Leonardo Tenori - UniFI · Human Genome Project since 1990s. According to one etymological analysis, the suffix 'ome' is derived from the Sanskrit OM ("completeness and fullness")

MetabolomicsMetabolomics

Leonardo TenoriLeonardo Tenori

FiorGen Fundation andFiorGen Fundation andCERMCERM

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) 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 MetabolomicaCos’è la Metabolomica

PuntiPunti didi forzaforza::

L’insiemeL’insieme deidei metabolitimetaboliti rappresentarappresental’espressionel’espressione amplificataamplificata deldelgenomagenoma

II metabolitimetaboliti sonosono caratterizzaticaratterizzati dadaelevataelevata stabilitàstabilità.. CiòCiò permettepermetteunauna elevataelevata precisioneprecisione eeriproducibilitàriproducibilità delledelle misuremisure

L’analisiL’analisi metabolomicametabolomica scattascatta““un’istantaneaun’istantanea”” dellodello statostato didisalutesalute oo malattiamalattia didi unun soggettosoggetto

Genomics:Genomics: the only -omics which is not context dependent

Metabolomics:Metabolomics: strong environmental influence

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:

Genomics is “only” the Genomics is “only” the start!start!

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:Metabolomica:la nuova frontierala nuova frontiera“Genomics and proteomics tell you whatmight happen, but metabolomics tellsyou what actually did happen”

Bill Lasley - University of California, DavisBill Lasley - University of California, Davis

“If you have a disease, it’s likely thatyour metabolism is going to beaffected. The same is true if you get hitwith a toxicant. To be honest, thediagnostic potential is staggering”

Mark Viant - University of Birmingham

Since the late 1990s, such metabolomic studies have undergone an

explosive growth and

Entr

ies in

Pub

med

800

1000

1200

1400

explosive growth and this trend is still

continuing, with more than a thousand of papers published in

2010!

Year

1998 2000 2002 2004 2006 2008 2010 2012

Entr

ies in

Pub

med

0

200

400

600

Metabonom*

Metabolom*

Metabonomics “…measurement of the dynamic multiparametric

metabolic response of living systems to pathophysiological stimuli or

genetic modification…” Nicholson et al., 1999

Metabolomics “...the complete set of metabolites/low-molecular-weight

What’s in a name?

intermediates, which are context dependent, varying according to the

physiology, developmental or pathological state of the cell, tissue,

organ or organism…” Oliver 2002

MetabolomicaMetabolomica

AnalisiAnalisi deldel profiloprofilo ((delladella concentrazioneconcentrazione didi ununparticolareparticolare metabolitametabolita oo didi unauna specificaspecificaclasseclasse))

AnalisiAnalisi dell’improntadell’impronta ((delladella presenzapresenza eeconcentrazioneconcentrazione didi tuttitutti ii metabolitimetabolitievidenziati,evidenziati, siasia purepure nonnon tuttitutti noti,noti, ee daldalconfrontoconfronto concon impronteimpronte campionecampione perperevidenziareevidenziare alterazionialterazioni dovutedovute aa malattie,malattie,esposizioneesposizione aa tossine,tossine, alterazionialterazioni genetichegeneticheoo impattoimpatto ambientaleambientale))

Metabolomica: Metabolomica: alcuni obiettivialcuni obiettivi

ValutareValutare eventualieventuali correlazionicorrelazioni tratraimprontaimpronta metabolicametabolica ee malattiamalattia

((sarebbesarebbe cosìcosì possibilepossibile disporredisporre didi nuovinuovistrumentistrumenti perper approfondireapprofondire leleconoscenzeconoscenze susu determinatedeterminate patologiepatologie))


CercareCercare didi capirecapire sese siasia possibilepossibile diagnosticarediagnosticareee valutarevalutare lolo stadiostadio didi avanzamentoavanzamento didi unaunamalattiamalattiamalattiamalattia

((unauna diagnosidiagnosi piùpiù precoceprecoce deidei tumoritumori didi quellaquellaattualmenteattualmente possibile,possibile, perper esempio,esempio,permetterebbepermetterebbe didi salvaresalvare ilil 3030%% didi malatimalatiutilizzandoutilizzando ii farmacifarmaci attualmenteattualmente disponibilidisponibili))


ScoprireScoprire nuovinuovi biomarkerbiomarker

((quelliquelli attualiattuali utilizzatiutilizzati perper lala diagnosidiagnosi didialcunealcune patologiepatologie potrebberopotrebbero nonnonessereessere gligli uniciunici e/oe/o ii piùpiù efficientiefficienti))


StudiareStudiare ii metabolitimetaboliti connessiconnessi aa specificispecificipathwaypathway metabolicimetabolici

((sarebbesarebbe possibilepossibile definiredefinire deidei nuovinuovibersaglibersagli perper farmacifarmaci futurifuturi ee valutarevalutarel’impattol’impatto didi quelliquelli attualiattuali permettendopermettendounauna personalizzazionepersonalizzazione avanzataavanzata delladellaterapiaterapia))

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, Includes peptides, oligonucleotides, sugars, nucelosides, organic acids, ketones, aldehydes, amines, amino acids, lipids, steroids, alkaloids and drugs (xenobiotics)

Includes human & microbial products

Concentration > 1µM

Com pound class N um ber C om pound cla ss N um ber

A cy l g lyc ines 10 Indo les and indo le derivatives 12

Acyl phosphates 10 Ino rgan ic ions and gase s 20

A lcoho l phosphates 2 K eto ac id s 8

A lcoho ls and po lyo ls 40 K etones 6

A ldehydes 3 L euko trienes 8

A lkanes and a lkenes 10 M inera ls and e lem ents 40

Am ino ac id phosphates 1 M isce llaneous 77

Am ino ac ids 114 N uc leosides 24

Am ino a lcoho ls 14 N uc leotid es 24

Am ino ketones 14 P eptid es 21

Arom atic ac id s 22 Phospho lip id s 2177

B ile acid s 19 Po lyam ines 11

B iotin and deriva tives 2 Po lypheno ls 22

Carbohydra tes 35 Po rphyrin s 6

Carn itin es 22 P rostano id s 23

Catecho lam ines and deriva tives 21 P terin s 14

Cobalam in deriva tes 4 P urines and purine d eriva tives 11

Coenzym e A deriva tives 1 P yridoxa ls and derivatives 7

Cyclin am ines 9 P yrim id ines and pyrim id ine deriva tives 2

D icarboxylic ac ids 17 Q u inones and derivatives 3

Fatty ac id s 65 R etino id s 11

G lucoron ides 8 Sph ingo lip id s 3

G lycero lip id s 1070 S tero id s and ste ro id derivatives 109

G luyco lip ids 15 Sugar pho sphates 9

H ydroxy acid s 129 T ricarboxylic ac id s 2

H2N

O

OH

Glycine

NH2

O

OH

NH2

HN

NH

H2N

O

OH

Arginine

OHHO

ONN

H2N

N

N

PO

O

OH

O

P

O

OH

O

PO

OH

OH

Esempio di metaboliti

NH

Tryptophan

OHHO

Adenosine-5'-triphosphate

O

O

OH

Pyruvic acid

O

OH

O

HO

Succinic acid

O

O

HO

O

OH

Oxaloacetic acid

Acetyl CoA

Why 1 µµµµM?

Equals ~200 ng/mL

Limit of detection by NMR

Limit of facile isolation/separation by 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 phenotypesAssess/identify phenotypes

Monitor gene/environment interactions

Track effects from toxins/drugs/surgery

Monitor consequences from gene KOs

Identify functions of unknown genes

Generate metabolic “signatures” for disease states or host responses

Obtain a more “holistic” view of metabolism (and treatment)

Medical Metabolomics

(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:

MetabolomicaMetabolomica

Pochi metaboliti di riferimento per ogni specifica patologia

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 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?Chemistry?

Throughput(more metabolites, greater accuracy, higher speed)

+

New Metabolomics Approaches

+

Impronta digitale metabolica

AdvantagesMeasure multiple (10’s to 100’s) of metabolites at once – no separation!!

Allows metabolic profiles or “fingerprints” to be generatedbe generated

Mostly automated, relatively little sample preparation or derivitization

Can be quantitative (esp. NMR)

Analysis & results in < 60 s

• Quantitative, very

fast

• Requires no work

up or separation

• Quite fast

• Very sensitive

• Allows analysis or

ID of 3000+ cmpds

NMR versus MS

• Allows analysis of

300+ cmpds at

once

• Not sensitive

ID of 3000+ cmpds

at once

• Not quantitative

• Requires work-up

1234567ppm

Two approaches:Two approaches:•• Identify as many metabolites as possibleIdentify as many metabolites as possible•• Use the whole spectrum as a fingerprint (statistics)Use the whole spectrum as a fingerprint (statistics)

2 Routes to Metabolomics

1234567ppm

hippurate urea

allantoin creatininehippurate

2-oxoglutarate

citrate

TMAO

succinatefumarate

water

creatinine

taurine

1234567ppm

-25

-20

-15

-10

-5

0

5

10

15

20

25

-30 -20 -10 0 10

PC1

PC2

Quantitativemethods

Chemometric methods(fingerprinting and pattern recognition)

Quantitative vs. Chemometric

• Identifies compounds

• Quantifies compds

• Concentration range of

1 µM to 1 M

• No compound ID

• No compound conc.

• No compound

concentration range1 µM to 1 M

• Handles wide range of

samples/conditions

• Allows identification of

diagnostic patterns

• Limited by DB size

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 cellReflect 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

Use of NMR in metabolomics studies

Advantages:

Non-destructive, non-biased

Easily quantifiable

Requires little or no separation

Permits identification of novel compoundsPermits 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)Usually large sample size (500 µL)

Processo SperimentaleProcesso Sperimentale

Raccolta e stoccaggio

dei campioni

Preparazionedei campioni

MisureNMR

Elaborazionedegli spettri

Assegnamento dei segnali

Analisi Statistica

Database di composti di riferimento

ProfiloImpronta

NMR ExperimentA 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.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 1H NMR di urina umana

Profilo Profilo 11H NMR di urina umanaH NMR di urina umana

Profilo Profilo 11H NMR di urina umanaH NMR di urina umana

Proteins + Lipids + Small molecules

Profilo di siero umanoProfilo di siero umano

Lipids + Small molecules

Lipids + Proteins

Lipids

serum

urine

saliva

fecal extract

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:

Data analysis - approach

modeling:

Spectral binning: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

PCAUnsupervised

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 theoriginal data parameters

Each successive PC explains the maximum amount of variancepossible, not accounted for by the previous PCspossible, not accounted for by the previous PCs

PCs Orthogonal to each other

Conversion of original data leads to two matrices, known asscores and loadings

The scores(T) represent a low-dimensional plane that closelyapproximates X. Linear combinations of the originalvariables. Each point represents a single sample spectrum.

A loading plot/scatter plot(P) shows the influence (weight) of theindividual X-variables in the model. Each point represents adifferent spectral intensity.

The part of X that is not explained by the model forms theresiduals(E)

X = TPT = t1p1T + t2p2T + ... + E

PCA Plot Nomenclature

• PCA Generate 2

kinds of plots, the

scores plot and the

loadings plot

• Scores plot (on • Scores plot (on

right) plots the data

using the main

principal

components

Z = X WZ = X Wscores loading

original

data

PCA Loadings Plot

• Loadings plot shows

how much each of the

variables

(metabolites)

contributed to the contributed to the

different principal

components

• Variables at the

extreme corners

contribute most to the

scores plot separation

PCA Details/AdviceIn 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 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 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

PLSSupervised 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) samples)

The quantitive relationship between the two tables is sought.

X = TPT + E

Y = TCT + 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 Main idea to seperate systematic variation in X into two parts, one linearly related to Y and one unrelated (orthogonal).

Comprises two modeled variations, the Y-predictive (TpPpT) and the Y-orthogonal (T

oPoT) compononents.

Only Y-predictive variation used for modeling of Y.

X = TpPpT + T

oPoT + E

Y = TpCpT + F

E and F are the residual matrices of X and Y

OPLS-DA compared to PLS-DA

Remarks on pattern classificationIntent 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 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.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 methodschemometric and targeted profiling methods

Newly emerging databases

HMDB good model for other databases

Challenge of standardisation

Databases

Metabolomica: Metabolomica: Fattori di variabilitàFattori di variabilità

SessoSesso

EtàEtà

DietaDietaDietaDieta

Ritmi fisiologiciRitmi fisiologici

GenotipoGenotipo

StressStress

PatologiePatologie

Effetto della dietaEffetto della dieta

Consumo di pesce

Trimetilammina N-ossido

Consumo di chewing-gum, caramelle etc..

Mannitolo

4 .00 3.90 3.80 3.7 0 3.60

NON Consumo di pesce

Mannitolo

Ind 1

Effetto del profilo individualeEffetto del profilo individuale

62

Ind 2

10.00 7.50 5.00 2.50 ppm

Campioni di saliva da individui sani e da individui affetti da periodontite cronica

Effetto delle patologieEffetto delle patologie

Metabolic signature of individualsMetabolic phenotype

Metabolic signature of diseases• Celiac disease

• tumor → metastasis (breast, colorectal)

• cardiovascular risk

Our interest in metabolomicsOur interest in metabolomics

• cardiovascular risk

• diabetes

• pulmonary diseases

• …

Metabolites and biobank samples• Sensitive reporters of stability

• Assess sample preparation and preanalytical procedures

• …

METabolomic REFerenceMETabolomic REFerence

Why?

The METREF project

Looking first at urine of healthy individuals, and developing a

feeling for the intraindividual vs. interindividual variations

• Training

• Urine samples are easy to collect

• Large number of samples

• Potential intrinsic value of the information

Why?


• 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

Experimental scheme:

• Collection suspended in case of illness; otherwise no restrictions

• Data recording:DietDrugsLifestyle, general habitsSmoker / No Smoker

• NMR analysis: 1D 1H spectra


Ind 1

Getting a first feeling…

Visual inspection suggests that it should be interesting to look for individual Visual inspection suggests that it should be interesting to look for individual fingerprints by fingerprints by statistical analysisstatistical analysis

Ind 2We believe the human eye is very sensitive to differences in patterns

METabolomic REFerenceMETabolomic REFerenceConvex hulls of 22 donors in the three most significant PCAConvex hulls of 22 donors in the three most significant PCA--CA dimensionsCA dimensions

PCA for data PCA for data reduction reduction

CA for CA for obtainobtainwell separated well separated

“natural” gender discrimination“natural” gender discrimination

Assfalg, Bertini, Colangiuli, Luchinat, Schäfer, Schütz, Spraul, Assfalg, Bertini, Colangiuli, Luchinat, Schäfer, Schütz, Spraul, PNASPNAS, , 20082008, 105, 1420, 105, 1420--44

well separated well separated clustersclusters

KNN for KNN for classificationclassification

99% accuracy 99% accuracy in montecarlo in montecarlo cross validationcross validation

MALEMALEFEMALEFEMALE

METabolomic REFerenceMETabolomic REFerenceDendrogram of the 22 donors on the 21Dendrogram of the 22 donors on the 21--dimensional PCAdimensional PCA--CA subspaceCA subspace

Assfalg, Bertini, Colangiuli, Luchinat, Schäfer, Schütz, Spraul, Assfalg, Bertini, Colangiuli, Luchinat, Schäfer, Schütz, Spraul, PNASPNAS, , 20082008, 105, 1420, 105, 1420--44

METabolomic REFerenceMETabolomic REFerenceGut microflora related metabolites

Concentrations of 12 selected metabolites for each donor. Absolute creatinine concentration (Crea) and relative metabolite

concentrations (relative to creatinine)


An individual metabolic fingerprint exist!But it is hidden inside the daily noise

Assfalg, Bertini, Colangiuli, Luchinat, Schäfer, Schütz, Spraul, Assfalg, Bertini, Colangiuli, Luchinat, Schäfer, Schütz, Spraul, PNASPNAS, , 20082008, , 105105, , 14201420--44

METabolomic REFerence 2METabolomic REFerence 2

• Expanding the dataset

• Trying to learn more about relevance of genetic vs lifestyle contributions

• Check the constancy of metabolic phenotypes over time

Why Healthy Individuals Again?


• 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

Experimental Scheme (2 years later)

• First in the morning preprandial

• Collection suspended in case of illness

• Data recording:DietDrugsLife styleSmoker / No Smoker

• NMR analysis: 1D 1H spectra

METREF 1,2,3

MetRef12005

MetRef2

11

father

& son

t

47 22

MetRef22007

MetRef32008

7

4

2

twins

5 2t 420

4


AD 99.905%AF 100.000%AG 100.000%AH 99.922%AI 99.760%AO 97.500%AP 97.500%AR 100.000%AS 100.000%AT 99.995%AU 100.000%

2005 collection 2007 collection

AI 100.000

%AO 100.000%AR 99.995%AS 99.941%AU 100.000%AW 99.998%BC 99.705%BF 99.629%BG 100.000%BH 100.000%

PCA-CA-KNN classification results

AU 100.000%AW 100.000%AX 100.000%AZ 100.000%BC 99.998%BD 100.000%BE 100.000%BF 100.000%BG 99.933%BH 100.000%BI 100.000%BK 99.470%

BH 100.000%BI 98.462%BQ 99.998%BS 100.000%BT 99.983%BU 96.647%BV 99.998%BX 99.998%BZ 100.000%TA 98.450%TB 98.235%

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


genes

lifestyle etc.

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

• 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 biomedicipotenziale per studi biomedici

MetRef1

Un “salto” metabolicoUn “salto” metabolico

Evoluzione del profilo metabolico di un individuo nell’arco di tre anni

MetRef2

MetRef3

Hippurate

Celiac Disease MetabolomicsCeliac Disease MetabolomicsWhat 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 MetabolomicsCeliac Disease Metabolomics

• Study subjects: 34• Control subjects: 34• Samples: Serum and Urine

NMR spectra acquired:

• 1D Noesy (standard 1D 1H spectra) for serum and urine samples

Experimental scheme:

• 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


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%


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 MetabolomicsCeliac Disease MetabolomicsSignificantly different metabolites in

serum (p<0.01)Significantly different

metabolites in urine (p<0.01)

Already known NAC = N-acetyl-


Celiac disease often associated with fatigue: Celiac disease often associated with fatigue: Why ?Why ?

Increased glucose, decreased pyruvate, lactate:Increased glucose, decreased pyruvate, lactate:Impaired glycolysis, impaired energy production Impaired glycolysis, impaired energy production

Lipid betaLipid beta--oxidation + use of ketonic bodies:oxidation + use of ketonic bodies:Alternate less efficient energy productionAlternate less efficient energy production


Follow-up analysis

Samples at 3, 6, 9, 12 months from diagnosis

Serum and urines

Metabolite profilingMetabolite profiling

Patter recognition

Using CPMG sera, all but one samples after 12 months on a gluten-free diet are classified as normal!


Clusterization of Celiac and Healthy subject serum spectra



Clusterization of Celiac and Healthy subject serum spectraand corresponding Follow-up


Subjects: 134Celiacs: 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)

Potential celiac diseasePotential celiac disease

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.

Serum CPMGSerum CPMGSerum CPMGSerum CPMG• Accuracy : 63.7 %63.7 %63.7 %63.7 %• Sensivity : 81.2 %81.2 %81.2 %81.2 %• Specifity : 19.7 %19.7 %19.7 %19.7 %

Serum NoesySerum NoesySerum NoesySerum Noesy

Celiacs Vs Potential CeliacsCeliacs Vs Potential CeliacsCeliacs Vs Potential CeliacsCeliacs Vs Potential Celiacs

Serum CPMGSerum CPMGSerum CPMGSerum CPMG• Accuracy: 82.3 %82.3 %82.3 %82.3 %• Sensivity: 82.3 %82.3 %82.3 %82.3 %• Specifity82.9 %82.9 %82.9 %82.9 %

Serum NoesySerum NoesySerum NoesySerum Noesy

CeliacsCeliacsCeliacsCeliacs Vs Vs Vs Vs HealthyHealthyHealthyHealthy

Potential Celiac DiseasePotential Celiac Disease

Serum NoesySerum NoesySerum NoesySerum Noesy. . . . Accuracy : 64.9 %64.9 %64.9 %64.9 %• Sensivity : 81.8 %81.8 %81.8 %81.8 %• Specifity : 24.7 %24.7 %24.7 %24.7 %

Urine NoesyUrine NoesyUrine NoesyUrine Noesy• Accuratezza : 59.9 %59.9 %59.9 %59.9 %• Sensivity : 79.0 %79.0 %79.0 %79.0 %• Specifity: 11.3 %11.3 %11.3 %11.3 %

Serum NoesySerum NoesySerum NoesySerum Noesy• Accuracy : 74.4 %74.4 %74.4 %74.4 %• Sensivity: 77.6 %77.6 %77.6 %77.6 %• Specifity : 70.1 %70.1 %70.1 %70.1 %

Urine NoesyUrine NoesyUrine NoesyUrine Noesy• Accuracy : 69.4 %69.4 %69.4 %69.4 %• Sensivity : 73.3 %73.3 %73.3 %73.3 %• Specifity: 64.1 %64.1 %64.1 %64.1 %

Celiachia PotenzialeCeliachia Potenziale

Celiaci – Sani –

Croci: predizione dei Celiaci Potenziali

Esiste una impronta metabolica della celiachia

Queste alterazioni sono presenti anche nei celicaci potenziali: esse precedono il danno

intestinale

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

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

Celiaci Potenziali: soggetti con anticorpi positivi

alla gliadina ma senza presenza di danno

intestinale. NON sono celiaci e NON vengono

messi a dieta

Spettro NMR di olio di oliva

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.

Prime esperienze con olio acquisite alcuni anni fa (Prof. Luchinat)

Chemistry and Physics of Lipids, (1999), 103, 47-55.

OLIO DI OLIVA EXTRAVERGINE

OLIO DI OLIVA

OLIO DI GIRASOLE

ββββ SITOSTEROLO

ACIDO LINOLENICO REGIONE DEI METILI REGIONE DEI METILI ((11H)H)

OLIO DI ARACHIDI

OLIO DI SOIA

OLIO DI MAIS

ATTRIBUTI SENSORIALI

Olio amaro

Olio avvinato

Cattiva separazione dalle acque di vegetazione

Olio pungente

Olio fruttato

OLIO EXTRAVERGINE

PRODOTTI DI OSSIDAZIONE QUASI ASSENTI IN UN OLIO BUONO

OLIO RANCIDO

Olio Siciliano Olio Umbro

CARATTERIZZAZIONE GEOGRAFICA (1H)

10

TCA LDA

CARATTERIZZAZIONE

GEOGRAFICA (1H):

OLI TOSCANI

Seggiano Lucca Arezzo

S

AR

A

Root 1R

oot

2

-8

-6

-4

-2

0

2

4

6

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

CARATTERIZZAZIONE

GEOGRAFICA (1H):

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

Spettro 1H NMR di latte

*Lattosio

Citrato

Acidi grassi

insaturi

Creatina

Lecitina

*Glicerolo

Acidi

grassi

Spettro 1H 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 1H 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

Spettro 1H NMR di latte di capra

Formato

Piruvato Acetato

Capra

Mucca

Spettro 1H NMR di latte UHT

Formato

PiruvatoAcetato

Fresco

UHT

(

Spettro 1H NMR di latte UHTformato Granarolo UHT

Granarolo Italia UHT

Maremma UHT

Mukki UHT

COOP UHT

acetatopiruvato

Formato acetato e

piruvato sono

caratteristici dei latti

UHT, anche se in

quantità diverse

Spettro 1H NMR di latte ω3

Acidi grassi

insaturi

Intero

ω3

Disponibilità di Tesi inMETABOLOMICA

presso ilCentro di Risonanze Magnetiche

dell’Università di Firenze

Argomento di Ricerca:

Analisi Metabolomica di Fluidi Biologicitramite 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: [email protected] informazioni: [email protected]

Documents

Leonardo Tenori - UniFI · Human Genome Project since 1990s. According to one etymological analysis, the suffix 'ome' is derived from the Sanskrit OM ("completeness and fullness")