the
generation of copious amounts of data at multiple levels of biology from gene sequence and expression to protein
and metabolite patterns underlying variability in cellular networks and function of whole organ systems (Nicholson
and Lindon, 2008; Wilke et al., 2008)
Genomics
Study of genes
Epigenomics
The study of the complete set of epigenetic (DNA methylation)
modifications on the genetic material of a cell, known as the
epigenome
Transcriptomics
All the mRNA in a cell/tissue/organism
Proteomics
All the proteins in a cell/tissue/organism
Metallomics
comprehensive analysis of the entirety of metal and metalloid species
within a cell or tissue type
Metabonomics/Metabolomics
All the metabolites in a cell/tissue/organism

“La metabolomica è l’ultima nata tra le scienze omiche e ha lo scopo di studiare il
metaboloma, che è l’insieme di tutti i metaboliti contenuti in un fluido biologico (o cellula
o tessuto)”.
Cos’è la Metabolomica
Punti di forza: forza
Genomics: the only -omics which is not context dependent
Metabolomics: strong environmental influence
L’insieme dei metaboliti rappresenta
l’espressione
l’espressione amplificata amplificata del
genoma
I metaboliti sono caratterizzati da
elevata stabilità. stabilità Ciò permette
una elevata precisione e
riproducibilità delle misure
L’analisi metabolomica scatta
“un’istantanea
un’istantanea” dello stato di
salute o malattia di un soggetto

Genomics is “only” the
start!
Genomics:
the complete blueprint of an individual. What do we need more?
There are 6 million parts in a 747 plane. If someone shows you the
blueprints of all of them one after the other, would you be able to tell how
the plane looks like?
Proteomics:
“only” 30-40,000 proteins.
However, millions of potential interactions that make an “individual”. And the
analysis is still very difficult…
Metabolomics:
Only a few thousand metabolites.
However, not negligible external variability.

Metabolomica:
la nuova frontiera
“Genomics and proteomics tell you what
might happen, but metabolomics tells
you what actually did happen”
Bill Lasley - University of California, Davis
“If you have a disease, it’s likely that
your metabolism is going to be
affected. The same is true if you get hit
with a toxicant. To be honest, the
diagnostic potential is staggering”
Mark Viant - University of Birmingham

Since the late 1990s,
such metabolomic
studies have
undergone an
explosive explosive growth growth and and
this trend is still
continuing, with more
than a thousand of
papers published in
2010!
Entries in Pubmed
1400
1200
1000
800
600
400
200
Metabonom*
Metabolom*
0
1998 2000 2002 2004 2006 2008 2010 2012
Year

Metabonomics “…measurement of the dynamic multiparametric
metabolic response of living systems to pathophysiological stimuli or
genetic modification…” Nicholson et al., 1999
What’s in a name?
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…” Oliver 2002

Metabolomica
Analisi del profilo (della della concentrazione di un
particolare metabolita o di una specifica
classe classe)
Analisi dell’impronta (della della presenza e
concentrazione di tutti i metaboliti
evidenziati, sia pure non tutti noti, e dal
confronto con impronte campione per
evidenziare alterazioni dovute a malattie,
esposizione a tossine, alterazioni genetiche
o impatto ambientale)
ambientale

Metabolomica:
alcuni obiettivi
Valutare eventuali correlazioni tra
impronta metabolica e malattia
(sarebbe sarebbe così così possibile possibile disporre disporre di di nuovi nuovi
strumenti strumenti per per approfondire
approfondire le le
conoscenze
conoscenze su su determinate
determinate patologie)
patologie

Metabolomica:
alcuni obiettivi
Cercare di capire se sia possibile diagnosticare
e valutare lo stadio di avanzamento di una
malattia
(una una diagnosi diagnosi più più precoce precoce dei dei tumori tumori di di quella quella
attualmente
attualmente possibile, possibile, per per esempio, esempio,
permetterebbe
permetterebbe di di salvare salvare il il 30 30%% di di malati malati
utilizzando utilizzando ii farmaci farmaci attualmente
attualmente disponibili)
disponibili

Metabolomica:
alcuni obiettivi
Scoprire nuovi biomarker
(quelli quelli attuali attuali utilizzati utilizzati per per la la diagnosi diagnosi di di
alcune alcune patologie patologie potrebbero potrebbero non non
essere essere gli gli unici unici e/o e/o ii più più efficienti)
efficienti

Metabolomica:
alcuni obiettivi
Studiare i metaboliti connessi a specifici
pathway metabolici
(sarebbe sarebbe possibile possibile definire definire dei dei nuovi nuovi
bersagli bersagli per per farmaci farmaci futuri futuri ee valutare valutare
l’impatto l’impatto di di quelli quelli attuali attuali permettendo
permettendo
una una personalizzazione personalizzazione avanzata avanzata della della
terapia)
terapia

The metabolome
consists of what?
Small organic molecules: amino acids, fatty acids,
carbohydrates, vitamins, and lipids
& some inorganic, elemental species
Metabolome informatics resource:
Metabolome informatics resource:
Kyoto Encyclopedia of genes and genomes (kegg)
http://www.genome.jp/kegg/compound
The human metabolome project: metabolomics toolbox
http://www.metabolomics.ca
National Centre for Plants & Microbial Metabolomics:
http://www.metabolomics.bbsrc.ac.uk

What is a
Metabolite?
Any organic molecule detectable in the
body with a MW < 1000 Da
Includes peptides, oligonucleotides,
sugars, nucelosides, organic acids,
ketones, aldehydes, amines, amino
acids, lipids, steroids, alkaloids and
drugs (xenobiotics)
Includes human & microbial products
Concentration > 1µM

C om p ound class N u m b er C om p ound cla ss N u m b er
A cyl g lycin es 10 Indoles and indole derivatives 12
A cyl p h osp h ates 10 In organic ions and gases 20
A lcohol p h osp h ates 2 K eto acid s 8
A lcohols an d p olyols 40 K eto nes 6
A ld ehyd es 3 L eukotrienes 8
A lkan es an d alkenes 10 M inerals and elem ents 40
A m in o acid p h osp h ates 1 M iscellaneous 77
A m in o acid s 114 N ucleosides 24
A m in o alcoh ols 14 N ucleotid es 24
A m in o k etones 14 P eptid es 21
A rom atic acid s 22 P hosph olipids 2177
B ile acid s 19 P olyam ines 11
B iotin an d d erivatives 2 P olyphen ols 22
C arb oh ydrates 35 P orphyrin s 6
C arn itin es 22 P rostan oid s 23
C atech olam in es an d d erivatives 21 P terins 14
C ob alam in d erivates 4 P urines and purine d erivatives 11
C oen zym e A d erivatives 1 P yridoxals and derivatives 7
C yclin am in es 9 P yrim idines and pyrim idine derivatives 2
D icarb oxylic acid s 17 Q uinones and derivatives 3
F atty acid s 65 R etinoid s 11
G lu coron id es 8 Sphingo lipid s 3
G lycerolip id s 1070 Steroid s and steroid derivatives 109
G lu ycolip id s 15 Sugar phosp hates 9
H ydroxy acid s 129 T ricarboxylic acids 2

H 2N
O
O
Glycine
O
Pyruvic acid
OH
HO
H 2N
OH
HO
O
Esempio di metaboliti
NH
O
H
N
Succinic acid
O O
Oxaloacetic acid
Arginine
N
H
Tryptophan
O
OH
OH
NH 2
O
O
NH 2
OH
OH
H 2N
N
N
N
N
Acetyl CoA
O
HO OH
Adenosine-5'-triphosphate
O
O
O
O
P
P
OH
P
OH
OH
O
O
OH

Why 1 µM?
Equals ~200 ng/mL
Limit of detection by NMR
Limit of facile isolation/separation by
many analytical methods
Excludes environmental pollutants
Most disease indicators have
concentrations >1 µM
Need to draw the line somewhere

Metabolomics
Generate metabolic “signatures”
Monitor/measure metabolite flux
Monitor enzyme/pathway kinetics
Assess/identify phenotypes
Monitor gene/environment interactions
Track effects from toxins/drugs/surgery
Monitor consequences from gene KOs
Identify functions of unknown genes

Medical Metabolomics
Generate metabolic “signatures” for disease
states or host responses
Obtain a more “holistic” view of metabolism
(and treatment)
Accelerate assessment & diagnosis
More rapidly and accurately (and cheaply)
assess/identify disease phenotypes
Monitor gene/environment interactions
Rapidly track effects from drugs/surgery

Metabolomica
Medicina:
Pochi metaboliti di
riferimento per ogni
specifica patologia
Metabolomica:
Quadro d’insieme dei
metaboliti

Traditional Metabolite
Analysis
HPLC, GC, CE, MS

Problems with
Traditional Methods
Requires separation followed by
identification (coupled methodology)
Requires optimization of separation
conditions each time
Often requires multiple separations
Slow (up to 72 hours per sample)
Manually intensive (constant supervision,
high skill, tedious)

What’s the
Difference Between
Metabolomics and
Traditional Clinical
Chemistry?
Throughput
(more metabolites, greater
accuracy, higher speed)

New Metabolomics
Approaches
+
Impronta digitale metabolica

Advantages
Measure multiple (10’s to 100’s) of
metabolites at once – no separation!!
Allows metabolic profiles or “fingerprints” to
be generated
Mostly automated, relatively little sample
preparation or derivitization
Can be quantitative (esp. NMR)
Analysis & results in < 60 s

NMR versus MS
• Quantitative, very
fast
• Requires no work
up or separation
• Allows analysis of
300+ cmpds at
once
• Not sensitive
• Quite fast
• Very sensitive
• Allows analysis or
ID of 3000+ cmpds
at once
• Not quantitative
• Requires work-up

2 Routes to Metabolomics
Two approaches:
• Identify as many metabolites as possible
• Use the whole spectrum as a fingerprint (statistics)
ppm
hippurate urea
fumarate
7
6
5
ppm
Quantitative
methods
hippurate
allantoin
creatinine taurine
water
4
7
TMAO
3
citrate
2
6
creatinine
1
5
2-oxoglutarate
succinate
4
3
2
1
Chemometric methods
(fingerprinting and pattern recognition)
25
PC2
20
15
10
5
0
-5
-10
-15
-20
-25
-30 -20 -10 0
PC1
10

• Identifies compounds
• Quantifies compds
Quantitative vs.
Chemometric
• Concentration range of
1 µM to 1 M
• Handles wide range of
samples/conditions
• Allows identification of
diagnostic patterns
• Limited by DB size
• No compound ID
• No compound conc.
• No compound
concentration range
• Requires strict sample
uniformity
• Allows identification
of diagnostic patterns
• Limited by training set

Benefits of analyzing the
metabolome
Number of metabolites lower than number of genes and proteins in
a cell - sample complexity reduced
Although concentration of enzyme & metabolic flux may not
significantly change during a biochemical reaction, concentration
of metabolites can change significantly
Reflect more accurately functional level of a cell
Metabolic fluxes regulated not only by gene expression but also by
environmental stresses - hence worth measuring downstream
products (i.e. metabolites)
Estimated that metabolomic expts are 2x to 3x less expensive than
proteomic & transcriptomic expts

Challenges when
analyzing metabolomes
Metabolomes extend over 7 to 9
order of magnitudes in
concentration (picomoles to
millimoles)
Currently not possible to analyze all
metabolites in a single analysis
Several analytical strategies (MS,
NMR in combination with
chromatographic separations,
whole cell analysis)
Requires high throughput

Advantages:
Use of NMR in
Non-destructive, non-biased
Easily quantifiable
Requires little or no separation
metabolomics studies
Permits identification of novel compounds
Does not require chemical derivatization
Particularly amenable to cmpds less tractable to GC-MS
or LC-MS (sugars, amines, volatile ketones, &
relatively non-reactive compounds)
Ref. Trends in analytical chemistry (2008) Vol 27, pp.228-237

Disavantage of the NMR
approach
Relatively insensitive technique
Lower limit of detection 1-5 µM
Usually large sample size (500 µL)

Raccolta e
stoccaggio
dei campioni
Processo Sperimentale
Preparazione
dei campioni
Analisi
Statistica
Impronta
Misure
NMR
Profilo
Assegnamento
dei segnali
Elaborazione
degli spettri
Database di
composti di
riferimento

NMR Experiment
A current through (green)
generates a strong magnetic field
polarizes the nuclei in the sample
material (red).
It is surrounded by the r.f. coil (black)
delivers the computer generated r.f.
tunes that initiate the nuclear
quantum dance.
At some point in time, the switch is
turned and now the dance is recorded
through the voltage it induces.
the NMR signal, in the r.f. coil.
The signals Fourier transform (FT) shows
"lines" for different nuclei in different
electronic environments.

NMR
A typical 950-MHz H NMR spectrum of urine showing the
degree of spectral complexity

Profilo 1 H NMR di urina umana

Profilo 1 H NMR di urina umana

Profilo 1 H NMR di urina umana

Profilo di siero umano
Proteins + Lipids + Small molecules
Lipids + Small molecules
Lipids + Proteins
Lipids

serum
urine
saliva
fecal extract

Data analysis - approach
Classify NMR spectrum based on its
inherent patterns of peaks
Identify spectral features responsible
for the classification (according to
physiological or pathological status)
NMR spectral data processing
Prepare NMR data for multivariate
modeling:
Spectral binning: spectra divided
into regions whose areas are
summed to extract peak intensities
Results in a data matrix:
Rows = samples/observations
Columns= variables (for example,
normalized peak intensities of
defined bins)

Data analysis and
interpretation
Data collected represented in a matrix
Chemometric Approach
Principle Component Analysis (PCA)
Soft Independent Modeling of Class Analogy (SIMCA)
Partial Least-Squares aka Projections to Latent Structures (PLS)
Orthogonal PLS (OPLS)
Targeted Profiling

PCA
Unsupervised
Multivariate analysis based on projection methods
Main tool used in chemometrics
Extract and display the systematic variation in the data
Each Principle Component (PC) is a linear combination of the
original data parameters
Each successive PC explains the maximum amount of variance
possible, not accounted for by the previous PCs
PCs Orthogonal to each other
Conversion of original data leads to two matrices, known as
scores and loadings
The scores(T) represent a low-dimensional plane that closely
approximates X. Linear combinations of the original
variables. Each point represents a single sample spectrum.
A loading plot/scatter plot(P) shows the influence (weight) of the
individual X-variables in the model. Each point represents a
different spectral intensity.
The part of X that is not explained by the model forms the
residuals(E)
X = TP T = t 1p 1 T + t 2p 2 T + ... + E

PCA Plot Nomenclature
• PCA Generate 2
kinds of plots, the
scores plot and the
loadings plot
• Scores plot (on
right) plots the data
using the main
principal
components
original
data
Z = X W
scores loading

PCA Loadings Plot
• Loadings plot shows
how much each of the
variables
(metabolites)
contributed to the
different principal
components
• Variables at the
extreme corners
contribute most to the
scores plot separation

PCA Details/Advice
In some cases PCA will not succeed in
identifying any clear clusters or obvious
groupings no matter how many components
are used. If this is the case, it is wise to
accept the result and assume that the
presumptive classes or groups cannot be
distinguished with PCA
As a general rule, if a PCA analysis fails to
achieve even a modest separation of classes,
then it is probably better to use other
statistical techniques to try to separate them

SIMCA
Supervised learning method
based on PCA
Construct a seperate PCA
model for each known class
of observations
PCA models used to assign the
class belonging to observations
of unknown class origin
Recommended for use in one class
case or for classification if no
interpretation is needed
CLASS SPECIFIC STUDIES
One-class problem: Only disease observations
define a class; control samples are too
heterogeneous, for example, due to other
variations caused by diseases, gender, age, diet,
lifestyle, etc.
Two-class problem: Disease and control
observations define two seperate classes

PLS
Supervised learning method.
Recommended for two-class cases instead of using
SIMCA.
Principles that of PCA. But in PLS, a second piece
of information is used, namely, the labeled set
of class identities.
Two data tables considered namely X (input data
from samples) and Y (containing qualitative
values, such as class belonging, treatment of
samples)
The quantitive relationship between the two tables
is sought.
X = TP T + E
Y = TC T + E
The PLS algorithm maximizes the covariance
between the X variables and the Y variables
PLS models negatively affected by systematic
variation in the X matrix not related to the Y
matrix (not part of the joint correlation
structure between X-Y.

OPLS
OPLS method is a recent modification of the PLS method to help overcome pitfalls
Main idea to seperate systematic variation in X into two parts, one linearly related to Y and one unrelated
(orthogonal).
T T
Comprises two modeled variations, the Y-predictive (T P ) and the Y-orthogonal (ToP ) compononents.
p p
o
Only Y-predictive variation used for modeling of Y.
T T
X = T P + ToP + E
p p o
T
Y = T C + F
p p
E and F are the residual matrices of X and Y
OPLS-DA compared to PLS-DA

Remarks on pattern
classification
Intent in using these classification techniques not to identify
specific compound
Classify in specific categories, conditions or disease status
Traditional clinical chemistry depended on identifying and
quantifying specific compounds
Chemometric profiling interested in looking at all
metabolites at once and making a phenotypic
classification of diagnosis

Targeted profiling
Targeted metabolomic profiling is fundamentally different than
most chemometric approaches.
In targeted metabolomic profiling the compounds in a given
biofluid or tissue extract identified and quantified by comparing
the spectrum of interest to a library of reference spectra of
pure compounds.
Key advantage: Does not require collection of identical sets =
More amenable to human studies or studies that require less
day-to-day monitoring.
Disadvantage: Relatively limited size of most current spectral
libraries = bias metabolite identification and interpretation.
A growing trend towards combining the best features of both
chemometric and targeted methods.

Databases
Large amount of data
Need for databases that can be easily searched
Better databases will help in combining
chemometric and targeted profiling methods
Newly emerging databases
HMDB good model for other databases
Challenge of standardisation

Databases

Metabolomica:
Fattori di variabilità
Sesso
Età
Dieta
Dieta
Ritmi fisiologici
Genotipo
Stress
Patologie

Effetto della dieta
Trimetilammina N-ossido
Consumo di pesce
NON Consumo di pesce
Consumo di chewing-gum, caramelle etc..
Mannitolo
4 .00 3.90 3.80 3.7 0 3.60

Ind 1
Ind 2
Effetto del profilo individuale
10.00 7.50 5.00 2.50 ppm
62

Effetto delle patologie
Campioni di saliva da individui sani e da individui affetti da periodontite cronica

Our interest in metabolomics
Metabolic signature of individuals
Metabolic phenotype
Metabolic signature of diseases
• Celiac disease
• tumor → metastasis (breast, colorectal)
• cardiovascular risk
• diabetes
• pulmonary diseases
• …
Metabolites and biobank samples
• Sensitive reporters of stability
• Assess sample preparation and preanalytical procedures
• …

METabolomic REFerence
The METREF project
Looking first at urine of healthy individuals, and developing a
feeling for the intraindividual vs. interindividual variations
Why?
• Training
• Urine samples are easy to collect
• Large number of samples
• Potential intrinsic value of the information

METabolomic REFerence
Experimental scheme:
• 22 Individuals, 11 Males & 11 Females
• ≥40 urine samples each, on a period of 2-3 months
• First in the morning preprandial
• Collection suspended in case of illness; otherwise no restrictions
• Data recording:
Diet
Drugs
Lifestyle, general habits
Smoker / No Smoker
• NMR analysis: 1D 1 H spectra

Ind 1
Ind 2
METabolomic REFerence
Getting a first feeling…
We believe the human
eye is very sensitive to
differences in patterns
Visual inspection suggests that it should be interesting to look for individual
fingerprints by statistical analysis

METabolomic REFerence
Convex hulls of 22 donors in the three most significant PCA PCA-CA CA dimensions
PCA for data
reduction
CA for obtain
well separated
clusters
KNN for
classification
99% accuracy
in montecarlo
cross validation
“natural” gender discrimination
MALE
FEMALE
Assfalg, Bertini, Colangiuli, Luchinat, Schäfer, Schütz, Spraul, PNAS PNAS, , 2008 2008, , 105, 1420 1420-4

METabolomic REFerence
Dendrogram of the 22 donors on the 21 21-dimensional dimensional PCA-CA PCA CA subspace
Assfalg, Bertini, Colangiuli, Luchinat, Schäfer, Schütz, Spraul, PNAS PNAS, , 2008 2008, , 105, 1420 1420-4

METabolomic REFerence
Gut microflora related metabolites
Concentrations of 12 selected metabolites for each donor. Absolute
creatinine concentration (Crea) and relative metabolite
concentrations (relative to creatinine)

METabolomic REFerence
An individual metabolic fingerprint exist!
But it is hidden inside the daily noise
Assfalg, Bertini, Colangiuli, Luchinat, Schäfer, Schütz, Spraul, PNAS PNAS, , 2008 2008, , 105,
1420-4
1420

METabolomic REFerence 2
Why Healthy Individuals Again?
• Expanding the dataset
• Trying to learn more about relevance of genetic vs lifestyle contributions
• Check the constancy of metabolic phenotypes over time

METabolomic REFerence 2
Experimental Scheme (2 years later)
• 20 Individuals, 9 Male & 11 Females
• 11 Individuals (6 M + 5 F) already in the first screening
• 40 samples/each on a period of 2-3 months
• First in the morning preprandial
• Collection suspended in case of illness
• Data recording:
Diet
Drugs
Life style
Smoker / No Smoker
• NMR analysis: 1D 1 H spectra

METREF 1,2,3
11
7 4 22
t 20 7 4
4
4
father
& son
5 22
twins
MetRef1
2005
MetRef2
2007
MetRef3
2008

METabolomic REFerence 2
2005 collection 2007 collection
AD
AF
AG
AH
AI
AO
AP
AR
AS
AT
AU
AW
AX
AZ
BC
BD
BE
BF
BG
BH
BI
BK
99.905%
100.000%
100.000%
99.922%
99.760%
97.500%
97.500%
100.000%
100.000%
99.995%
100.000%
100.000%
100.000%
100.000%
99.998%
100.000%
100.000%
100.000%
99.933%
100.000%
100.000%
99.470%
AI
AO
AR
AS
AU
AW
BC
BF
BG
BH
BI
BQ
BS
BT
BU
BV
BX
BZ
TA
TB
100.000
100.000% %
99.995%
99.941%
100.000%
99.998%
99.705%
99.629%
100.000%
100.000%
98.462%
99.998%
100.000%
99.983%
96.647%
99.998%
99.998%
100.000%
98.450%
98.235%
PCA-CA-KNN classification results
Bernini, P.; Bertini, I.; Luchinat, C.; Nepi, S.; Saccenti, E.; Schäfer, H.; Schütz, B.; Spraul,
M.; Tenori, L. Individual human phenotypes in metabolic space and time, J. Prot. Res. 2009

METabolomic REFerence
genes
lifestyle etc.
• Il metabotipo consiste di una parte variabile (ambiente) e di una parte
invariabile (genetica+ambiente)
• La parte invariante rimane inalterata per almeno 2/3 anni
• La scoperta del fingerprint metabolico individuale ha un grande
potenziale per studi biomedici
Bernini, P.; Bertini, I.; Luchinat, C.; Nepi, S.; Saccenti, E.; Schäfer, H.; Schütz, B.; Spraul, M.; Tenori, L.
Individual human phenotypes in metabolic space and time, J. Prot. Res. 2009

Un “salto” metabolico
Evoluzione del profilo metabolico di un individuo
nell’arco di tre anni
Hippurate
MetRef1
MetRef2
MetRef3

Celiac Disease Metabolomics
What is Celiac Disease?
• Celiac Disease (CD), or sprout, is a permanent intolerance to gluten
• Gluten is a proteic complex formed by gliadin and glutenin
• Gluten is found in wheat, rye and barley and others
• Gliadin and glutenin comprise about 80% of the protein contained in wheat
seeds.
• Gluten is present in bread, pasta, pizza, biscuits…
• Gluten is one of the most used alimentary additives
The ONLY therapy is a
totally gluten-free diet
Aim: define the metabolome of celiac disease; obtain hints on its biochemistry

Celiac Disease Metabolomics
Experimental scheme:
• Study subjects: 34
• Control subjects: 34
• Samples: Serum and Urine
NMR spectra acquired:
• 1D Noesy (standard 1D 1H spectra) for serum and urine samples
• CPMG: to remove signals due to macromolecules (on serum samples)
• @ a Bruker 600 MHz
Statistical
Analysis
Projection to Latent Structures (PLS) to reduce data dimension Optimal number of
components obtained by minimizing the Cross-Validated (CV) error
Canonical Analysis (CA) to obtain two well separated clusters
Support Vector Machines (SVM) for classification

Celiac Disease Metabolomics
Results
CPMG spectra Serum
Accuracy: 83.4%
Sensivity: 83.4%
Specifity: 83.4%
NOESY spectra Serum
Accuracy: 78.9%
Sensivity: 74.0%
Specifity: 82.8%
NOESY spectra Urine
Accuracy: 69.3%
Sensivity: 73.9%
Specifity: 63.9%

Celiac Disease Metabolomics
Note: both subjects are
asymptomatic!
Clusterization of serum spectra of celiac and healthy subjects
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

Celiac Disease Metabolomics
Significantly different metabolites in
serum (p

Celiac Disease Metabolomics
Celiac disease often associated with fatigue:
Why ?
Increased glucose, decreased pyruvate, lactate:
Impaired glycolysis, impaired energy production
Lipid beta beta-oxidation oxidation + use of ketonic bodies:
Alternate less efficient energy production

Celiac Disease Metabolomics
Follow-up analysis
Samples at 3, 6, 9, 12 months from diagnosis
Serum and urines
Metabolite profiling
Patter recognition
Using CPMG sera, all but one samples after 12
months on a gluten-free diet are classified as
normal!

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

Celiac Disease Metabolomics
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

Potential celiac disease
Subjects: 134
Celiacs: 59 (9 male, 50 female, age 40,2 ± 14,9)
Potential Celiacs: 25 (5 male, 20 female, age 34,2 ± 13,1)
Healthy: 50 (18 male, 32 female, age 36,1 ± 13,9)
Aim: define the metabolome of potential
celiacs subjects
The term potential CD patients has been proposed for those subjects who do not
have, and have never had, a jejunal biopsy consistent with clear CD, and yet have
immunological abnormalities similar to those found in celiac patients.

Potential Celiac Disease
Celiacs Celiacs Vs Vs Healthy
Serum Serum Serum CPMG
CPMG
• Accuracy: 82.3 82.3 %
%
• Sensivity: 82.3 82.3 %
%
• Specifity82.9 82.9 % %
%
Serum Serum Serum Serum Serum Noesy
Noesy
• Accuracy : 74.4 74.4 % %
%
• Sensivity: 77.6 77.6 77.6 %
%
• Specifity : 70.1 70.1 %
%
Urine Urine Noesy
Noesy
• Accuracy : 69.4 69.4 %
%
• Sensivity : 73.3 73.3 73.3 % %
%
• Specifity: 64.1 64.1 %
%
Celiacs Celiacs Vs Vs Potential Potential Celiacs
Celiacs
Serum Serum CPMG
CPMG
• Accuracy : 63.7 63.7 %
%
• Sensivity : 81.2 81.2 %
%
• Specifity : 19.7 19.7 %
%
Serum Serum Noesy Noesy Noesy Noesy
Noesy Noesy Noesy
. . Accuracy : 64.9 64.9 %
%
• Sensivity : 81.8 81.8 81.8 % %
%
• Specifity : 24.7 24.7 %
%
Urine Urine Noesy
Noesy
• Accuratezza : 59.9 59.9 %
%
• Sensivity : 79.0 79.0 %
%
• Specifity: 11.3 11.3 11.3 %
%

Celiachia Potenziale
Celiaci – Sani –
Croci: predizione dei Celiaci Potenziali
Celiaci Potenziali: soggetti con anticorpi positivi
alla gliadina ma senza presenza di danno
intestinale. NON sono celiaci e NON vengono
messi a dieta
Esiste una impronta metabolica
della celiachia
Queste alterazioni sono presenti
anche nei celicaci potenziali:
esse precedono il danno
intestinale
La celiachia potenziale è molto
simile da un punto di vista
metabolico alla celiachia. Molti
metaboliti che differenziano I
controlli e celiaci sono alterati
anche nei celiaci potenziali. I
nostri risultati suggeriscono l’uso
di dieta priva di glurine anche
nei celiaci potenziali
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

Spettro NMR di olio di oliva
Prime esperienze con olio acquisite alcuni anni fa (Prof. Luchinat)
L.Mannina, C.Luchinat, M.Patumi, M.C.Emanuele, E.Rossi. A.L.Segre,
"Concentration dependence of 13C NMR spectra of triglycerides: implications for the
NMR anlysisis of olive oils",
Magnetic Resonance in Chemistry (2000), 38, 886-890.
L.Mannina, C.Luchinat, M.C.Emanuele, A.L.Segre,
"Acyl positional distribution of glycerol tri-esters in vegetable oils: a 13C NMR study",
Chemistry and Physics of Lipids, (1999), 103, 47-55.

ACIDO LINOLENICO REGIONE DEI METILI
( 1H) H)
β SITOSTEROLO
OLIO DI OLIVA
EXTRAVERGINE
OLIO DI OLIVA
OLIO DI GIRASOLE
OLIO DI MAIS
OLIO DI SOIA
OLIO DI ARACHIDI

ATTRIBUTI SENSORIALI
Olio amaro
Olio avvinato
Cattiva separazione
dalle acque di
vegetazione
Olio pungente
Olio fruttato

PRODOTTI DI OSSIDAZIONE QUASI ASSENTI IN
UN OLIO BUONO
OLIO EXTRAVERGINE
OLIO RANCIDO

CARATTERIZZAZIONE GEOGRAFICA ( 1 H)
Olio Siciliano Olio Umbro

CARATTERIZZAZIONE
GEOGRAFICA ( 1 H):
OLI TOSCANI
TCA LDA
Seggiano Lucca Arezzo
Root 2
10
-8
-15 -10 -5 0 5 10 15
L. Mannina, M.Patumi, N.Proietti, A.L. Segre, Italian Journal of Food Science. (2001), 13, 53-64
8
6
4
2
0
-2
-4
-6
Root 1
S
AR
A

CARATTERIZZAZIONE
GEOGRAFICA ( 1 H):
OLI DEL CENTRO-NORD
LDA
L.Mannina, M.Patumi, N.Proietti, D.Bassi, A.L.Segre, Journal of Agriculture and Food Chemistry, (2001), 49, 2687-
2696

Metabolomica del latte
Recente interesse per
l’applicazione della metabolomica
all’analisi del latte e dei suoi
derivati

*Lattosio
Spettro 1 H NMR di latte
Lecitina
Creatina
Glicerolo
Acidi grassi
insaturi
*
Citrato
Acidi
grassi

Spettro 1 H NMR di marche diverse
Non si osservano differenze negli
spettri di latte fresco intero tra marche
diverse.
Il Granarolo parzialmente scremato
presenta picchi meno intensi nella
zona degli acidi grassi (frecce).
Maremma
Mukki
Granarolo

Spettro 1 H NMR di latte scremato
Lo spetto di latte scremato non
presenta i picchi relativi alla presenza
degli acidi grassi saturi e insaturi e
quelli relativi al glicerolo
Coop
Intero
Coop
Scremato

Formato
Spettro 1 H NMR di latte di capra
Piruvato
Acetato
Capra
Mucca

Formato
Fresco
Spettro 1 H NMR di latte UHT
UHT
Piruvato
Acetato

formato
Formato acetato e
piruvato sono
caratteristici dei latti
UHT, anche se in
quantità diverse
Spettro 1 H NMR di latte UHT
Granarolo UHT
Granarolo Italia UHT
Maremma UHT
(
Mukki UHT
COOP UHT
piruvato
acetato

Spettro 1 H NMR di latte ω3
Acidi grassi
insaturi
Intero
ω3

Disponibilità di Tesi in
METABOLOMICA
presso il
Centro di Risonanze Magnetiche
dell’Università di Firenze
Argomento di Ricerca:
Analisi Metabolomica di Fluidi Biologici
tramite Risonanza Magnetica Nucleare
La proposta è rivolta a laureandi
(Laurea di I livello e\o II livello)
In Chimica, Chimica Farmaceutica,
Biologia, Biotecnologie
Per informazioni: tenori@cerm.unifi.it