Metabolomics and the Molecular Phenotype of Obesity
Burant Lab
Mary Treutelaar
Jinghua Xu
Sydney Bridges
Joe Dosch
Cristina Lara-Castro
Julian Munoz
Erin Shellman
Katie Overmyer
Charles Evans
Arun Das
Jane Cao
Angela Subauste
Tanu Soni
IWMC
Amy Rothberg
Mitali Kapila
Chritine Fowler
Andrew Miller
Internal Medicine
Sub Pennathur
Jaimen Byun
Chemistry
Bob Kennedy
Matt Lorenz
Chunhai Ruan
MCRU
Theresa Han-Markey
Bionutrition Support
Metabolic Kitchen
Washington University
Sam Klein
NCIBI/CCMB
Alla Karnovsky
Maureen Sartor
H V Jagadish
Terry Weymouth
Tim Hull
Glenn Tarcea
Jing Gao
Brian Athey
Jim Cavalcoli
Funding
DK072380
DK077200
DK089503
University of Wisconsin Robert C. and
Alan Attie
Veronica Atkins
Foundation
Columbia University
Sharon Wardlaw
Endowment for the
Judith Korner
Biological Sciences
1
Clinical and Molecular Phenotyping
Biological Data
Transcriptomics
Proteomics
Metabolomics
Predictive Model
of the System
2
Systems Roles
DNA
The ultimate potential of a cell
The current direction of a cell
Material
Proteins
Metabolites
The functional capabilities of a cell
Information
RNA
The limiting currency of a cell
3
The ‘omic’s
Genome
Transcriptome
Proteome
Metabolome
~30,000 genes
~100,000 transcripts
~1,000,000 protein forms?
~2000 to 5,000 metabolites
4
What is a Metabolite?
• Any organic molecule detectable in the body with a
MW < ~2000 Da
• Includes peptides, oligonucleotides, sugars,
nucleosides, organic acids, ketones, aldehydes,
amines, amino acids, lipids, steroids, alkaloids and
drugs (xenobiotics)
• Includes human & microbial products
• Concentration > 1nM*
5
Mass Distribution of Compounds
in the Human Metabolome
50
• Metabolome
Number of cmpds per 20 daltons
45
– natively biosynthesized
– monomeric
40
35
30
• Complex metabolites
• Xenobiome
25
20
15
10
5
0
0
200
Mass
400
600
800
1000
1200
1400
1600
1800
6
Why Are Metabolites Relevant?
•
•
•
•
•
Generate metabolic “signatures”
Monitor/measure metabolite flux
Monitor enzyme/pathway kinetics
Assess/identify phenotypes
Monitor gene/environment
interactions
• Track effects from
toxins/drugs/surgery
• Monitor consequences from gene
KOs
• Identify functions of unknown
genes
Metabolites are the Canaries of the Genome
7
Why Are Metabolites Relevant?
• Generate metabolic “signatures” for disease states
or host responses
• Obtain a more “holistic” view of metabolism (and
treatment)
• Accelerate assessment & diagnosis
• More rapidly and accurately (and cheaply)
assess/identify disease phenotypes
• Monitor gene/environment interactions
• Rapidly track effects from drugs/surgery
8
2 Routes to Metabolomics
ppm
7
6
5
4
Quantitative
Methods
3
2
1
Chemometric (Pattern)
Methods
25
20
TMAO
hippurate
allantoin creatinine taurine
hippurate
urea
creatinine
15
10
citrate
5
2-oxoglutarate
water
Condition 1
succinate
fumarate
PC2 0
-5
-10
Control
Condition 2
-15
ppm
7
6
5
4
3
2
1
-20
-25
-30
-20
-10
PC1
0
10
9
The Technology of Metabolomics
15.0e6
chromatogram
12.5e6
10.0e6
7500e3
5000e3
2500e3
5
10
15
20
25
30
35
4000e3
45
50
55
60
65
2500e3
120
mass spectrum
3500e3
40
150
mass spectrum
2250e3
2000e3
3000e3
1750e3
135
2500e3
1500e3
91
2000e3
1250e3
1000e3
1500e3
77
750e 3
107
1000e3
65
500e 3
51
500e 3
51
250e 3
77
105
136
0e3
0
25
50
75
100
125
144
63
89
117
0e3
150
0
25
50
75
100
166
125
150
175
175
10
Separations Based Metabolomics
Platforms
AD
CE-MS
GC-MS
LC-MS
Small Injection
Volumes
High resolution
Soft ionization
Library ID
Full metabolome
coverage
Chemical derivitization
Limited structural info
High Resolution
DA
Low capacity
Difficult MS interface Harsh ionization
Requires charged
analytes
Lower Resolution
Limited metabolite
applicability
• ALL ESI-MS Methods Are Subject to Ion Suppression
• Response Factors of Analytes are Not Equal
Adapted From: Want, E. J.; Cravatt, B. F.; Siuzdak, G., ChemBioChem 2005, 6, 1941 – 1951
Adapted From: Villas-Boas, S. G.; Mas, S.; Akesson, M.; Smedsgaard, J.; Nielsen, J., Mass Spectrom Rev 2005, 24, (5), 613-46
11
Relative risk of health problems
associated with obesity
Greatly increased
(relative risk >>3)
Moderately increased
(relative risk 2-3)
Diabetes
Coronary heart disease
Gall bladder disease
Osteoarthritis (knees)
Hypertension
Hyperuricemia and
gout
Dyslipidemia
Insulin resistance
Slightly increased
(relative risk 1-2)
Cancer (breast cancer in
postmenopausal women,
endometrial cancer, colon
cancer)
Reproductive hormone
abnormalities
Breathlessness
Polycystic ovary
syndrome
Sleep apnea
Impaired fertility
Low back pain
Increased anesthetic risk
Fetal defects arising
from maternal obesity
12
Excess U.S. Medical Costs Related to
Abnormal Body Weight
Int J Obesity 2005;29:334-339
13
Causes of Obesity
Genetics
Behavior
Environment
14
Environmental effects…
Keith SW, et al. Int J Obes. 2006;30:1585-1594.
15
DBP: Metabolomics and Obesity
Investigational Weight Management Clinic
Nutrition Obesity Research Center Demonstration Unit
• Primary goal: Develop tools for multiscalar integration of clinical, behavioral
and molecular phenotyping data in a clinical setting.
• Insurance-supported clinical care for 400 obese patient
• Undertaking a variety of studies related to nutrition and obesity
• Michigan Nutrition Obesity Center Demonstration Unit project: Broad
phenotyping at baseline, 3 months, 24 months for 400 obese and baseline
studies in100 lean (BMI < 27).
16
Phenotypic response to diets
350
Lipid Level (mg/dl)
300
250
200
150
100
50
0
Day 0 Day Day
21
21
PUFA CHO
Total Cholesterol
Day 0 Day Day
21
21
PUFA CHO
Triglycerides
Day 0 Day Day
21
21
PUFA CHO
HDL
Day 0 Day Day
21
21
PUFA CHO
LDL
17
Lipomic assessment of plasma
Principal Component
Can macronutrient consumption
be detected in fatty acid profiles?
Analysis
(% of total lipid in fraction)
• 18:2 and 14:0 predicts PUFA at day
2, 7, 21
• 14:1 and 16:1 in TG and PL
predicts day 2, 7, 21 CHO
• 18:2 and 16:1 in CE predict day 21
CHO
18
Palmitoleate, Glucose and Insulin Sensitivity
Correlation between glucose and 16:1
levels in CE
8
7
16:1 (%)
Baseline
R² = 0.7241
6
Day 21 Pufa
R² = 0.0142
5
Day 21 CHO
R² = 0.4835
4
Palmitoleate
3
2
1
0
70
90
100
Glucose
110
120
Correlations between HOMA and 16:1
levels in CE
8
16:1 (%)
80
7
R² = 0.2855
6
R² = 0.0426
5
R² = 0.2295
Baseline
Day 21 Pufa
Day 21 CHO
4
3
2
1
0
0
0.5
1
1.5
2
2.5
HOMA
19
Phenotyping of Patients
Phenotyping: Investigational Weight Management Clinic (Rothberg)
and Analysis Laboratory for Physical Activity and Exercise Intervention Research (Gordon)
MMOC Human Phenotyping Core (Horowitz)
MMOC Molecular Phenotyping Core (Burant)
NCIBI/CCMB (Athey, Cavalcoli)
•
•
•
•
•
•
•
Anthropometric tests. Height, weight, blood pressure, heart rate, temperature, skin fold thickness,
waist-to-hip ratio, skin fold thickness. Dual Energy X-Ray Absorptiometery (DEXA, new).
Metabolic Assessment. VO2peak, resting metabolic rate (RMR) and R/Q measurement. Oral glucose
tolerance tests (for those without a diagnosis of diabetes), Total cholesterol, LDL, HDL, triglycerides,
free fatty acid, insulin (at 0 and 30 and 120 minutes of oGTT), leptin, adiponectin, C-Reactive Protein.
Peripheral Blood Metabolomic Assessment (including lipomics). The pattern of metabolite levels will
be determined, including fatty acid profiles of lipid subclasses in EDTA collected plasma
Peripheral Blood Transcriptomic Assessment. Fasting blood collected for RNA expression will be
collected in PaxGene tube
Genomic Assessment. DNA will be isolated from peripheral blood for assessment of DNA
polymorphisms related to obesity and ability to lose weight (Boehnke, not funded).
Muscle and adipose tissue biopsy metabolite and transcript analysis. Biopsies will be performed on
the vastus lateralis muscle and anterior abdominal fat.
Behavioral assessment. 4-Day Food Intake Record. A Depression inventory ( Beck Depression (BDII) 21 item questionnaire or Zung Self-Rating Questionnaire).
20
Gastric Bypass and Gastric Banding
Pre-operative Medications
Post-operative Medications
21
Weight Maintenance after Bariatric Surgery
N Engl J Med. 2004;351:26
22
Gastric Bypass and
Gastric Banding
Early Clinical
Effects
Int J Obes (Lond). 34:462-471, 2010
23
Differentiating Roux-en-Y and Gastric
Banding-30 min of MMTT
Administer 250
cc Ensure
0 15 30 60 90 120 150 180
+ 650 mg
acetaminophen
RnY/GB RnY/GB
Before Sx After Sx
Metabolite
1.176728
2.852039
14.33873
6.798669
Asparagine
Phenyl sulfate
1.31608
2.222061
5-[2-(hydroxymethyl)-5-methylphenoxy]-2,2dimethyl-Pentanoic acid (Gemfibrozil M4)
1.966002
1.079641
0.932653
0.786977
0.782593
1.323149
4.808725
2.107524
2.097368
1.984876
1.876784
1.874516
1.744332
1.620464
2-Hydroxyethinylestradiol
docusate
beta-D-Fucose
Lactate
D-Glucose
Citric acid
trihydroxyoctadecenoic acid
1.340463
1.536745
1D-Myo-inositol 1,3,4,5-tetrakisphosphate
1.703495
0.91429
1.844499
1.77825
1.656207
0.461833
1.271909
1.519994
1.479224
1.468781
1.444315
1.142015
1.083051
1.054801
2-Aminopropiophenone
hydroxy capric acid
2-Hydroxymestranol
Pro Lys Pro
Glu His
Creatine
Mono-N-depropylprobenecid
1.152962
1.479468
1.050375
1.041754
GPEtn(16:0/22:4(7Z,10Z,13Z,16Z))
Ribitol
1.221211
1.035339
1-eicosanoyl-2-(11Z,14Z-eicosadienoyl)-snglycerol
0.829258
1.020315
0.947206
0.943904
GPEtn(18:0/18:3(9Z,12Z,15Z))[U]
D-Glucose
2.105966
0.764784
6,9-hexadecadienoic acid
1.014266
0.886469
0.763771
0.75287
N-(2-phenoxy-ethyl) arachidonoyl amine
Allopregnanalone sulfate
0.872243
0.668707
0.672242
0.706845
0.660295
1.681267
1.637326
0.722102
0.67994
0.637524
0.633441
0.600171
0.596823
0.583346
GPEtn(18:1(11Z)/18:1(9Z))[U]
Dihydrodipicolinic acid
Amiloride
undecenoic acid
Glutamic Acid
Arginine
Lauric acid
0.286136
0.93842
1.992268
0.569132
0.533196
0.510135
2-Hydroxy-3-(4-methoxyethylphenoxy)propanoic acid
GPIns(18:1(9Z)/18:1(9Z))
GPCho(O-12:0/O-12:0[U])
2.034236
0.487977
1-(9Z-hexadecenoyl)-2-(9Z,12Zheptadecadienoyl)-sn-glycerol
0.602441
0.428638
Methylprednisolone succinate
0.191259
0.410316
2,4-Dihydroxybutyric acid
24
Potential effects of increased dietary protein to
enhance weight loss
Amino Acid Effects
• Postprandial meal-induced
visceral signals
• Release of PYY and other
enteric hormones
• Vagal nerve stimulation
• Direct action of amino acids in
the brain
Tome et al. Am J Clin Nutr 2009;90(suppl):838S–43
25
Mixed Meal Tolerance Test:
Pre and Post Weight Loss
300
Leucine (mM)
250
200
150
100
50
0
Lean
Obese
Pre VLCD
Obese
Post VLCD
Each represents the time course of the indicated
metabolite/hormone following administration of 250 ml of Ensure
as a mixed meal tolerance test (0,30,60,90,150 minutes)
Obese
PreSx
Obese
PostSx
26
Mixed Meal Tolerance Test:
Pre and Post Weight Loss
27
Mixed Meal Tolerance Test: Amino
Acid Dynamics
28
Change in Amino Acid dynamics following
Roux-en-Y gastric bypass
Peak
Insulin
Tryptophan 2
Tyrosine
1.5
Histidine
Lysine
Ornithine
Glucose
Alanine
Glycine
1
Valine
0.5
Leucine
0
Glutamine
Isoleucine
Phenylalanine
Threonine
Glutamic acid
Serine
4-Hydroxyproline
Proline
Methionine
Asparagine
Aspartic Acid
AUC
Insulin
Tryptophan 2
Tyrosine
1.5
Histidine
Lysine
Ornithine
Glucose
Alanine
Glycine
1
Valine
0.5
Leucine
0
Glutamine
Isoleucine
Phenylalanine
Glutamic acid
Threonine
Serine
4-Hydroxyproline
Proline
Methionine
Asparagine
Aspartic Acid
29
Cerebral Spinal Fluid Amino Acids
MEAL SNACK
Bateman et al. Nat. Med. 12:856-861, 2006
Clock Time
30
Can the CSF protect its amino acid levels?
•
Assess Plasma and CSF Amino Acid and
Lipid Profiles at baseline and following 10%
weight loss.
Defined Diet for 72 hrs. prior to sampling.
Amino Acid
Fats
2.5
Ratio CSF D /Plasma D
•
2.0
1.5
Baseline
BMI
26.6
30.8
42.7
57.3
1.0
0.5
0.0
31
Obese individuals have elevated plasma levels of amino
acids (and other nutrients)
Hypothesis
1.The brain requires a certain amount of nutrients to feel ‘sated’.
2. Amino acids and/or lipids may provide part of the signal.
3. People eat until the nutrient level in the brain is adequate.
4. Overweight and obese individuals need to eat more to get to the ‘ok’
level in the brain…thus have higher nutrient levels in the blood.
5. Weight loss decreases brain levels of nutrients, increasing appetite
Amino
Acid
Amino Acid
Fats
Fats
32
Summary
Metabolomic measurements can provide clues to the
dynamic relationship between genes and environment
in people
The metabolome is complex and changes appear
coordinated
Statistical and visualization methods can provide
otherwise hidden relationships between phenotypic
characteristics
33