Nutrigenómica y Obesidad. Alfredo Martínez

NUEVOS ENFOQUES EN EL TRATAMIENTO DE LA
OBESIDAD.
¿ESTAMOS EN EL BUEN CAMINO?
NUTRIGENÓMICA DE LA OBESIDAD Y NUTRICION
PERSONALIZADA BASADA EN GENOTIPO
J Alfredo Martínez
• University of Navarra.
Pamplona
¿NUTRIGENÓMICA Y PERSONALIZACION DE LA OBESIDAD ?
1
SNC
Neuropéptidos
R
Adctividad
Fisica
estructure
BMR
VITs
INGESTA
Termogeneis
Lipidos
Protein
as
Alcohol
mins
Señales eferentes
CHO
Señales aferentes
DEMANDAS
Genética / hábitos / Estado neuroendocrino
Balance Nutricional
BALANCE ENERGÉTICO
Balance E. =
Grasa?
¿Obesidad?
Ingesta
Alimentos
Bebidas
- Gasto.
Metabolismo Basal
Actividad física
Termogénesis
¿Genética/Habitos?
2
FUNCIONES de NUTRIENTES
Nutrientes
Energía
Elementos Reguladores Funciones Estructurales
Proteínas
EXPRESSIÓN GÉNICA: CONTROL
DNA
Transcripción
Ac. Grasos
Retinoides
Vitamina D
Glucosa
Energía
mRNA
Translación
Aminoácidos
Hierro
Selenio
Proteínas
Postranslación
Minerales
Vitaminsa
3
EXPRESSION GENICA: CONTROL
Cascada
quinasas
FT
RE
Nutrientes
Nutrientes &
Metabolitos
●
Nutrients
Mechanismos : - Receptores (membrana/núcleo)
- Metabolismo intermediario
- Factores Transcripción (FT): afinidad & concentración
GENOTIPO → FENOTIPO
DNA
RNA
Transcription
PROTEINAS
Translation
Expresión Génica= f(DNA × factores entorno)
4
HOMEOSTASIS: INTERACCIONES
GENÉTICA
Nutrición
Hormonas
Homeostasis
Act. Física
Sistema Nervioso
F. Ambientales
NUTRICIÓN MOLECULAR
Nutrigenética
Polimorfismos
Genes
Nutrientes
Nutrigenómica
Expresión de genes
NUTRICION PERSONALIZADA
5
GENÉTICA
Genética estudia cómo los organismos vivos heredan
rasgos de sus antepasados y averigua cómo se transmiten
estas características de generación en generación
NUTRICION PERSONALIZADA
La genética determina
necesidades nutricionales únicas
y las respuestas a los diferentes
alimentos y nutrientes
Basado en:
- La secuenciación del genóma humano,
- Análisis de la subsecuenciación de las
variaciones humanas,
-  Estudio de la asociación de variantes genéticas
con marcadores de enfermedad
- Impacto of nutrición en expresión genica
6
GENOTIPO & FENOTIPO: INTERACCIONES
Genotipo
Estado
Nutricional
Expresión génica
GENETICA de la OBESIDAD
Sindromes Mendelianos
•  Autosomal
dominante
•  Autosómico
recesivo
• X-ligado
Modelos Animales
• Obesidad en animales
• Animales transgénicos
•  Q Rasgos Loci (QTL)
Asociación y Ligamiento
Genes candidato
•  Segregación
•  GWAS
• 
7
LIGAMIENTO GENETICO
Framingham
Fenotipo
Canada
BMI
Noruega
BMI/Pliegue
n
r2
0,19
3138
0,12
23936
0,12
Padres/hijos 4027
0,23
7194
0,20
43586
0,20
Hermanos
0,28
3924
0,34
19157
0,24
Esposos
n
r2
1163
IMC
992
n
r2
MODELOS GENETICOS
ANIMAL
Tipo
HUMANO
Chr
Loci
Herencia
Chr Gene Product
Diabetes (db)
4
Lepr
Recesivo
1
leptina receptor
Fat (fat)
8
Cpe
Recesivo
4
carboxìpeptidasa E
Obese (ob)
6
Lep
Recesivo
7
leptina
Tubby (tub)
7
Tub
Recesivo
11
Desconocido?
Agouti yellow
2
Ay
Dominante
20
proteina agouti
8
MODELOS GENETICOS
Alteraciones metabólicas
•  Knock-out de TNF-α (Tolerancia a Glucosa)
•  Knock-out de c/EBP (Diferenciación adipocitos)
•  Knock-out de adrenalin (Termoregulación)
•  Knock-out de LPL (Metabolismo de lípidos)
•  Knock-out de BAT (Termogénesis)
Genotipo → Fenotipo (Obesidad)
30%
Cultural
Cultural
Cultural
transmisión
transmission
transmission
Genética
Genetic
Genetic
25%
Nontransmissible
Nontransmissible
No-transmisible
45%
9
DETERMINANTES DE OBESIDAD INFANTIL
5
4,18
4
3
OR
2,02
2
1
1,74
0,94
Actividad
Fisica
TV
Azúcar y bebidas
azucaradas
Antecedentes
fsmiliares
Ochoa, et al. 2006
Trp64Arg MUTACION en ADRENOCEPTOR β3
0,92
C/C
C.R.
0,91
0,91
0,90
Units
0,9
0,89
0,88
0,87
0,87
0,86
0,86
0,85
0,84
0,83
TT (-)
TA+AA (+)
CORBALAN, et al. 2000
10
PPARγ2 x ADRβ3:INTERACCIONES
6
5
OR
4
3
2
1
0
(-)
PPARү (+)
PPARү+ADRβ3 (+)
Carriers
Ochoa, et al. 2004
OBESIDAD Y GENES
•  ADIPOGENESIS: PPAR, C/EBP5, PKA, AP2,, RXR,...
•  TERMOGENESIS: β3AR, β2AR, UCPS,...
•  APETITO: NPY, LEP, CCK, MC4R, POMC, OREX,..
• OTROS. FTO, IL6
11
OBESIDAD HUMANA: MAPEO
1
2
ACP1
Do1
3
4
GCKR
LEPR
5
6
ACH
Nidd/gk1
Do2
ATP1A2
Obq2
LEP
Mob-4
CHS1
255 Mb
214 Mb
14
15
203 Mb
194 Mb
D11S419
UCP2/3
Nidd/gk5
bw/gk1
DRD2
171Mb
155 Mb
145 Mb
144 Mb
UMS
Weight 1
Mob-1
APOD
183 Mb
12
Niddm1
Do3
KEL
D1S202
11
BBS1
ADRB3
Mob-2
GRL
Nidd/gk6
10
SURTUB
Mob-1
Obq1
PRKAR2
PC1
CPE
Obq2
9
LPL
Do6
Mob-4
UCP 1
Do6
PigQTL
8
GLO1
TNFir24
BBS3
HS
3B1
7
PPAR
POMC
APO B
APOA4
144 Mb
143 Mb
X
236 Mb
13
ESD
16
17
18
Mob-1
19
MC4/5R
LIPC
114 Mb
109 Mb 106 Mb
BBS4
98 Mb
ADA
92 Mb
Y
85Mb
Bw2
Bw3
P1
Obq1
BBS4
Do2
22
Choroideremia
WTS
bw/gk1
ASIP
Niddm3
Mob-3
21
Do6
LDLR
PWS
20
67 Mb
PPCD
Mob-5
72 Mb
56 Mb
28 Mb
Bw1
SGBS
BFLS
50 Mb
164 Mb
LATEST: UCP, POMC, LEP, βAR, PPAR, AP2, TNF,...
GENES y NUTRICION:INTERACCIONES
12
GENOTIPO & NUTRICION INTERACCIONES
Nutricion
Expresion
Génica
Fenotipo
MICROARRAY
13
Microchip scanning
Building the
Chip
Sample collection
RNA isolation
MASSIVE PCR
PCR PURIFICATION
and PREPARATION
ROBOTIC
PRINTING
lean
obese
~~~
Reverse ~ ~ ~ ~
~
transcription
Label with
~~~~~~~~ fluor dyes ~~~~~~~
~
Competitive
~~~~~~~~
Computer data analysis
hybridization
Detection with confocal
laser microscope
Moreno et al. 2001
+
TOOLS: Microchip Scanning
Macronutrient Metabolism
Fold
~49.2
15.7✝
7.3
~6.7✝
~4.9✝
~4.6✝
4.1
3.3
3.2
3
2.9
2.8
2.5
2.5
2.4
2.2
2.2
2.2
2.2
2.2
2.2
2.2
2.2
2.1
2
2
Code
Name
D45862
J02773
J00713
U64451
AF063302
AF034577
M95591
S69874
K03249
AB002558
AB005743
L07114
U20643
M26594
M60322
S56481
S81497
J02585
X15580
L12016
U32314
D10354
L25331
D43623
D10655
AF035943
Leptin, ob
Low molecular weight FABP
Carboxypeptidase-a- 5
Short-branched chain acyl-CoA DH precursor
Carnitine palmitoyltransferase I beta
Pyruvate dehydrogenase kinase isoenzyme 4
Squalene synthetase
Fatty acid-binding protein (FABP)
Enoyl-CoA-hydratase-3-hydroxyacyl-CoA DH
Glycerol 3-phosphate dehydrogenase
Fatty acid transporter
Apolipoprotein B
Aldolase A
Malic enzyme
Aldose reductase
Beta 3-adrenergic receptor
Lysosomal acid lipase
Liver stearyl-CoA desaturase
6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase
Tricarboxylate transport protein
Pyruvate carboxylase
Alanine aminotransferase
Lysyl hydroxylase
Carnitine palmitoyltransferase I like protein
Dihydrolipoamide acetyltransferase
Uncoupling protein-3
AB015724
X12752
S77528
AB011365
AF022081
X13167
Nuclear receptor binding factor-1
DNA binding protein C/EBP
C/EBP-related transcription factor
PPAR-gamma protein
Small nuclear RING finger protein
NF-1 like DNA-binding protein
Transcription factor
3.7✝
2.8✝
2.5
2.1
2
2
Hormone receptor and signal transduction
~10.7✝
4
3.6✝
3
2.9
2.9
2
2.8
2.7
2.6
2.6✝
2.5
2.4
2.4
2.4✝
2.3
2.3
2.2
2.2
2.2✝
2
2
2
2
M96159
S79241
U93880
K03045
D38036
Z83757
X92069
E12286
X17053
S74351
X06107
M64300
D85183
L13619
L35767
S49003
D85435
D89655
U21101
J03819
AF022952
S50461
L25633
M12492
Adenylyl cyclase type V
Oxytocin receptor
Insulin receptor substrate-3 (IRS-3)
Retinol-binding protein (RBP)
Truncated TSH receptor
Growth hormone receptor
P2X5 receptor (ATP-gated ion channels)
GM2 activator protein
Immediate-early serum-responsive JE
Protein tyrosine phosphatase
Insulin-like growth factor I
Signal-related kinase (ERK2)
SHPS-1 (protein tyrosine phosphatase)
Insulin-induced growth-respons protein
Very low density lipoprotein receptor,
Short isoform growth hormone receptor
Protein kinase C delta-bindig protein
Scavenger receptor class B
Cyclic GMP stimulated phosphodiesterase
Thyroid (T3) hormone receptor
Vascular endothelial growth factor B
Signal-transducing G protein alpha 12 subunit
Neuroendocrine-specific protein
Type II cAMP-dependent PK regulatory subunit
K00512
AF004811
X60351
AF041373
U50717
M83196
Myelin basic protein
Moesin
Alpha B-crystallin
Clathrin assembly protein short form
Synaptic density protein PSD-93
Microtubule-associated protein 1A
Cellular cytoskeleton
~4.9
~4.0✝
3.5
3.1
2.7
2.5
✝
-
Macronutrient Metabolism
Fold
-22.5
-22.1✝
~-20.0✝
-19.0✝
-9.1
~-6.4✝
-6.0
-3.8
-2.9
-2.8
-2.6
-2.4
-2.2
Code
Name
AF001898
AB009999
AB017260
D37920
L25387
AB010428
S68135
M18467
AF080468
S49760
X04979
M93297
L07736
Aldehyde dehydrogenase (ALDH)
CDP-diacylglycerol synthase
High-affinity carnitine transporter
Squalene epoxidase
Phosphofructokinase C
Acyl-CoA hydrolase
GLUT1
Aspartate aminotransferase
Glycogen storage disease type 1b protein
Diacylglycerol kinase
Apolipoprotein E
Ornithine aminotransferase
Carnitine palmitoyl-transferase I
Redox and stress proteins
-7.8-34.1✝
-4.8-6.2
-4.2
-2.6-3.2
S82820
X62660
M11794
X02904
Glutathione S-transferase Yc2 subunit
Glutathione S-transferase subunit 8
Metallothionein-2 and metallothionein-1
Glutathione S-transferase P subunit
Transcription factor
-25.2
-5.2
-2.7
U78102
X94246
M91802
Krox20 ó EGR-2(early growth response protein 2)
Pax-8 protein
Homeobox protein (Hox 1.11)
Hormone and signal transduction
-117.5
~-92.3✝
~-47.3
~-8.6✝
-4.1
-2.8
-2.3
-2.3
-2.3
-2.2
-2.1
-2
S49491
J04488
D63772
M12450
U57715
U48596
L06096
U53184
X59132
D64045
AF014009
M91599
Proenkephalin
Prostaglandin D synthetase
Neuronal high affinity glutamate transporter
Vitamin D binding protein
FGF receptor activating protein FRAG1
MAP kinase kinase kinase 1 (MEKK1)
Inositol trisphosphate receptor subtype 3 (IP3R-3)
Estrogen-responsive uterine
Secretin receptor
Phosphatidylinositol 3-kinase p85 alpha subunit
Acidic calcium-independent phospholipase A2
Fibroblast growth factor receptor subtype 4
Cellular cytoskeleton
~-21.4✝
~-12.2✝
~-6.6✝
~-3.8✝
-3.2
-2.3
X81448
M93638
AF013247
M59936
X67788
X81449
Keratin 18
Keratin 5
Beta-A4 crystallin
Connexin-31
Ezrin p81
Keratin 19
✝ corresponds to a transcript absent in the obese group (basal line).
✝ corresponds to a transcript absent in the control group (basal line).
14
EXPRESIÓN GÉNICA Y NUTRICIÓN
1,25
**
0,75
Control (8 d)
**
Cafetería (8 d)
0,5
0,25
0
UCP3
PPARγ2
aP2
Margareto, et al. 2001
EXPRESIÓN GÉNICA Y NUTRICIÓN
3
2
Control (30 d)
**
2,5
Relative Units
relative units
1
Cafetería (30 d)
**
1,5
1
0,5
0
UCP3
PPARγ2
aP2
Margareto, et al. 2001
15
EXPRESIÓN GÉNICA Y NUTRICIÓN
40
30
20
10
%
0
-10
-20
-30
-40
Leptin
HSL
UCP2
PGC-1
PAARγ2
Viguerie, et al. 2005
EXPRESIÓN GÉNICA Y NUTRICIÓN
40
30
20
%
10
high-fat
0
low-fat
-10
-20
-30
-40
Leptin
HSL
UCP2
PGC-1
PAARγ2
Viguerie, et al. 2005
16
17
18
GENOTIPO y NUTRICION: INTERACCIONES
Genotipo
r
Nutricion
Fenotipo
Expresión
génica
Nutricion
Status
MICROARRAY
19
POLIMORFISMOS (SSCP)
Marti, et al. 2003
IDENTIFICACION PORTAQMAN - PCR
20
NUTRIGENOMICA: Nutrición personalizada basada en genotipo.
?
Nutrigenómica es el estudio de las relaciones moleculares entre la nutrición y la
respuesta de los genes, con el objetivo de extrapolar cómo dichos cambios sutiles
pueden afectar a la salud humana.
CONSUMO DE GRASA y GENOTIPO INTERACCIONES
7,5
6,5
Cambios en IMC
5,5
4,5
3,5
2,5
1,5
0,5
-0,5
-1,5
-2,5
<2,1
2,3
2,6
2,9
3,1
3,3
3,6
3,9
4,2
>4,2
Ingesta de grasa (MJ)
Ow/Op Ow=overweight at baseline
Ow/Np Nw=normal weight at baseline
Nw/Op Op=obese parents
Nw/Np Np=normal weight parents
SORENSEN, et al. 1997
21
TIPO DE GRASA y GENOTIPO: INTERACCIONES PPAR
6
No portadores
5
Portadores (Pro12Ala)
Odds Ratio
4
3
2
1
0
1
2
3
4
5
Energía derivada de grasa (Quintiles)
Memisoglu, et al. 2003
TIPO DE GRASA y GENOTIPO: INTERACCIONES PPAR
IMC (Kg/m2)
27
26
Pro/Pro
Ala
25
24
<0,39
<0,51
<0,66
>0,66
Cuartiles de P:S ratio
Luan, et al. 2001
22
CONSUMO DE CHO y GENOTIPO: INTERACCIONES en ADRB3
35,82
36
IMC (Kg/m2)
35
31,97
34
33
33,65
32
32,35
31
Mutación
30
No mutación
<49%E de CHO
>49% E de CHO
Martínez. et al. 2003
CONSUMO DE CHO y GENOTIPO: INTERACCIONES en PPAR
40
39
IMC 38
37
36
35
34
33
32
31
30
35,3
33,4 31,5
32,4
Mut (+)
Mut (-)
<49% E
CHO
>49% E
CHO
Corbalan, et al. 2002
23
TRATAMIENTOS DE LA OBESIDAD
Dietoterapia y restricción E
Fármacos
Programas de actividad Física
Cirugía Bariátrica
Personalización de la .........
.............Nutrición por genotipo
POLYMORFISMO EN GEN APOLIPOPROTEINA A5
TT
C
30
29,1
29
BMI
(Kg/m2)
28,1
28
27
27,8
27,9
26
26,5
25
25
24
23
0
6
12
semanas
Aberle, et al. 2005
24
POLIMORFISMO EN GEN PERILIPINA y RESTRICCION E
10
8
Peso
perdido
(%)
7,7
6
4
2
0,97
AA
GG
Corella, et al. 2005
LIPC – 514>CT POLYMORFISMO Y FIBRA
(TERCILES)
1
1
OR
0,5
0,5
Segundo
Tercil
Tercer
Tercil
0.5
Primer
Tercil
Santos, et al. 2006
25
SNPS en ADIPOQ – 11377 C>G Y PERDIDA DE PESO
1,2
Peso
Perdido
(Kg)
Alto en grasa
Bajo en grasa
1
Heterocigotos
Homocigotos
Sorensen, et al. 2006
HAPLOTIPOS de UCP3 : IMPACTO EN PESO
8
5,6
6
Peso
Perdido
(Kg)
4
4,3
4,7
2
C/C
C/R
R/R
Cha, et al. 2006
26
PPAR x ADRB2: interaccion
10
8,0
8
Perdida
de peso
(Kg)
6
5,6
4
2
Pro 12 Pro
Glu 27 Glu
Pro 12 Ala
Gln 27 Glu
E. L. Rosado, et al. 2006
CAMBIOS de PESO Y GENOTIPO
100
95
Trp64Arg β3AR
And A->G UCP-1
90
85
Trp64Arg β3AR
80
No mutationes
A->G UCP-1
75
70
0 1 2 3 4 5 6 7 8 9 10 11 12
FOGELHOLM, M. et al. 1998
27
28
¿Conclusiones?
29
¿EPIOBESOGENES? Revisión?
voluntarios?
Nutricion del presente y del futuro: personalizacion
30
Medicina personalizada
Hipócrates (Cos, 460 a. C. -­‐ Tesalia 370 a. C.) “…que tu alimento sea tu medicamento y la medicación tu alimentación” Galeno (Pérgamo, Grecia, 130 -­‐ Roma, 200) “ninguna causa puede ser eficiente sin una apLtud del cuerpo”….los individuos heredan respuestas únicas a los alimentos ! suscepLbilidades únicas a enfermedades crónicas C.Bernard (S XIX.) “…no hay enfermedades , sino …….enfermos” Garrod (ppios. s XX) sugiere que dieta influye en enfermedad de manera diferente según el individuo. Williams (1956): Diversos estudios muestran amplias variaciones en los niveles de insulina, colesterol, iones…. Gené7ca estudia la herencia, los genes y su variación Secuenciación genoma humano muestra: • Humanos idén7cos 99,9 %! aprox. 3·∙106 SNPs responsables de todas las diferencias • ≈ 1000 mutaciones responsables de enfermedades y genes relacionados con enfermedades mulLfactoriales como DMT2, obesidad, ECV y cáncer. • Mutación cualquier cambio en la secuencia de nucleótidos del ADN
.
31
GENOTIPO → FENOTIPO
DNA
RNA
Transcription
PROTEINS
Translation
Gene Expression= f(DNA × Environmental factors)
• Genotipo: conjunto de genes (y alelos) de un organismo
• Fenotipo: forma en la que se expresa un carácter observable
Nutri7on And Gene7c Interac7ons Possible interac7ons of diet with gene7c variability to affect disease risk. Jenab MZ et al. Hum Genet (2009) 125: 507–525 32
• Mutación cualquier cambio en la secuencia de nucleótidos del ADN
.
• Polimorfismo mutación con alelo de frecuencia superior a 1%
ENFERMEDADES
MONOGÉNICAS
Celiaquía
Intolerancia a la lactosa
Hipercolesterolemia familiar
Fenilcetonuria
Galactosemia
• 
• 
ENFERMEDADES
POLIGÉNICAS
Obesidad
Diabetes tipo 2
Hiperlipidemias
Hipertensión
Enfermedad cardiovascular
Factores exógenos: exposición a toxinas o productos químicos, radiación, Osteoporosis
alérgenos, contaminantes, virus y bacterias…. Enfermedades
Factores endógenos y voli7vos: esLlo de vida, ejercicio ^sico, consumo de alcohol o tabaco, ingesLón calórica neurodegenerativas
y componentes de la dieta, alteraciones del sueño…. Cáncer
HAPLOTIPO + AMBIENTE " PREDISPOSICIÓN (% contribución)
PERSONALIZED NUTRITION ?
YES, PLEASE
Genetic make-up can determine
unique nutritional requirements
and responses to different
foods.and nutrients
Based on:
- The sequencing of the human genome,
- subsequent analyses of human genetic variation,
- studies that associate gene variants with disease markers
- Impact of nutrition/nutrients on gene expression
33
GENOMA HUMANO DE LA OBESIDAD
1
2
ACP1
Do1
3
4
5
GCKR
ACH
Nidd/gk1
Do2
PigQTL
10
Obq2
LEP
Mob-2
GRL
Mob-4
CHS1
145 Mb
255 Mb
214 Mb
14
15
203 Mb
194 Mb
DRD2
144 Mb
UMS
Weight 1
Mob-1
171Mb
183 Mb
D11S419
UCP2/3
Nidd/gk5
bw/gk1
APOD
Nidd/gk6
12
Niddm1
Do3
KEL
D1S202
11
BBS1
ADRB3
PC1
CPE
Obq2
9
SURTUB
Mob-1
Obq1
PRKAR2
UCP 1
Do6
ATP1A2
8
LPL
Do6
Mob-4
BBS3
HS
3B1
7
GLO1
TNFir24
APO B
LEPR
6
PPAR
POMC
APOA4
144 Mb
155 Mb
143 Mb
X
236 Mb
13
16
ESD
17
18
Mob-1
19
20
PWS
LIPC
114 Mb
Bw2
Bw3
P1
Obq1
BBS4
ADA
Do2
109 Mb 106 Mb
Y
98 Mb
92 Mb
Choroideremia
WTS
bw/gk1
ASIP
Niddm3
Mob-3
22
Do6
LDLR
BBS4
21
MC4/5R
85Mb
67 Mb
PPCD
Mob-5
72 Mb
56 Mb
28 Mb
Bw1
SGBS
BFLS
50 Mb
164 Mb
ÚLTIMOS: UCP, POMC, LEP, βAR, PPAR, AP2, TNF,...
WP 4: Recrui7ng centres for PoP Study 1. 
University College Dublin (Ireland) 2. 
Maastricht University (The Netherlands) 3. 
University of Navarra (Spain) 4. 
University of Reading (UK) 5. 
Na7onal Food and Nutri7on Ins7tute Warsaw N = 1280 (excluding 20 % dropout rate) 220 (Poland) 6. 
Harokopio University Athens (Athens) 7. 
Technische Universitaet Muenchen (Germany) 220 220 220 220 220 220 Courtesy John Mathers, UNew
34
Background
Personalised nutrition: the concept?
“Personalised nutriLon is the tailoring of dietary advice to suit an individual based on their geneLc make-­‐up.” 2000 Objectives
• To explore the scientific,
business and
consumer aspects of
personalised nutrition
• To determine whether
dietary advice based on
a person’s genes, could
deliver consumer
benefits
35
What worries the public about
personalised nutrition?
Data may be misused by authoriLes Data may be misused by insurers Data may be misused by adverLsers Data may be misused by Pers Nutr provider Data may not be stored securely Data may not be treated confidenLally Data may be accessed by hackers Personal diet may not be effecLve 0 Courtesy Barbara Stewart-Knox, UU
0,5 1 1,5 2 2,5 3 3,5 4 Indicate extent you agree or disagree 1-­‐5 (mean score) (N=9381) 36
Who do the public trust to
provide personalised nutrition?
Social Media News media Friends/Family Personal Trainers DieLcians/NutriLonists Consumer Orgs UniversiLes On-­‐Line Pers Nutr Comp's Food Manufacturers Food Retailers NHS Eu Commission Dept of Health Family doctor 0 Courtesy Barbara Stewart-Knox, UU
0,5 1 1,5 2 2,5 3 3,5 4 Indicate extent you trust or do not trust 1-5 (mean score) (N=9381)
Phenotyping devices
Abbot
Continuous
glucose
monitoring
system
The Zeo sleep
Manager (Oram)
Direct Life Activity
Monitor
Sensor tail 5 mm 37
DBS: dry blood spot analysis 30 – 100 µl per spot Courtesy Prof Hannelore Daniel TUM
ega
3 Om
Di
et
s noid
ote
Ap
oE
Car
ht
Choleste
rol O eig
e FT
ype
not
Gene
W
cos
Glu
Phe
Fat
e lat
Fo
Fruit & Veg en
ts Phys
i
Ac7v cal ity Su
pp
lem
s R HF
T
M
Courtesy John Mathers, UNew
38
TOOLS: Microchip scanning
Sample collection
Building the Chip
RNA isolation
MASSIVE PCR
PCR PURIFICATION
and PREPARATION
ROBOTIC
PRINTING
lean
~~~~~~~~
~~~~~~~~
obese
~~~
Reverse ~ ~~~ ~
transcription
Label with
fluor dyes
Competitive
hybridization
Computer data analysis
~~~~
~~~~
Detection with confocal
laser microscope
Moreno et al. 2010
APPROACHES AGAINST OBESITY
Nutritional and Diet Theraphy
Drugs Treatments
Physical Activity Programmes
Bariatric Surgery
Others: Personalized.........
....................................Nutrition
39
40
41
Dietary fatty acid distribution modifies obesity risk linked to the rs9939609
polymorphism of the fat mass and obesity-associated gene in a Spanish casecontrol study of children
Fig. 2. BMI-­‐standard deviaLon score (SDS) of children and adolescents according to SFA consumpLon (percentage of total energy, dichotomised by the median) and the presence of the fat mass and obesity associated (FTO) rs9939609 polymorphism in a dominant model. Values are means, with their standard errors represented by verLcal bars. Br J Nutr, 2012; 107 (4): 533-8
Obesity Suscep7bility Loci on Body Mass Index and Weight Loss in Spanish Adolescents ajer a Lifestyle Interven7on Supplementary Figure 2 (online): DistribuLon of the GeneLc PredisposiLon Score (GPS), trend and cumulaLve effects on BMI-­‐SDS and fat mass percentage in the adolescent populaLon A.) At baseline and B.) Aner 10 weeks of mulLdisciplinary intervenLon. Len axis: Prevalence. Right axis: A) BMI-­‐SDS or Fat mass percentage (baseline) and B) BMI-­‐SDS or Fat mass percentage variaLon (aner the intervenLon). J Pediatr, 2012 Sept 161: 466-470.e2
42
SCORE -UNAV
Gen
OBESIDAD
SNP
Genotipo
Score/SNP
FTO
rs9939609
rs17782313
MTHFR
rs1801133
0,6
15
2,5
3
0
50
4,1
MC4R
AA
Prevalencia
CC
Prevalencia
CC
Prevalencia
Score/patolog (mi nor-­‐mi nor) (ma jor-­‐mi nor) (ma jor-­‐ma jor) Major allele (Minor 1)
allele (2)
AA
Prevalencia
CC
Prevalencia
CG
Prevalencia
CC
Prevalencia
TT
Prevalencia
2
15
0
95
0,3
15
0
52
0,4
10
3,7
CC
Prevalencia
CT
Prevalencia
TT
Prevalencia
0
50
0
34
0,4
10
1,4
-4
55
-­‐5
0,6
15
2,5
3
0,2
10
0,3
60
2
47
0
40
0
25
0
50
0
50
T
A
T
C
C
T
2
15
0,3
1
0,3
5
0,3
4
0,4
10
0,6
60
0,2
4
0,2
15
0,1
44
0,2
55
0
25
0
95
0
80
0
52
0
35
T
A
C
G
C
G
C
G
C
T
0,2
10
0,2
7
0,4
10
0,1
40
0
34
0,1
55
0
50
0
59
0
35
C
T
C
T
C
T
4
10
0
35
-­‐4
55
T
C
DIABETES
FTO
rs9939609
PPARA
rs1800206
PPARG
rs1801282
MTNR1B rs10830963
GNB3
rs5443
HIPERTENSIÓN
MTHFR
rs1801133
NOS3
rs1799983
GNB3
rs5443
INTOLERANCIA A LA LACTOSA
LCT
rs4988235
TT
Prevalencia
(C;C) 4x lactose intolerant
(T;T) -4x lactose tolerant
(C;T) 0x lactose intolerant
SCORE CINFA-UNAV
INTERACCIONES GEN-DIETA
Gen
SNP
Major allele (Minor 1)
allele (INTERACCIONES
2)
APOA1
rs670
G
A
Si GG (3)
Si su dieta e s rica e n grasas o si su i ngesta de grasas monoinsaturadas, como e l aceite de oliva, e s muy APOA1
rs670
G
A
Si AA (1)
Una dieta rica e n ácidos grasos de tipo poliinsaturado e s e specialmente beneficiosa e n su caso para i n
LIPC
rs1800588
C
T
Si TT (1)
Una dieta rica e n grasas monoinsaturadas l e ayudará a reducir sus niveles de colesterol LDL y, por l o ta
MTHFR
rs1801133
C
T
Si TT (1)
Si l a i ngesta de ácido fólico no e s e levada, usted tiene más riesgo que l a mayoría de l a población de pr
NOS3
rs1799983
C
T
Si TT (1)
Los ácidos grasos de tipo n-­‐3 son particularmente beneficiosos e n su caso para mantener bajo control e
PLIN
rs894160
G
A
Si AA (1)
Una dieta rica e n grasas saturadas (de origen animal, e n e special carnes grasas y l ácteos e nteros, y tam
PPARA
rs1800206
C
G
Si GG (1)
Si su dieta e s rica e n grasas y e specialmente e n grasas saturadas (carnes grasas y l ácteos e nteros), su p
PPARG
rs1801282
C
G
Si GG (1)
Si su dieta e s pobre e n ácidos grasos monoinsaturados (en e special aceite de oliva o girasol alto oléico
Si su dieta es rica en grasas o si su ingesta de grasas monoinsaturadas,
como el aceite de oliva, es muy elevada, usted tiene mayor predisposición
que la mayoría de la población para desarrollar diabetes tipo 2, obesidad
e hipertensión.
Le recomendamos una dieta algo más baja en grasas para prevenir estas
posibles consecuencias.
43
SCORE CINFA-UNAV
SNP
polimorfismo
01
APOA5 (rs662799) g.4430 T>C
02
APOB (rs5742904) c.10580 G>A
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
APOA1 (rs670) g.4926 G>A
ESR1 (rs2234693) c.453-387 T>C
FTO (rs9939609) c.46-23525 T>A
GC (rs2282679) c.*26-796 A>C
GCKR (rs1260326) c.1337 C>T
GNB3 (rs5443) c. 825 C>T
MTNR1B (rs10830963) c.223+5596 C>G
MC4R (rs17782313) g.5785109 T>C
LPL (rs328) c.1421 C>G
LIPG (rs4939883) g.47167214 C>T
CETP (rs1800777) c.1403 G>A
LIPC (rs1800588) g.4501 C>T
NOS3 (rs1799983) c.894 G>T
PLIN4 (rs894160) c.772-799 G>A
PPARA (rs1800206) c.484 C>G
PPARG (rs1801282) c.34 C>G
CELSR2 (rs12740374) c.*919 G>T
MTHFR (rs1801133) c.665 C>T
LCT/MCM6 (rs4988235) c.1917+326 T>C
APOE (rs429358) c.388 T>C
APOE (rs7412) c.526 C>T
21*
23
24
Gen
SNP
SNP
(nomenclatura
alternativa)
GPS basado en suma de alelos de riesgo
30
20
25
P= 0.0020
15
Percentage of body fat
35
Interacción GPS * Ingesta energética
1000
2000
3000
4000
TOTAL ENERGY INTAKE
low GPS (<=7 risk alleles)
high GPS (>7 risk alleles)
Ajustado por sexo, edad y factor de actividad física
44
28
22
24
26
P= 0.0316
20
20
25
Percentage of body fat
P= 0.0027
15
Percentage of body fat
30
30
Interacción GPS * Ingesta proteica
0
50
100
150
200
0
50
100
200
high GPS (>7 risk alleles)
low GPS (<=7 risk alleles)
high GPS (>7 risk alleles)
23
21
22
P= 0.0034
20
Percentage of body fat
24
25
low GPS (<=7 risk alleles)
150
ANIMAL PROTEIN intake
PROTEIN intake
10
20
30
40
50
60
VEGETABLE PROTEIN intake
low GPS (<=7 risk alleles)
high GPS (>7 risk alleles)
Ajustado por sexo, edad, factor de actividad física e ingesta energética
25
23
24
Percentage of body fat
24
P= 0.0151
22
22
P= 0.0292
20
50
100
150
200
10
20
30
FAT intake
40
50
AGS intake
high GPS (>7 risk alleles)
low GPS (<=7 risk alleles)
high GPS (>7 risk alleles)
24
26
low GPS (<=7 risk alleles)
20
22
P= 0.0024
18
0
Percentage of body fat
Percentage of body fat
26
26
Interacción GPS * Ingesta de grasa
0
10
20
30
40
AGP intake
low GPS (<=7 risk alleles)
high GPS (>7 risk alleles)
Ajustado por sexo, edad, factor de actividad física e ingesta energética
45
25
23
24
P= 0.0304
21
22
Percentage of body fat
24
23
22
P= 0.0078
21
0
100
200
300
400
500
0
100
CARBOHYDRATES intake
200
300
COMPLEX CARBOHYDRATES intake
high GPS (>7 risk alleles)
low GPS (<=7 risk alleles)
high GPS (>7 risk alleles)
22
24
26
low GPS (<=7 risk alleles)
20
P= 0.0394
18
Percentage of body fat
Percentage of body fat
25
26
26
Interacción GPS * ingesta CHO-Fibra
10
20
30
40
50
60
FIBER intake
low GPS (<=7 risk alleles)
high GPS (>7 risk alleles)
Ajustado por sexo, edad, factor de actividad física e ingesta energética
OBESIDAD: AGENTES ETIOLÓGICOS
GENÉTICA
Nutrición
F. Endocrinos
OBESIDAD
S. Nervioso
Act. Física
AMBIENTE
46
FARMACOGENÉTICA Y NUTRIGENÉTICA
Genética del metabolismo + Genética del receptor
del fármaco
del fármaco
= Heterogeneidad de la
respuesta al fármaco
Eficacia
Pacientes y
Enfermedades
Farmacogenética
Toxicidad
Absorción
Distribución
Metabolismo
Excreción
Patogénesis
Susceptibilidad
Microchip
Prevención
Nutrigenética
Consumidores
y Salud
Tratamiento
Genética del metabolismo + Genética del receptor
del nutriente
del nutriente
= Heterogeneidad de la
respuesta al nutriente
NUTRICIÓN MOLECULAR
Nutrigenética
Polimorfismos
Genes
Nutrientes
Nutrigenómica
Expresión de genes
NUTRICION PERSONALIZADA
47
Personalized Nutrition
Rather than there being an ‘op7mal’ human diet, there are a range of adequate diets which depend upon individual biological and cultural varia7on. EPIGENETIC MARKS PHYSICAL ACTIVITY GENETIC BACKGROUND Personalized diet ALLERGIES AND INTOLERANCES FAMILY HISTORY LIKES AND DISLIKES PREVIOUS DISEASES CULTURE Conclusiones
GENOMA
Genoma:
30.000 genes (30% presentan polimorfismos)
Expresión génica = f (DNA ! Factores ambientales )
Diagnostico:
-Requerimientos de nutrientes
-Predisposición para trastornos asociados a la nutrición
-Prescripción de dietas (Profilaxis/terapia)
48
¿Conclusiones?
¿EPIOBESOGENES? Revisión?
voluntarios?
49
Nutriepigenética
Los procesos epigenéticos modulan la actividad y expresión
génica sin cambios en secuencia de nucleótidos.
Modificaciones Epigeneticas
DNA Methylation and Histone modification.
(From Molecular Development - Epigenetics by Dr Mark Hill.)
http://www.youtube.com/watch?v=eYrQ0EhVCYA&NR=1
50
Modificaciones Epigenéticas
Me
Me
Metilacion en DNA
Empaquetado de
cromatina
Me
Modificaciones
en Histonas
DNA Packaging
en Nucleosomas
Ac
Me Me
Me Me
Me Me
Me Me
Ac
Ac
Ac
Me Me
Me Me
Relaciones ente metilación de DNA y estructura de la cromatina
Lodygin D et al. Cell Research (2005) 15, 237–246.
51
Periodos vitales susceptibles de existir metilacion en DNA
Supresión de la impronta de
metilación ocurre casi
exclusivamente de dos etapas:
· células germinales primordiales
· desarrollo de blastocisto
Los cuidados maternos, el
envejecimiento, la dieta, las
toxinas y la inflamación,
regulan la metilación del ADN.
Transmisión
transgeneracional
Nutrientes y enfermedades influyen en enzimas modificadores
de histonas implicados en transcripcion génica
Metabolic
Status
Diet and
nutrients
Diseases
Metabolic
Compounds
mRNA
levels
Protein
levels
Activity
as enzymatic substrate
Histone-modifying enzymes
Repressed genes
OCCH 3
OCCH 3
Ub
OCCH 3
Activated genes
Transcription
Sum
OCCH 3
Ac
OCCH 3
Ub
Ac
P
Ub
Me-­‐C
OCCH 3
Sum
Ub
“Histone-Code”
OCCH 3
Ac
P
OCCH 3
Biotin
52
Metilacion y modificaciones en histonas implicados represion
(izda) o activation (dcha) de transcripcion genica
The Histone Code
Activated genes
Repressed genes
OCCH3
OCCH3
Sum
OCCH3
Ub
Transcription
OCCH3
Ac
OCCH3
Ub
Ac
Ub
P
Me-­‐C
Sum
OCCH3
OCCH3
Ub
Ac
Bio7n
Ub
OCCH3
Methylatedresidues
Ubiquitylated residues
Acetylatedresidues
biotinylated residues
P
Ac
P
OCCH3
Bio7n
Phosphorylatedresidues
Sum
Sumoylated residues
Me-­‐C
Methylatedcytosine
Methods in DNA methylation
Bisulfito + Secuenciacion
PCR (BSP)
MSP / MethyLight
Pirosecuenciacion
Sodium Bisulfite treatment
(converts cytosine into uracyl)
U
PCR with specific BSP primers
Methylation Specific PCR (MSP)
is a bisulfite conversion-based
PCR technique.
PCR products are cloned
and transformed in bacteria
Sequencing with accurate primers
Electroforesis
capilar
MethyLight method is based on
MSP,
but using quantitative real-time
PCR
53
Biomarcadores epigene7cos y pérdida de peso Antes 8 semanas de restricción energética
Después Illumina Microarray Milagro FI et al. FASEB J (2011)
Cambios en metilacion asociados a dieta hipocalorica
Microarray
Changes by the diet
625 CpG hipermetilacion
Antes vs. Tras restricción
(20 % variacion p < 0.05)
945 CpG hipometilacion
0,9
Inicial
0,8
(% metilación)
0,7
***
***
Final
***
***
0,6
0,5
0,4
0,3
***
0,2
0,1
0
APOA5
APOA5
PTEN
PTEN
GNAS
GNAS
H19
H19
IL26
IL26
Milagro FI et al. FASEB J (2011).
54
• B
iomarcadore epigeneticos
Biomarcadores
epigeneticosy yperdida
perdidade
depeso
peso
(n=27)
Subcutaneous
Adipose Tissue
(SAT)
Blood
8 weeks of Low
Calorie Diet
No vs. respondedores
SAT
Blood
(n=6)
(n=21)
Cordero P et al. J Physiol Biochem 2011
Cambios en metilación asociados a dieta hipocalórica
LEPTINA
TNFα
BASELINE methylation %
Ladder
70
T
60
50
unmet
unmet
met
sample 1
40
met
sample 2
*
Respuesta
30
Responders
No r espuesta
20
No Responders
10
MSP 0
LEPTIN
Leptina
TNFa
TNF-­‐alfa
Cordero P et al. J Physiol Biochem 2011
55
Nutrientes
Energia
DNA
Regulación
mRNA
Funciones estructurales
Proteinas
PROGRAMACIÓN PERINATAL Y ENF. CRÓNICAS
Genetics
Genomics
Epigenetics
56
NUTRITION PERSONALIZADA
Rather than there being an ‘optimal’ human diet, there are a range of adequate
diets which depend upon individual biological and cultural variation.
MARCAS EPIGENETICAS ACTIVIDAD FISICA HERENCIA Personalized diet ALERGIAS E INTOLERANCIAS HISTORIA FAMILIAR GUSTOS/AVERSIONES ANTECEDENTES DE ENFERMEDAD CULTURA HOMEOSTASIS: INTERACCIONES
GENETICA
Nutrición
Hormonas
Homeostasis
Act. Fisica
Sistema Nervioso
F. Ambientales
57
Conclusiones
GENOMA
Genoma:
30.000 genes (30% presentan polimorfismos)
Expresión génica = f (DNA ! Factores ambientales )
Diagnostico:
-Requerimientos de nutrientes
-Predisposición para trastornos asociados a la nutrición
-Prescripción de dietas (Profilaxis/terapia)
58
BASES GENÉTICAS Y EPIGENÉTICAS DE LA
OBESIDAD
J Alfredo Martinez
• University of Navarra.
Pamplona
59
Methods in Protein-DNA Interactions
Methyl Chip-on-Chip for Methyl-CpGs Binding Proteins (MBDs)
ChIP-on-chip is used to investigate interactions between proteins and DNA in vivo.
It combines chromatin immunoprecipitation (ChIP) with microarray technology (chip).
it allows the identification of binding sites of DNA-binding proteins on a genome-wide basis.
CpG Island Analysis in the promoters of different genes
Role in obesity
Gene symbol
adipogenesis
PPARGC1A
adipogenesis
NR0B2
adipogenesis
FGF2
adipogenesis
PPARG
adipogenesis
PTEN
adipogenesis and cell cycle
CDKN1A
adipogenesis and inflammation
LEP
adipogenesis
ESR1
adipogenesis
NR3C1
inflammation
TNF
inflammation
PLA2G4A
inflammation
SOD3
inflammation
SOCS1/3
inflammation and apoptosis
CASP8
energy metabolism
COX7A1
fat metabolism
LPL
fat metabolism
FABP4
insulin signalling
CAV1
insulin signalling
PIK3CG
insulin resistance and adiposity
HSD11B2
insulin resistance
IGFBP3
60
Some promoter methylable regions on putative obesogenes
Table 1. Differentially methylated regions in several obesogenic genes by Illumina microarray and their corespondent analysis by Sequenom
Epityper approach.
MICROARRAY
Symbol
Chrom.
AQP9
15
ATP10A
15
CD44
11
SEQUENOM
Chromosomic CpG
position
56217683
Illumina ID
PRIMERS
cg11098259
56217974
cg11577097
23577342
cg11015241
23577440
cg17260954
35117468
cg18652941
35117805
cg04125208
LEFT
aggaagagagTGAAAATTTTTT
TTGGATTAGGGTT
RIGHT TCCTCACTTTCACAACCAAATA
cagtaatacgactcactatagggagaaggctAA
LEFT
aggaagagagAGGTTGGTTTTT
TTATTTAGGTTGG
RIGHT CCCAAATTCAAATAATTCTCCT
LEFT
aggaagagagGGATATTATGGA
TAAGTTTTGGTGG
RIGHT TTTCTAAAAAACCCATTACCAA
CpGs present in
the region
7
CpG dinucleotide
equivalent to
CpG 1
22
CpG 3 and CpG4
A
CpG 7
cagtaatacgactcactatagggagaaggctCT
A
CpG 5, CpG10, CpG16
cagtaatacgactcactatagggagaaggctCC
31
C
IFNG
12
66839844
cg26227465
LEFT
MEG3
14
100362433
cg05711886
LEFT
aggaagagagTGTGTTGTATTT
TTTTTGGTTGTTG
aggaagagagATGGGTTTTGTT
TTTTTGGATATGT
CpG 2 and CpG3
CpG 26
cagtaatacgactcactatagggagaaggctAA
RIGHT AAAACTTCCTCACCAAATTATT
C
cagtaatacgactcactatagggagaaggctTA
RIGHT AACTAAAATCCCTACCACCCA
5
CpG 1
6
CpG 2
6
CpG 6
11
CpG 8
AC
NTF3
12
5473803
cg04740359
LEFT
POR
7
75421357
cg20748065
LEFT
aggaagagagTTTTTTTAGAAT
GTTTAGAGGGGAG
aggaagagagGGGGTAAGGTTT
AGTATTTAGGTGG
cagtaatacgactcactatagggagaaggctAA
RIGHT AAACCTCAACTTTAAACAAAA
TACTCT
cagtaatacgactcactatagggagaaggctTC
RIGHT TAACAAAAAAACAAAACCCAA
AA
TNFRS9
1
7922901
cg08840010
LEFT
WT1
11
32406026
cg04096767
LEFT
32406214
cg12006284
aggaagagagTATAAGAGGTTG
AATGATTTTGTTGTG
aggaagagagGGGAGATTAGTT
TTAATTTTTTTTAAG
cagtaatacgactcactatagggagaaggctAA
RIGHT AAAATACACCCTCAAACTTTA
ACAA
cagtaatacgactcactatagggagaaggctCT
RIGHT AAATCTCCCTCCATCCCAAATA
4
CpG 3
34
CpG 9 and CpG10
C
CpG 21
Main histone modifications, enzymes involved and donors
H2B
H3
KMTs
SAM
KDMs
aK
H2A
aK
meK
meK
meR
AR
uK
aK
bK
H2A
meK
Acetyl CoA
aK meK
uK
aK
ATP
Serine phosphatase
H4
HDACs
aK
meK
Serine kinase
HATs
pS
uK
pS
aK
AR
H4
aK
aK
meR
H2B
aK
biotinidase
holocarboxylase synthetase
Biotine
bK
biotinidase
H3
NAD+
aK, acetyl lysine; meR, methyl arginine; meK, methyl lysine ; uK-ubiquinated lysine; pS,
phosphorylated serine; bK, biotinylated lysine; AR, ADP-ribosylation
61
Examples of experimental changes in the level of histone methylations
(lysines, K and arginines, R) by different metabolic conditions.
effect
increase
residue and methylation affected
H3K4me1
experimental model
insulin via ROS, and high glucose, choline defficiency
cite
Kabra09
Brassachio
Davison 09
H3K4me2
Methyl defficency in mice, Hypoxia, Hexavalent Chromium, aging, and glucose
Dobosy 08
Miao 07
Jonhson 08
burke 2009
Xia 2009
Sun 2009
H3K4me3
Hexavalent Chromium, Arsenite nickel, LPS, aging, hypoxia, glucose, ischemia,
benzoapirene
Sun 2009
Zhou 2009
Ara 08
El-meyayen 2009
Xia 2009
Johnson 08
burke 2009
Zager 2009
Lyllicrop
Kabra09
Murayama 2008
Xia 2009
Davison 09 Chen 2006 Miao 07
Pogribny 06 Pogribny 09
Protein restriction, nickel ion exposure, insulin via ROS with hyperglycemia,
deprivation of glucose, gestational choline supply, hypoxia, chromium, high
glucose
H3K9me1
reduction
H3K9me3
food restriction, methyl deficient model, hexavalent chromium
H3K27me3
food restriction, hypoxia, gestational choline supply
Chen 2006
Sharif 07
Sun 2009
Sharif 07
H3K36me3
H3K79me2
H4R3me2
H3K4me1
H3K4me2
hypoxia
hypoxia
hypoxia
insulin via ROS with hyperglycemia
High glucose, cAMP glucose induced, diabetic state
Xia 2009
Johnson 08
Johnson 08
Kabra09
Miao 07
H3K4me3
food restriction, chromium, benzoapirene, methyl donors, LPS
Sharif 07
Ara 08
H3K9me1
H3K9me2
insulin via ROS
Protein restriction, choline/methionine restriction, BIX-01294, high glucose
Kabra09
Lyllicrop
Dobosy 08
H3K9me3
db/db mice, adenosinealdehide
Villeneuve 08 Heit 2009
H3K27me3
DZNEP, adenosinealdehide, sinefungin, hypoxia, hexavalent chromium, methyl
defficient diet
H3R17
H3R2me2
H4K20me3
Insulin
Hexavalent Chromium
food restriction, adenosinealdehide, methyl deficient diet, cAMP glucose induced
H3K20me3
DZNEP
miranda 09
Johnson 08
Hall 07
Sun 2009
Sharif 07
Pogribny 06
miranda 09
Johnson 08
Davison 09
burke 2009
Miao 07
Schnekenburger
El-meyayen
2007
2009
Schnekenburger 2007
Sun 2009
Schnekenburger 2007
Dobogy 08 Miao 07
Kubicek 2007
Sun 2009
Pogribny 09
Heit 2009
burke 2009
Pogribny 09
Dietary and metabolic factors involved in histone acetylation and deacetylation
DIETARY FACTORS
HDACs and SIRT
Inhibitors
METABOLISM
Inflammation
Infections (virus…)‫‏‬
Hypoxia
Oxidative Stress
Smoking
Physical activity
HISTONE ACETYLATION
aK
ACTIVATION
DRUGS
DIETARY FACTORS
aK
aK
aK
INHIBITION
Isothiocianates
Alcohol
Butyrate
NAD
Caffeinae
Resveratrol
Calóric Restriction
High-salt diets
Lipoic acid, vit. E
Biotin
Nicotinamide (niacin)‫‏‬
Anacárdic Acid
Garcinol
Curcumin
Teofilin
Cupper
Chromium
Nickel
DRUGS
HAT inhibitots
Acetyl CoA inhibitors
METABOLISM
Estrógens
Hyperglycemia
62
Inductors of the different histone modifications,
enzymes implicated and amino acidic residues affected
Phosphorylation
ADPRibosylation
Sumoylation
Ubiquitination
Deimination
Biotinylation
Proline
isomerization
Sumo-protein
ligases
Ubiquitin ligase
Peptidylargini
ne deiminases
(PAD 1-6)
Biotinidase
Holocarboxylas
e synthetase
Cyclophilins
FK506-binding
proteins
Pin1
Lysine
Arginine
Lysine
Proline
PYR-41
Inflammation
Oxidative
stress
Biotin
C
e
l
l
proliferation
Oxidative stress
Juglone
Enzymes
Serine kinases
Serine
phosphatases
polyADPribose
polymerase
mono-ADPribosyltransfer
ase
ADP-ribosyl
cyclases
Sirtuin SirT4
Amino
acids
Serine, threonine,
tyrosine
Arginine,
glutamic acid,
phosphoserine
Inducers
DNA breaks
Benzene
Bisphenol A
UVA
Oxidative stress
Hypoxia
Butyrate (inhibitor)
Oxidative
stress
Genotoxics
DNA breaks
Tryptophan
NAD+
Nicotinamide
(niacin)
1
Ginkgolic acid
Anacardic
acid
Kerriamycin B
Epigenetic modifications due to different nutritional conditions
Maternal
alterations
Caloric restriction
Increase of genomic methylation of ras DNA
Reduction of rat uterine blood flow
Alteration of the methylation status of p53 in the kidney of the
offspring
Dietary protein restriction of pregnant rats
Hypomethylation of hepatic gene expression (glucocorticoid
receptor and PPARalpha) in the offspring
Restriction of cobalamine, folate, methionine
Folate deficiency
DNA methylation in the preovulatory oocyte and the preimplantation
embryo
Impact on DNA methylation (i.e.in rat liver) and colon cancer
susceptibility
Folic acid, vitamin B12, choline, and betaine
Prevention of transgenerational amplification of body weight in A(vy)
mice
Differerent micronutrient intake
Epidemiologic data relating methylation in p16(INK4a) with low
consume of folate, vitamin A, vitamin B1, potassium and iron.
Selenium defficiency
Modification of methyl metabolism in different tissues
Arsenite defficiency
Hypomethylation of Caco-2 cells not treated with arsenite
Vegetarian diet
Vegetarian vs omnivore diet
Fatty acids
Butyrate supplementation
3-fold increase in the expression of the MnSOD gene and
decreased CpG methylation in its promoter region
Demethylation of RARbeta2 in cancer cells
Other compounds
Soy genistein
Reversal of hypermethylation of several genes and
Tea polyphenols supplementation
Inhibition of DNA methyltransferase in cancer cells
Diallyl disulfide
Increase of histone acetylation in human colon tumor cell lines
Sulforaphane
Inhibition of histone deacetylases
Royal jelly intake
Modifications in reproductive and behavioural status of honeybees
Bisphenol A
DNA hypomethylation and body weight gain in rats
Methyl donors
Microminerals
Toxins/Drugs
.
Campion J, Milagro FI and Martinez JA (2010)
63
Many genes related to obesity are regulated by DNA methylation
Individuality and
epigenetics in obesity.
Campion J, Milagro FI
and Martinez JA.
Obesity Rev 2009; 10:383-92
Epigenetic modifications
DNA Methylation and Histone modification.
(From Molecular Development - Epigenetics by Dr Mark Hill.)
http://www.youtube.com/watch?v=eYrQ0EhVCYA&NR=1
64
Epigenetic Phenomena
- DNA methylation
Addition or removal of a methyl group (CH3), predominantly where cytosine is
followed by a guanine (CpGs).
- Post-translational modifications on N-terminal tails of histones,
phosphorylation, sumoylation, ubiquitination, acetylation, and methylation
These modifications alter chromatin structure to influence gene expression.
In general, tightly folded chromatin (heterochromatin) tends to be
shut down, or not expressed, while more open Chromatin (euchromatin) is
functional, or expressed.
- Other mechanims
Gene expression maybe modified by miRNA, transposons, etc
Epigenetic modifications
Me
Me
Chromatin Packaging
Alterations
DNA Methylation
Me
Histone Modifications
DNA Packaging
around Nucleosomes
Ac
Me Me
Me Me
Me Me
Me Me
Ac
Ac
Ac
Me Me
Me Me
65
Relationship between DNA methylation and chromatin structure
The carbon atom at the 5' position of cytosines within a CpG site can be methylated by DNA
methyltransferases (DNMTs).
Methylated CpGs are recognized and bound by specific proteins including histone deacetylases (HDACs).
Histone deacetylation alters the nucleosomal structure and decreases the chromatin transcriptional activity.
Subsequent methylation of histone tails by histone methyltransferases (HMTs) enhance the formation of
transcriptionally incompetent heterochromatin.
Lodygin D et al. Cell Research (2005) 15, 237–246.
Periods of life in which DNA methylation processes take place
- Erasure of methylation imprints is almost exclusively of two stages:
•  primordial germ cells
•  blastocyst development
- Maternal care,
ageing,
dietary compounds,
toxins,
inflammation,
regulate DNA
methylation.
- Transgenerational Transmission
66
Many genes related to Nutrition are regulated by DNA methylation
Individuality and
epigenetics in obesity.
Campion J, Milagro FI
and Martinez JA.
Obesity Rev 2009; 10:383-92
TNF-a promoter methylation is tissue specific
Sullivan KE et al.
Epigenetic regulation of tumor necrosis factor alpha.
Mol Cell Biol 2007, 27: 5147-5160.
67
HYPOTHESIS
May high energy or high-fat diets affect gene promoter methylation,
affecting thus obese “phenotype”?
The methylation pattern of leptin promoter is modified by high fat dietinduced obesity in rats.
Milagro FI, Campión J, García-Díaz DF, Goyenechea E, Paternain L, Martínez JA.
J Physiol Biochem 2009; 65: 1-8.
BACKGROUND
In Vivo Methylation Patterns of the Leptin Promoter in Human and Mouse
Stöger R.
Epigenetics 1:4, 155-162, 2006
68
MATERIAL AND METHODS
77 days
Control
n=5
Cafetería
x2
n=6
Proporcion en nutrientes de las dietas
oooo
80
70
Proporcion (%)
60
50
Control
40
Cafeteria
30
20
10
0
Proteínas
Proteins
Lípidos (%)
Lipids
Hidratos de carbono (%)
Carbohydrates
J Physiol Biochem 2009; 65: 1-8
EXPERIMENTAL DESIGN
Retroperitoneal adipose tissue
Caf
C
Adipocyte Isolation
(colagenase digestion)
gDNA Extraction
Sodium Bisulfite treatment
(converts cytosine to uracyl)
U
Methylation-amplification PCR
Sequencing
69
RESULTS
B
250
200
150
100
50
0
Control Diet
400
300
200
100
0
High fat Diet
D
Leptin (ng/ml)
HOMA
4
2
Control Diet
High fat Diet
G
20
15
10
5
**
2.5
5.0
2.5
High fat Diet
Control Diet
High fat Diet
High fat Diet
*
2.0
1.5
1.0
0.5
0.0
Control Diet
Control Diet
F
7.5
0.0
**
25
0
High fat Diet
10.0
6
0
Control Diet
E
**
C
**
500
Retroperitoneal
White adipose tissue (g)
**
300
Leptin mRNA expression
(Retroperitoneal WAT)
Food Intake (Kcal/day)
350
Final Body Weight (g)
A
Control Diet
High fat Diet
Total Methylation (%)
60
50
40
30
20
10
0
RESULTS
Transcription
0
Leptin gene
CpGs
0 +2000 +8000 -536
-664
-613 -597
-549 -529 -498 -477
+10000 -443
-447 -419 -398 -395
% -443 CpG Methylation
-­‐2000 0.50
0.25
0.00
CpGs
-­‐700 -­‐600 -­‐500 *
0.75
Control Diet
High fat Diet
-­‐400 Methylated
Unmethylated
% Methylation
70
DIET MODIFIES DNA METHYLATION
A
HighFat Diet
Control Diet
r=-0.899
p=0.015
10.0
7.5
5.0
r=-0.400
p=0.505
2.5
0.0
0
25
50
75
B
Final Body Weight (g)
Leptin (ng/ml)
12.5
100
600
HighFat Diet
Control Diet
r=-0.841
p=0.036
500
400
300
r=-0.400
p=0.505
0
25
50
75
100
Total Methylation (%)
-443 CpG Methylation (%)
Association between circulating leptin and methylation of the -443 CpG (A),
and between the total methylation of the distal island and rat final body weights (B).
High-fat diet-induced obesity in rodents is able to enhance the methylation
pattern of the leptin promoter in adipocytes.
Those subjects more methylated in the HF diet, showed lower leptin levels
and lower body weight.
J Physiol Biochem 2009; 65: 1-8
Nutriepigenomics
Wonders:
1- How the dietary-induced epigenetic marks could be inherited and influence
the obesity susceptibility and metabolic alterations of the offspring ?.
2- How the diet could influence the epigenetic pattern (DNA methylation)
of gene promoters and epigenetics could be concerned with obesity ?.
3- How the epigenetic marks related with inflammation could be used as
biomarkers of disease risk or weight loss/weight gain susceptibility ?.
71
Is inflammation-related epigenetic status a good marker of weight loss?
Does epigenetically-mediated inflammatory regulation participate in body
weight control?
Could the epigenetic control of inflammation-related gene promoters be
implicated in the susceptibility to lose body weight by a hypocaloric diet?
Campión J, Milagro FI and Martinez JA.
TNF-alpha promoter methylation as a predictive biomarker for weight-loss response.
Obesity 2009; 17: 1293-7.
Epigenetic biomarkers in weight loss
hypocaloric diet/exercise
Microarray and validation
PBMCs
White cells
72
Epigenetic biomarkers in weight loss
Epigenetic biomarkers and weight loss
Epigenomic and transcriptomic responses of 14 overweight and obese
postmenopausal women to a caloric restriction intervention.
Bouchard L et al. Am J Clin Nutr. 2010; 91: 309-20
Epigenetic biomarkers and weight loss
Bouchard L et al. Am J Clin Nutr. 2010; 91: 309-20
Epigenetic biomarkers in weight loss ( Bouchard et al.)
Subcutaneous adipose tissue
73
TNF-a promoter methylation is tissue specific
Sullivan KE et al.
Epigenetic regulation of tumor necrosis factor alpha.
Mol Cell Biol 2007, 27: 5147-5160.
EXPERIMENTAL DESIGN
24 obese subjects
(BMI: 30.5 ± 1.5 kg/m2)
12 men
12 female
Serum: TNF-a
Blood, PBMC: DNA
8-week hypocaloric diet
Weigh loss ≥ 5% of initial body
Responders
Non-responders
Serum: TNF-a
74
TNF-α gene
% Methylation
Campion et al Obesity 2009
RESULTS
Weight Loss
Baseline TNF-α
Final TNF-α
Subjects less
methylated lose
more weight
Subjects with more
baseline TNF have
the promoter
more methylated
Subjects with the
promoter more
methylated decrease
less the TNF levels
Campion et al Obesity 2009
75
Epigene7c biomarkers in weight loss Before 8 weeks of hypocaloric diet
Aner Illumina Microarray Milagro FI et al. FASEB J (2011)
Methylation changes induced by an energy restricted diet
Microarray
Changes by the diet
625 CpG hypermrth
Before vs. After diet
(20 % variation p < 0.05)
945 CpG hypometh
(%
metilación)
0,9
***
***
0,8
***
***
0,7
Inicial
Final
0,6
0,5
***
0,4
0,3
0,2
0,1
0
APOA5
APOA5
PTEN
PTEN
GNAS
GNAS
H19
H19
IL26
IL26
Milagro FI et al. FASEB J (2011).
76
Methylation patterns after an energy-restriction intervention
A
B
*
C
*
D
*
*
Epigene7c biomarkers in weight loss Body Weight
Body Mass Index (BMI)
Fat Mass
Waist Girth
Systolic Blood Pressure
Diastolic Blood Pressure
Total Cholesterol
HDL-Cholesterol
LDL-Cholesterol
Triglycerides
Glucose
Insulin
HOMA
Leptin
Adiponectin
Ghrelin
PAI-1
IL-6
TNFPlasma Malondialdehyde
Units
Kg
Kg/m2
kg
cm
mm Hg
mm Hg
mg/dL
mg/dL
mg/dL
mg/dL
mg/dL
µU/mL
ng/mL
µg/mL
pg/mL
ng/mL
pg/mL
pg/mL
µM
High responders
(n=6)
Mean
SEM
96.7
±
2.7
31.0
±
0.7
26.7
±
1.3
102
±
3
127
±
5
67
±
3
196
±
21
47
±
3
127
±
19
108
±
17
92
±
5
17.9
±
5.6
4.1
±
1.4
13.2
±
2.9
8.6
±
1.6
982
±
120
172
±
16
1.3
±
0.1
0.9
±
0.2
2.1
±
0.4
Baseline
Low responders
(n=6)
Mean
SEM
93.8 ± 4.2
30.2 ± 0.6
26.6 ± 2.0
100 ± 2
131 ± 2
77 ± 6
237 ± 22
46 ± 2
168 ± 17
117 ± 20
95 ± 3
11.2 ± 1.8
2.6 ± 0.4
13.8 ± 2.1
7.5 ± 0.7
941 ± 55
139 ± 19
3.1 ± 1.0
1.5 ± 0.3
2.1 ± 0.3
p
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
Milagro FI et al. FASEB J (2011).
77
Epigene7c biomarkers in weight loss Milagro FI et al. ( FASEB J , 2011)
602 CpG hypermethylation
Low vs. High responders
before diet
(>20 % variation, p < 0.05)
432 CpG hypomethylation
(% methylation)
1
***
***
0,9
0,8
Respuesta
***
0,7
No r espuesta
***
0,6
***
0,5
0,4
0,3
0,2
0,1
0
IL10
IL10
AR
AR
GNAS
GNAS
SIRT1
CASP8
SIRT1
CASP8
Milagro FI et al. FASEB J (2011).
Methylation levels of CpG sites by two different approaches
Table 3. DNA methylation levels of different CpG sites from several obesogenic genes by Illumina microarray and Sequenom Epityper analysis. Results are
compared upon the effects of the diet (before vs. after) and upon the techniques (microarray vs. epityper).
Symbol
AQP9
AQP9
ATP10A
ATP10A
CD44
CD44
IFNG
MEG3
NTF3
POR
TNFRSF9
WT1
WT1
ID
site
cg11098259 CpG 1
cg11577097 CpG 7
cg11015241 CpG 3-4
cg17260954 CpG 10
cg18652941 CpG 2-3
cg04125208 CpG 26
cg26227465 CpG 1
cg05711886 CpG 2
cg04740359 CpG 6
cg20748065 CpG 8
cg08840010 CpG 3
cg04096767 CpG 9-10
cg12006284 CpG 21
Microarray
Mean ± SEM
45.4%
28.9%
78.1%
73.5%
8.5%
11.3%
69.0%
47.0%
53.4%
51.4%
53.8%
32.0%
23.4%
±
±
±
±
±
±
±
±
±
±
±
±
±
6.3%
3.0%
8.1%
6.0%
4.4%
6.2%
2.6%
6.6%
8.2%
6.3%
5.3%
6.6%
4.1%
Epityper
Mean ± SEM
48.4%
59.5%
73.6%
27.2%
0.7%
8.1%
0.8%
47.0%
62.6%
55.2%
4.5%
38.0%
21.7%
±
±
±
±
±
±
±
±
±
±
±
±
±
2.9%
6.1%
4.6%
0.6%
0.2%
2.3%
0.2%
0.3%
6.6%
2.2%
0.7%
1.9%
1.8%
Microarray
Epityper
Mean ± SEM
26.1%
37.6%
89.9%
76.2%
5.6%
6.2%
73.6%
46.7%
72.1%
33.5%
36.1%
30.6%
24.2%
±
±
±
±
±
±
±
±
±
±
±
±
±
3.1%
2.3%
1.6%
3.2%
0.9%
0.6%
2.7%
5.2%
1.8%
2.7%
2.2%
2.4%
2.4%
Spearman
Mean ± SEM
$
$
$
$
$
32.7%
60.1%
80.3%
26.7%
0.4%
9.1%
1.0%
45.2%
73.6%
51.4%
8.0%
38.7%
27.1%
±
±
±
±
±
±
±
±
±
±
±
±
±
4.3%
6.0%
4.4%
0.7%
0.2%
2.3%
0.4%
1.0%
6.7%
3.3%
2.3%
2.6%
2.3%
2-After
Epityper
Microarray vs Epityper
3-before
+after
Illumina
After intervention (n=10)
1-before
Basal (n=12)
*
*
#
*
*
*
*
*
#
*
*
*
*
$ p<0.05 for comparisons between dietary groups (before vs. after) by paired t-test, results from microarray
# p<0.05 for comparisons between dietary groups (before vs. after) by paired t-test, results from epityper
* p<0.05 for comparisons between techniques (Microarray vs. Epityper) by Spearman correlation test
(1) before intervention, (2) after intervention, or (3) both before and after internvention together.
Milagro FI et al. FASEB J (2011).
78
2-3 4-28 5
6-7
8
9-10 11-12 13 14
15
16
17 18 19-20-21 22 23
24-25
26 27
4-28 29
30
31
CD44
High Responders
3
2
0
10
0
43
2
19
14
3
23
32
3
8
0
2
0
8
15
18
3
Low Responders
1
2
0
9
0
14
2
16
6
0
16
24
2
7
0
2
0
11
7
10
6
*
*
Before diet
(% methylation)
CpG 9-10 r2(p)
CpG 14 r2(p)
CpG 30 r2(p)
Fat mass change (%)
0.255 (p=0.001)
0.297 (p=0.006)
0.194 (p=0.027)
BMI change
0.364 (p=0.001)
0.304 (p=0.005)
0.299 (p=0.005)
Final leptin (ng/ml)
0.142 (p=0.069)
0.179 (p=0.044)
0.050 (p=0.107)
Waist girth change (cm)
0.246 (p=0.01)
0.181 (p=0.038)
0.120 (p=0.090)
p = 0.001
1-2 3-4
5-10-16 6
p = 0.003
p = 0.005
9-19 5-10-16
14
15
5-10-16
18
19
ATP10A
High Responders
100
66
25
16
32
19
90
15
Low Responders
100
72
28
15
31
22
94
24
Before diet
(% methylation)
*
*
CpG 5-10-16 r2(p)
CpG 18 r2(p)
Fat mass change (%)
0.040 (p=0.353)
0.078 (p=0.147)
0.375 (p=0.001)
BMI change
0.016 (p=0.540)
0.172 (p=0.038)
0.434 (p<0.001)
Final leptin (ng/ml)
0.336 (p=0.003)
0.010 (p=0.635)
0.377 (p=0.001)
Waist girth change (cm)
0.119 (p=0.087)
0.036 (p=0.363)
0.382 (p=0.001)
CpG 3-4
r2(p)
p < 0.001
p = 0.001
79
Epigene7c biomarkers in weight loss (n=27)
Subcutaneous
Adipose Tissue
(SAT)
Low vs. High responders
before diet
Blood
8 weeks of Low
Calorie Diet
SAT
Blood
(n=6)
(n=21)
Cordero P et al. J Physiol Biochem 2011
Epigene7c biomarkers in weight loss LEPTIN
TNFα
BASELINE methylation %
Ladder
70
T
60
50
unmet
unmet
met
sample 1
MSP 40
met
sample 2
*
Respuesta
30
Responders
No r espuesta
20
No Responders
10
0
LEPTIN
Leptina
TNFa
TNF-­‐alfa
Cordero P et al. J Physiol Biochem 2011
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PROGRAMACIÓN PERINATAL Y ENF. CRÓNICAS
NUTRITION, PERINATAL PROGRAMING AND DISEASE
Genetics
Genomics
Epigenetics
PERSONALIZED NUTRITION Rather than there being an ‘optimal’ human diet, there are a range of adequate
diets which depend upon individual biological and cultural variation.
EPIGENETIC MARKS PHYSICAL ACTIVITY ALLERGIES AND INTOLERANCES GENETIC BACKGROUND Personalized diet FAMILY HISTORY LIKES AND DISLIKES CULTURE PREVIOUS DISEASES 81
San Sebastián/Donostia 2009
Nutrition and Health
But genetics/epigenetcs is only the tip of the iceberg
82
Personalized Medicine and Nutrition
Based on:
- The sequencing of the human genome,
- subsequent analyses of human genetic variation,
- studies that associate gene variants with disease markers
- Impact of nutrition/nutrients on gene expression
Genetic make up can determine
unique nutritional requirements
and responses to different foods.
Relationship between DNA methylation and chromatin structure
The carbon atom at the 5' position of cytosines within a CpG site can be methylated by DNA
methyltransferases (DNMTs).
Methylated CpGs are recognized and bound by specific proteins including histone deacetylases (HDACs).
Histone deacetylation alters the nucleosomal structure and decreases the chromatin transcriptional activity.
Subsequent methylation of histone tails by histone methyltransferases (HMTs) enhance the formation of
transcriptionally incompetent heterochromatin.
Lodygin D et al. Cell Research (2005) 15, 237–246.
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