foodómica, herramienta para el estudio de … · insulina proantocianidinas. endocrinology 2004;...
TRANSCRIPT
Foodómica, herramienta para el estudio de componentes bioactivos
de alimentosLluís Arola Ferrer
CTNS-URV
2Big science at the table, Lucas Laursen, NATURE | VOL 468 | 23/30 December 2010
Ideas
Componentes bioactivos de los alimentos como señales celulares
Biomarcadores
3
4
TranscriptomicsTranscriptomics
ProteomicsProteomics
MetabolomicsMetabolomics
Non‐hypothesis driven
High throughput
NOISY
MASSIVE DATA!
OMICS Integration???
GenomicsGenomics
5
The term nutrigenomics was coined ten years ago to describe a branch of nutrition and food research that applies new profiling techniques for transcripts, proteins and metabolites to better understand the interplay of the genome with its nutritional environment. In this respect, nutrigenomics is still in its infancy and it will need time until it really delivers what was hoped. Although all technologies are attractive to use and promise to deliver huge data sets with new insights into mechanisms for dietary adaptation, we have learned that it is not trivial to collect the data and it is even more difficult to analyze and extract meaning from them. Although the number of nutrigenomics studies is rapidly growing, most applications are still of descriptive nature. Technologies are maturing but reproducibility and robustness are still in need of improvement.
In contrast, nutrigenetics with SNP or haplotype analysis is more easily performed is highly accurate and robust from a technological perspective. Genetic variations in numerous genes can be associated with disease risks and susceptibility, although each SNP alone may have only a minor impact. However, each individual’s susceptibility can be modulated by diet and this forms one of the conceptual lay-outs for nutritional requirements on an individual level. It can be predicted that future studies will employ all combinations of the different nutrigenomics techniques in the same study and including comprehensive genotyping of the volunteers. Yet, it can also be foreseen that huge knowledge gaps remain in the understanding of human nutrition and physiology.
6
Genomics Genomics
TranscriptomicsTranscriptomics
Proteomics Proteomics
MetabolomicsMetabolomics UntargetedNon‐hypothesis‐driven
TargetedHypothesis‐driven
TargetedHypothesis‐driven
TargetedHypothesis‐driven
Hypothesis‐driven Omics Integration Bottom‐up Omics Integration
7
FLAVONOIDES
J Am Diet Assoc. (2010) 110:390-8
60,1%
Total oligómeros + polímeros
10,3 %
Total monómeros
Oligomers and polymers
8
ALIMENTOS RICOS EN FLAVANOLES
USDA Database for the Proanthocyanidin Content of Selected Foodshttp://www.nal.usda.gov/fnic/foodcomp/Data/PA/PA.html
mg/100g edible portion
mg/100g edible portion
9
EFECTOS BIOLÓGICOS DE LAS PROANTOCIANIDINAS
Estudios epidemiológicos
Reducción de la mortalidad por causas cardiovasculares
Reducción en la incidencia de determinados tipos de cáncer
Estudios de intervención, con animales o in vitro
Antioxidantes
Antiinflamatorios
Anticancerígenos
Hipolipidémicos
Antidiabéticos
Hipotensores
Antimicrobianos
etc.
10
Un numero sorprendente de fitoquímicos de la dieta interaccionan con reguladores clave de la fisiología de los mamíferos promoviendo beneficios de salud.
¿QUÉ SENTIDO BIOLÓGICO TIENE?
Xenohormesis: Sensing the Chemical Cues of Other SpeciesKonrad T. Howith and David A. Sinclair; Cell, 133, 387-391, 2008
11Xenohormesis: Sensing the Chemical Cues of Other SpeciesKonrad T. Howith and David A. Sinclair; Cell, 133, 387-391, 2008
12
¿No tan solo los fitoquímicos?
13
VÍAS DE SEÑALIZACIÓN
EXPRESIÓN GÉNICA
DIETA
CRECIMIENTO CELULAR NORMAL
FACTORES DE TRANSCRIPCIÓN
Destino y actividades de los nutrientes en la célula
microRNA
INGREDIENTES
Zn
MODULACIÓN HEMOSTASIS
NUTRIENTES
14
Componentes bioactivos de los alimentos como señales celulares
Aproximación ómica
Tratamiento crónico de ratas normales con proantocianidinas de pepita de uva
Tratamiento crónico de ratas obesas con proantocianidinas de pepita de uva
Interacción proantocianidinas con ligandos
Factores de transcripción
microRNA
Zn
15Br. J. Nutr. 2010; 103: 944–952
GSPE
OHO
OH
OH
OH
OH
HO
OH
O
OH
OH
OH
HO
OH
O
OH
OH
OH
n
16
14 h ayuno
Sonda oral 2,5 ml manteca/kg
peso + 0, 5 o
25 mg
GSPE/kg
peso
3h
SacrificioHÍGADO
Administración crónica de GSPE durante 21 días2 ml leche condensada /kg
peso + 0, 5
o 25
mg
GSPE/kg
peso
Vehiculo
+ GSPEVehiculo
Ratas Wistar
Agilent
Microarrays
RMN
TRANSCRIPTÓMICA
METABOLÓMICA
Aproximación ómica ADMINISTRACIÓN CRÓNICA GSPE
17
235
5 mg/kg
83
25 mg/kg
28
55
3547
82
138
97
Cambios significativos de expresión
Aproximación ómica TRANSCRIPTÓMICA NO DIRIGIDA
RELEVANCIA BIOQUÍMICA:
Ciclo celular
Ensamblaje proteico
Apoptosis
Metabolismo lipídico
Inflamación
Señalización celular
18
14 h ayuno
Sonda oral 25 mg
GSPE/kg
peso
3h
SacrificioHÍGADO
Administración crónica
de GSPE durante 10 días2 ml leche condensada
/kg
peso + 25 mg
GSPE/kg
peso
Vehiculo
+ GSPEVehiculoRatas Wistar
nLC asociada a LTQ-Orbitrap
PROTEÓMICA
15 semanasDieta de cafeteria
ad libitum
Aproximación ómica GSPE CRÓNICA EN OBESIDAD
19
Aproximación ómica PROTEÓMICA NO DIRIGIDA
A.HFD induce síndrome metabólico y activa glicogénesis, glucólisis y síntesis de AG y TAG
B.HFD y administración de 25mg/kg peso de GSPE durante 10 días, revierte la situación
Molecular & Cellular Proteomics, 2010, 9, 1499
20J. Nutr. Biochem., 2011; 22, 380
Aproximación ómica DIRIGIDA
21
CDCA
SHPFXR + SHP
SREBP1
+
Lipid synthesis
-
-
Proantocianidinas
VLDL
Interacción con ligandos: FACTORES DE TRANSCRIPCIÓN
Mol Nutr Food Res. 2009; 53: 805Int. J. Obes. 2009; 33:1007Mol. Nutr. Food Res.; 2010, 54: 37
22Free Rad. Res. 2011; 45: 611
Interacción con ligandos: FACTORES DE TRANSCRIPCIÓN
23
pY
pY
pY
pY
pY
pY
PI-3-quinasa
IRS 1/2
p85
p110
pY
GLUT-4
Gluc
Gluc
W t i
SB 203580
p38-MAP quinasa
GLUT-4
pReceptorde Insulina
Insulina
Proantocianidinas
Endocrinology 2004; 145:4985
J. Nutr. Biochem. 2010; 21: 476
Interacción con ligandos: FACTORES DE TRANSCRIPCIÓN
24
Tratamiento células HepG2 con:
Extracto pepita de uva (100 mg/l)
Extracto de cacao (100 mg/l)
Epigalocatequina galato (50 mg/l)
Array microRNA
Interacción con ligandos: microRNA
0,1 % DMSO (Control)0,1 % DMSO + different treatments
5h treatment Total RNA extraction
HepG2 cells
miRNA-specific reverse- transcription (Applied Biosystems)
25PLoS ONE, 2011; 6 (10), e25982
Interacción con ligandos: microRNA
Explica muchos cambios inducidos por las proantocianidinas
La FAS es una de sus dianas
26
Tratamiento células HepG2 con:
Extracto pepita de uva (150 mg/l)
Array
Interacción con ligandos: Zn
Control5M Zn
12h treatment
Total RNA extraction
HepG2 cells
La expresión de los genes de la metalotioneina y de los transportadores de Zn de las familias ZnT y ZIP está fuertemente modificada por acción del extracto de proantocianidinas.
27
Núcleo
wwwwwwwwwwwwwwww
GolgiCitoplasma
Mitocondrias
´Retículo
endoplásmico
Alb ZIP1
ZnT5
ZnT1
ZIP7
ZIP7
Metalo-
tioneína
Apo-
tioneína
Metalo-
tioneína
G-SZn
G-SH
MTF1
MTF1
MTs ZnT1 ɣGCS
MTF1
Apo-
proteínas
Zn-Metalo-
proteínas
Zn-Metalo-
proteínas
Proteínas
sin Zn
Zn2+
lábilnM ZnT10
ZnT6
ZnT7
ZnT5
ZnT6
ZnT7ZIP4
ZIP6
ZIP10
ZIP13
Zn-Metalo-
proteínas
Zn2+
lábilnM
Zn2+
Zn2+
TOTALμM
Zn2+
TOTALμM
Conjunto de efectos similares a los de quelantes
extracelulares de Zn
J. Nutr. Biochem., 2011; 22, 153–163
28
Biomarcadores
BIOCLAIMS will provide BIOmarkers of Robustness of Metabolic Homeostasis for Nutrigenomics-derived Health CLAIMS Made on Food
Development of an in vivo model of dyslipidemia
Detection of early biomarkers of dyslipidemia
29
Basal Estrés
Señal amplificada
FríoAyunoInfecciónO2PresiónEjercicioXenobióticosDaño tisularDaño DNAFFsDietasD.Hiperprot.Dieta GrasaEstrés cognit.Inflamación…
30
Development of an in vivo model of dyslipidemia
GOLDEN SYRIAN HAMSTERS
Suitable model
for
cholesterol and
lipoprotein
studies:
common
metabolic features with humans:
‐Low rate of hepatic cholesterol synthesis
‐Substantial
amount
of
cholesterol
are
transported
in
LDL
when
the diet contains cholesterol
‐Bile acid synthesis is not activated by dietary cholesterol
‐Receptor‐dependent and independent uptake of LDL
exist
‐The liver produces exclusively ApoB
100 isoform
31
High fat diet: BCD + lard instead of wheat starch (21% energy from fat) + 0.1% cholesterol
Bioclaims control diet (BCD):10% energy from fat (sunflower, flaxseed and coconout oils)
Sacrifice (after 5h of fasting):•Biometric parameters: body weight, adiposity and liver weight•Blood parameters: TG, CHOL, lipoprotein profile, phospholipids, glucose, insulin, leptin•Tissues collection: brown and white adipose tissue, liver, brain, kidney, muscle…•PBMC isolation: RNAm isolation for transcriptomics•Plasma collection: metabolomics
30d4d 15d
-
Dyslipidemia
degree +
Control diet
High
fat
diet
4d
Males12‐week‐old
Development of an in vivo model of dyslipidemia
32CONTROL
HIGH
FAT
050100150200250300
Triglyceride
s(m
g/dL) Plasma triglycerides
0 4 15 30Time (days)
*
Plasma cholesterol
100
150
200
250
300
Cholesterol(mg/dL)
0 4 15 30
Time (days)
** *
0 4 15 30
Plasma phospholipids
200
250
300
350
400
450
Phosph
olipids(m
g/dL)
Time (days)
* *
* vs
control animals
(Student’s
t‐test, p<0.05)
Development of an in vivo model of dyslipidemia
Chol HDL / Chol LDL
0
1
2
3
4
0 4 15 30
ratio
*
**
Time (days)
33
Light
Dyslipemia
degree
DyslipidemicControl (Healthy) Moderate
TRANSCRIPTOMIC
ANALYSIS
(microarrays)
METABOLOMICANALYSIS
(HPLC‐MS)
PBMC
PLASMA
ONLY
2800 GENOMIC
SEQUENCES
OF
HAMSTER
IN NCBI
ONLY
2800 GENOMIC
SEQUENCES
OF
HAMSTER
IN NCBI
HAMSTER
MICRROARRAY
NOT
AVAILABLE
HAMSTER
MICRROARRAY
NOT
AVAILABLE
DEVELOPMENT
OF
HAMSTER
MICROARRAY
DEVELOPMENT
OF
HAMSTER
MICROARRAY
?
Detection of early biomarkers of dyslipidemia
34
DEVELOPMENT
OF
HAMSTER
MICROARRAYDEVELOPMENT
OF
HAMSTER
MICROARRAY
Total RNA
isolation
(CTNS)
mRNA
sequentiation
(CRG)
(pool of
different
tissues: blood, liver, brain, WATs, iBAT, spleen, muscle, kidney…)
(PolyA+ selection, transcription
(cDNA) and
sequentiation
Illumina
Genome
Analyzer
IIx
Sequence
indentification
(qGenomics
and
CTNS)
De novo sequence
assembly
(qGenomics
and
CTNS)
40000
identified sequences matching 16600
different
genes+111000 sequences not identified (yet)
1,4x106
sequences
(contigs)
Probe
design
(qGenomics
and
CTNS)
Microarray
design
and
validation
(qGenomics
and
CTNS)
Blast against Human,
Mouse and Rat RefSeqs
allowed the identification
and optimization of the
sequences
Detection of early biomarkers of dyslipidemia
35
METABOLOMICS ANALYSIS: Set up of
the
LC‐MS analysis
in plasma samplesMETABOLOMICS ANALYSIS: Set up of
the
LC‐MS analysis
in plasma samples
Extraction
of
metabolites
from
100 μL of
plasma. MS detector: time of flight (TOF)
Analysis
of
data
by
means
of
the
XCMS
software.
XCMS
provides
methods
for
feature
detection,
non‐linear retention
time alignment, visualization, relative
quantization
and
statistics
and
is
capable
of
simultaneusly
analyzing
and
visualizing
the
raw
data from
hundreds
of
samples.
Ionization: Electrospray
(ESI) in both
positive and
negative
modes
Detection of early biomarkers of dyslipidemia
36
EQUIPO INVESTIGADOR
INVESTIGADORES
A. Ardèvol
A. Arola
L. Arola
C. Bladé
M. Blay
J. Fernández
S. García
M. Mulero
B. Muguerza
M. Pinent
G. Pujades
A. Romeu
J. Salvadó
TÉCNICAS
I. Baiges
V. Grifoll
N. Llópiz
Y. Tobajas
PRE-DOC
L. Baselga
E. Casanova
A. Castell
L. Cedó
A. Ceretò
H. Dabbagh
A. Fernández
R. Gordillo
N. González
L. Guerrero
M. Margalef
N. Martínez
M. Ojeda
V. Pallarés
A. Ribas
C. Rojas
POST-DOC
M. Quiñones
M. Suárez
ESTUDIANTES
K. Acosta
Z. Pons