Application of Nutrigenomics Toward Personalized …...Protein Function e.g., enzyme activity...
Transcript of Application of Nutrigenomics Toward Personalized …...Protein Function e.g., enzyme activity...
Application of Nutrigenomics Toward
Personalized Dietary Recommendations
Johanna Lampe, PhD, RD
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center, Seattle, WA
North American Menopause Society Annual Meeting
October12, 2013, Dallas, TX
How can genetic variation influence response to diet?
How can “omic” approaches be incorporated into the study of gene-diet interactions?
How do we move forward to personalized dietary recommendations, now and in the future?
Questions
Nutrigenomics: Study of the interactions between genes and diet.
Study of how genetic variation influences response to diet.
Health &
Disease Risk EPIGENOME
MICROBIOME
Gut bacterial
genome
GENOME
DIET
DNA mRNA Protein
Substrate Product
Down-stream
Effects
Protein Function
e.g., enzyme activity
Transcription Translation
Susceptibility
(e.g., genetic variation)
Nutrigenomic Approaches: Analysis of Gene-Diet Interactions
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Transcriptomics
Proteomics
Metabolomics
Epi/genomics
The human diet is complex.
1000s of compounds
Variety of methods of food preparation Structure and particle size
Bioavailability to host
Food preference
Food tolerance
Absorption
Transport
Metabolism
Effect in target tissue
How can genetic variation
influence response to diet?
Lampe and Potter, in Gene-Environment Interactions (Costa and Eaton, eds), 2006
Metabolism: NAT2 Polymorphism Modifies Dietary-
Induced DNA Damage in Colorectal Mucosa
2-day vegetarian diet vs 2-day grilled meat diet
Measured DNA strand breaks in epithelial cells extracted from stool
samples
Kiss et al, Eur J Cancer Prev, 9:429, 2000
NAT2 Genotype
0
20
40
60
80
NAT2 rapid NAT2 slow
Ta
il m
om
en
t
Vegetarian
baseline
High-meat *
Increased Urinary Mutagenicity with Fried Red
Meat Intake in Individuals with UGT1A1*28
• 2-week controlled
feeding study in 60
subjects
• Fed red meat cooked at
250°C
• Mutagenicity of urine
tested using Salmonella
YG1024 (+S9)
Peters et al, Environ. Mol. Mutagenesis, 2004
P for interaction between UGT1A1 and meat intake = 0.04
Increase in urinary mutagenicity per 10 g meat
intake/day
UGT1A1
genotype
Point
estimate
95% CI P-value
6/6 747 -1166-2660 0.450
6/7 or 7/7 4062 1623-6511 0.003
Unhydrolyzed Urinary Mutagenicity and Cooked Meat:
Stratified by UGT1A1 Genotype
Metabolism of Chemopreventive Phytochemicals:
Isothiocyanates Conjugated by Glutathione
S-Transferases and Excreted in Urine
GST
Dithiocarbamates, excreted in urine
ITC
+
GSH
ITC-
glutathione
ITC-
cysteine-
glycine
ITC-
cysteine
ITC-
N-acetylcysteine
g-GT CG AT
GSTM1-null subjects had:
Greater rate of urinary excretion of sulforaphane in first 6 h
Higher % sulforaphane excretion over 24 h
Impact of GSTM1 Polymorphism
on Sulforaphane Pharmacokinetics
0
20
40
60
80
100
120
0 4 8 12 16 20 24
Time (h)
Uri
na
ry e
xc
reti
on
(%
)
GSTM1-null
GSTM1+
Gasper et al, Am J Clin Nutr, 2005
Integrating Genomics and Metabolomics:
A Cross-Sectional Study
284 men
GWA study data with serum
metabolomics-based quantitation of 363
metabolites.
Reported associations of frequent SNP
with differences in the metabolic
homoeostasis, explaining up to 12% of
observed variance.
Gieger C et al, PLoS Genet , 2008.
Metabolomics
Epi/genomics
Genotypes and Metabotypes:
Endogenous Metabolite-SNP Interactions
Gieger C et al, PLoS Genet , 2008.
• Associations of frequent SNP with
differences in the metabolic
homoeostasis explained up to 12%
of observed variance.
• Using ratios of certain metabolite
concentrations as proxy for
enzymatic activity, explained up to
28% of the variance (P-values 10-
16–10-21).
• Identified 4 variants in genes
coding for lipid metabolism
enzymes (FADS1, LIPC, SCAD
and MCAD), where corresponding
metabolic phenotype matched
pathways in which these enzymes
are active.
Genetically Determined Metabotypes:
A GWA study of metabolic traits in human urine
• Designed to investigate the
detoxification capacity of human body.
• Tested for associations between 59
metabolites in urine from 862 male
participants in the SHIP study and
replicated the results in independent
samples.
• Reported 5 loci with joint P values of
association from 3.2 × 10−19 to 2.1 ×
10−182. Variants at 3 loci previously
linked with important clinical outcomes:
SLC7A9 is a risk locus for chronic
kidney disease, NAT2 for coronary
artery disease and genotype-
dependent response to drug toxicity,
and SLC6A20 for iminoglycinuria.
Moreover, we identify rs37369 in
AGXT2 as the genetic basis of hyper-
-aminoisobutyric aciduria.
Suhre et al, Nat Genet 43: 565–69, 2011.
Gut microbial variation:
Metabolism of soy isoflavone daidzein
O
O
HO
OH
OHO
OH
O
O
HO
OH
OH
O
HO
OH
OHO
OHOH
Daidzein Dihydrodaidzein
EquolO-Desmethylangolensin
Cis/Trans-isoflavan-4-ol
1
100
10000
equol
30-50% of
individuals
produce
Urinary Equol Excretion
nmol/d
INTERVENTION: Lymphocyte Gene Expression Differentially
Induced in Equol-Producing and Nonproducing Women
30 postmenopausal women
~900 mg isoflavones for 84 d
Gene expression array of peripheral lymphocytes
27 genes changed with isoflavones
Stronger effect on estrogen-responsive genes in equol producers than nonproducers.
Niculescu et al, J Nutr Biochem 18:380, 2007.
Gut Microbiome Phenotypes: Enterotypes
Arumugam et al, Nature, 2011
View to the Future
Focusing efforts in areas where evidence is
suggestive, but inconclusive, most likely to
result in findings that can advance scientific
knowledge or change public health practice
Understand the mechanisms
Reconcile the heterogeneity
Application of genomic and systems approaches to
understand:
Biologic pathways and mechanisms
Complexity of effects of dietary patterns
Behavior
Ultimate Goal of Metabotyping
• Develop a novel approach to personalized health care
based on a combination of genotyping and metabolic
characterization.
• Identify genetically determined metabotypes that can
subscribe the risk for a certain medical phenotype, the
response to a given drug treatment, or the reaction to
a nutritional intervention or environmental challenge.
• Characterize microbial modification of effects of diet.
Gieger et al, PLoS Genet , 2008
Lampe et al, Proc Nutr Soc, 2013
What do we need to get there?
Sufficiently sensitive technologies to detect small, but physiologically relevant, differences or changes in response to diet.
Data analysis, visualization, and annotation methods.
Ability to integrate the various omics platforms in a systematic fashion.
Characterization of phenotypes.
Contig
57903R
C
BR
D3
AI2
05537R
C
Contig
38824R
C
FLJ1
0254
TN
NT
1
CY
P1B
1
NR
G1
HC
A112
KIA
A1024
FLJ2
0701
IL1B
DN
AJC
7
Contig
35145R
C
Contig
40530
C1Q
B
LR
8
EP
B41L3
US
P6
CC
R2
PF
KF
B3
Contig
38043R
C
CS
PG
2
NR
G1
NR
G1
HM
L2
Contig
65401R
C
AO
C2
Contig
23408R
C
FLJ2
0202
TP
ST
1
IDS
LO
C51107
RP
GR
IP1
NE
SG
1
PB
X2
Pa
rtic
ipa
nts
5 10 15 20 25 30 35
60
50
40
30
20
10
Pa
rtic
ipa
nts
5 10 15 20 25 30 35
60
50
40
30
20
10
a.
16
10
5 10 15 20 25 30 35
16
10
5 10 15 20 25 30 35
c.
b.
5 10 15 20 25 30 35
20
10
5 10 15 20 25 30 35
20
10
Transcriptomics
Proteomics
Metabolomics
SNP arrays
Is there potential for personalized, or more
precise, individual dietary recommendations?
Yes, but still have to: Establish the relevant parameters
Integrate the omics data
Don’t lose sight of the broader public health
messages that can have the greatest impact
on the largest number of people