Yoshida_Shimadzu_Metab2013_Analysis for Medical Research

Transcription

Metabolomics for Medical Science
Dr. Metabolo by Dr. Megumi KIBI
Masaru Yoshida M.D., Ph.D.
Division of Metabolomics Research, Gastroenterology,
The Integrated Center for Mass Spectrometry,
Kobe University Graduate School of Medicine
Today’s Contents
1. Background and Present State of Metabolomics
2. Methods to Measure Metabolites
3. Study for Biomarker Discovery
4. Study for Drug (metabolites) Discovery
Omics Studies
DNA: Genomics
(23,000)
The large-scale study of genome
Genome wide association study
Protein: Proteomics
(1,000,000)
The large-scale study of proteins
Metabolite: Metabolomics
(4,000)
The systematic study of metabolites
（possible by recent progress of mass spec.
& analysis software）
Why Metabolomics?
✓Smaller numbers compared to genome, RNA, and proteome
Human genome = about 23,000
Human functional RNA = about 100,000
Human proteome = about 1,000,000
Human metabolome = about 3,000-4,000
(enzyme related gene, less than 1,100)
✓Metabolites have been examined by traditional assays
Traditionally, metabolites have been well investigated in biochemical fields.
✓Close to phenotype
Alterations in genome and proteome do not always change the phenotype
due to homeostasis.
✓No species-specificity
Analytical methods are available to samples from different species.
Global Movement of Metabolomics
2020 visions (nature, 2010)
・Search Engines ・Ecology
・Microbiome
・Metabolomics
・Lasers
Multi-platform System for Widely Targeted Profiling
Metabolites ・・・・a great variety of physicochemical properties
Polarity
small
hydrophobic
Organic acid
Amino acid
Amine Sugar
Fatty acid
MW
Multi-platform system is
required.
hydrophilic
Sugar alcohole
large
Sugar phosphate
CoA Nucleotide
Lipid
Peptide
LC/MS
GC/MS and
Ion-paring LC/MS
代謝物カテゴリー
Derivatizat
Method Ionization
ion
Mobile phase
GC/MS
EI
Essential
Gas
LC-MS
ESI
No need
Liquid
Amino
acid
Sugar,
Sugar
alcohol
○
○
◎
×
×
×
×
◎
◎
×
△
△
×
×
◎
◎
×
Fatty
acid
Lipid
Organic Sugar phosphate,
Amine
acid
Co A, Nucleotide
△
×
○
×
Reverse
phase
◎
◎
×
Ion pair
method
×
×
PFPP
column
×
×
by Nishiumi S, Izumi Y, Matsubara A et al. Metabolomics Analysis by GC/MS
Capilary column for
metabolites separation
in the colum oven
Each metabolite is fragmented
by 70 eV thermoelectron.
Fragmentation
70eV
thermoelectron
73.2
Metabolite X
155.0
Mass (m/z)
Metabolites are efficiently separated at
their own specific boiling points.
Observed EI spectrum
Column oven temp.
100ºC~
~320ºC
Database spectrum
316.8
TIC chromatograms obtained by GC/MS of serum
Pancreatic cancer patient
Healthy volunteer
Superposition of TIC chromatograms
Some metabolites are changed in patients.
(Nishiumi S et al. Metabolomics 2010)
Metabolites Database for Identification by GC/MS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
Boric acid
Trichloroacetic acid
Phenol
Lactic acid
2-Hydroxyisobutyric acid
Caproic acid
Glycolic acid
L-Alanine
L-Glycine
Glyoxylic acid
Oxalic acid
2-Hydroxybutyric acid
2-Furoic acid
Sarcosine
3-Hydroxypropionic acid
Pyruvic acid
Valproic acid
4-Cresol
3-Hydroxyisobutyric acid
2-Hydroxyisovaleric acid
alpha-Aminobutyric acid
2-Methyl-3-hydroxybutyric acid
Malonic acid
beta-Aminoisobutyric acid
3-Hydroxyisovaleric acid
2-Keto-isovaleric acid
Methylmalonic acid
L-Valine
Ethylhydracrylic acid
Urea
2-Hydroxyisocaproic acid
3-Hydroxyvaleric acid
D,L-Norvaline
Acetoacetic acid
2-Hydroxy-3-Methylvaleric acid
Benzoic acid
Acetoacetic acid
Octanoic acid
Cyclohexanediol
2-Methyl-3-hydroxyvaleric acid
2-Propyl hydroxyglutaric acid
L-Leucine
Glycerol
Acetylglycine
Phosphoric acid
Ethylmalonic acid
2-Ketoisocaproic acid
L-Isoluecine
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
allo-Isoleucine
Phenylacetic acid
Maleic acid
L-Proline
2-Octenoic acid
Succinic acid
Methylsuccinic acid
Glyceric acid
Fumaric acid
Uracil
Citraconic acid
Propionylglycine
L-Serine
Acetylglycine
Mevalonic lactone
Isobutyrylglycine
2-propyl-3-hydroxy-pentanoic acid
L-Threonine
Mesaconic acid
Glutaric acid
Thymine
3-Methylglutaconic acid
3-Methylglutaric acid
Propionylglycine
Isobutyrylglycine
2-Deoxytetronic acid
3-Methylglutaconic acid(E)
Glutaconic acid
Succinylacetone
Decanoic acid
3-Methylglutaconic acid(Z)
2-Propyl-5-hydroxy-pentanoic acid
Citramalic acid
Mandelic acid
Isovalerylglycine
Malic acid
Adipic acid
Phenyllactic acid
p-Nitrophenol
Isovalerylglycine
2-Hexenedioic acid
Aspartic acid
L-Methionine
5-Oxoproline
Thiodiglycolic acid
4-Hydroxyproline
3-Methyladipic acid
Acetylsalicylic acid
7-Hydroxyoctanoic acid
2-Propyl-glutaric acid
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
Cinnamic acid
5-Hydroxy-2-furoic acid
Tiglylglycine
3-Methylcrotonylglycine
Tiglylglycine
3-Hydroxybenzoic acid
3-Methylcrotonylglycine
2-Hydroxyphenylacetic acid
2-Hydroxyglutaric acid
Pimelic acid
3-Hydroxy-3-methylglutaric acid
L-Glutamic acid
4-Hydroxybenzoic acid
2-Ketoglutaric acid
L-Phenylalanine
Lauric acid
Tartaric acid
Hexanoylglycine
2-Ketoglutaric acid
N-Acetylaspartic acid
Glutaconic acid
Asparagine
2-Hydroxyadipic acid
Octenedioic acid
3-Hydroxyadipic acid
Suberic acid
Lysine
2-Keto-adipic
alpha-Aminoadipic acid
Tricarballylic acid
Glutaconic acid
Aconitic acid
Orotic acid
3-Methoxy-4-hydroxybenzoic acid
Homovanillic acid
L-Glutamine
Azelaic acid
Hippuric acid
Isocitric acid
Citric acid
Glucuronoic lactone
Hippuric acid
Homogentisic acid
Myristic acid
Glucuronoic lactone
Methylcitric acid
3-(3-Hydroxyphenyl)-3-hydroxypropionic acid
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
Caffeine
Hydroxylysine (2 isomers)
Methylcitric acid
Vanilmandelic acid
Sebacic acid
Decadienedioic acid
4-Hydroxyphenyllactic acid
Theophylline
L-Histidine
3,4-Dihydroxymandelic acid
L-Tyrosine
Indole-3-acetic acid
Palmitoleic acid
Palmitic acid
2-Hydroxysebacic acid
3-Hydroxysebacic acid
2-Hydroxyhippuric acid
Dodecanedioic acid
Naproxen
N-Acetyltyrosine
Uric acid
Margaric acid
3,6-Epoxydodecanedioic acid
Indolelactic acid
Stearic acid
L-Tryptophan
3-hydroxydodecanedioic acid
Chloramphenicol
Amino acids
Other organic acids
Other organic acids(Fatty Acids)
Alcohols
Ketones
Nucleosides
Carbohydrates
Heterocyclic molecules
Inorganic compounds
Anionic Metabolites Profiling by Ion-Paring-LC/MS/MS
Central metabolism
Most of intermediates metabolites are water-soluble anionic metabolites.
Glycolysis
Pentose phosphate pathway
【Anionic metabolites】
 Sugar phosphates
Citrate cycle
 Organic acids
 Nucleotides
+ Coenzyme etc.
 Cofactors (Acetyl-CoA, NAD(P)H, etc.)
Anionic Metabolites Profiling by Ion-Paring-LC/MS/MS
【ODS C18 column + Ion-pair reagent】
Tributylamine (TBA)
Cationic ion-paring
reagent
NH+
Hydrophobicity of each
polar-anionic metabolites
is increased!
Retention and
separation
UHPLC Nexera + LCMS-8040
(Shimadzu Co.)
Ion paring
cannot be
retained on the
ODS column.
ODS C18 particle
G6P
highly polar anionic metabolite
<HPLC condition>
Column: Unison UK-C18 column, 3 m, 2.0 X 150 mm
(Imtakt Corp.)
Column Temp.: 35.0oC
Injection: 5 L
Solvent A: 10 mM TBA/15 mM acetic acid in water
B: MeOH
Flow rate: 0.3 mL・min-1
Serum Lipidomics by LC/MS/MS
Lipidomics
Large-scale lipids profiling
(one of the metabolomics)
Lipid metabolism
related enzyme
stimulate
inhibit
Cancer
onset ・ malignant
Lipids may be associated with
the each process of diseases.
Candidates of biomarkers
Target lipids
Lipids variety: Theoretical → over 30,000 species; Actual → over 1,000 species
・Free fatty acid (FFA): approximately 50 metabolites
Basic structure of lipids
O
 Simple lipids (neutral lipids: C, H, O)
・Lipids
O
・Diacylglycerol (DG) → R2, R3:acyl chains
R2
・Triacylglycerol (TG) → R1, R2, R3:acyl chains
C
O
O
C
O
R3
R1
CH
H2C
 Complex lipids (C, H, O + P, N, S, Sugars)
Glycerol
 Phospholipids
 Glycerophospholipids
H2C
O
R3:
P
O
X
OH
・lyso-Phosphatidylcholine (LPC) ・lyso-Phosphatidylethanolamine (LPE)
・Phosphatidylcholine (PC) ・Phosphatidylethanolamine (PE)
・Phosphatidic acid (PA) ・Phosphatidylglycerol (PG)
・Phosphatidylinositol (PI)・Phosphatidylserine (PS)
 Sphingophospholipids
・Sphingomyelin (SM)
 Glycolipids
 Glyceroglycolipids
・Monogalactosyldiacylglycerol (MGDG)
 Sphingoglycolipids
・Cerebroside (CB)
Glycerophospholipids metabolic pathway
NADH
ATP
NAD+
ADP
H2 O
Dihydroxyacetonephosphate (DHAP)
Glycerol
Glycerol‐3‐phosphate (G3P)
Phosphatidylglycerophosphate
Acyl‐CoA
CoA‐SH
CDP‐DAG
Pi
CMP
Phosphatidylglycerol
(PG)
Cardiolipin (CL),
Diphosphatidylglycerol
CMP
myo‐inositol
G3P
CMP
PPi
CTP
sn-1-acyl-G3P
CDP‐DAG
Acyl‐CoA
Phosphatidylinositol (PI)
ATP
CoA‐SH
ADP
Choline
ATP
ATP
ADP
ADP
Ethanolamine
ATP
ATP
ADP
ADP
O‐Phosphocholine
CTP
ADP
H2 O
PPi
ATP
Pi
O‐Phosphoethanolamine
PI4P
ATP
PI5P
ADP
ADP
PPi
PI3,4P2
PI4,5P2
ATP
PI3,5P2
ADP
CMP
CMP
Diacylglycerol (DAG)
CO2
Serine
ATP
ADP
Choline
PI3,4,5P3
Serine
Ethanolamine
Phosphatidylcholine (PC)
H2 O
Ceramide
Fatty acid
Lysophosphatidylcholine (LPC)
Phosphatidylserine (PS)
Phosphatidylethanolamine (PE)
H2 O
Fatty acid
DAG
Sphingomyelin (SM)
Lysophosphatidylethanolamine (LPE)
ATP
ADP
CTP
CDP‐ethanolamine
CDP‐choline
ATP
PI3P
Phosphatidic acid (PA)
+ Free fatty acid (FFA)
MRM settings for multi-targeted lipid profiling
Identification of lipids using various samples by exact m/z
(Mouse liver, intestine, brain, and blood plasma, and Human serum)
Condition of UHPLC Chromatography for Structural Isomers Separation
Precursor-ion scan
Neutral loss scan
Positive mode
Phosphoryl choline
Common fragment
of m/z 184.1
Choice of Precursor Ions
Fatty acid
Negative mode
Determination of Fatty Acids
Product-ion scan
Fatty acid
LC/MS/MS(triple-quqdrupole)
A total of 200 MRM transitions settings with posi・nega switching
• Free fatty acid (FFA) ・・・ 35 MRM transitions (Negative)
• Phosphatidylcholine (PC) ・・・・ 59 MRM transitions (Positive)
• Lysophosphatidylcholine (LPC) ・・・ 21 MRM transitions (Positive)
• Phosphatidylethanolamine (PE) ・・・・ 67 MRM transitions (Positive)
• Lysophosphatidylethanolamine (LPE) ・・・18 MRM transitions (Positive)
Each Cancer Mortality Rate
People / 100 thousand people
Gastric cancer
Pancreatic cancer
Breast cancer
Ovarian cancer
Leukemia
Male
Liver cancer
Lung cancer
Uterine cancer
Prostate cancer
Colorectal cancer
Female
Source: ‘‘vital statistics’’ by Ministry of Health, Labour and
Welfare (MHLW) in Japan
The number of colorectal cancer patients has been
increased with a Western-style food.
Colorectal Cancer (CRC)
Early CRC
•
Occult blood test
→ Resistance toward stool collection
→ False negative
•
Conventional tumor makers
→ Lower sensitivity at the early stage
Advanced CRC
• Imaging methods (CT etc.)
→Not applicable to very early screening
Complete remission rate:
almost 100%
• Colonoscopy
→ Invasive procedure
When CRC is first diagnosed,
40-60% are advanced.
Omics Research using Blood for Diagnosis
Number of
targets
Analysis
Health
condition
Genomics
(gene)
Proteomics
(protein)
≈ 23,000
≈ 100,000
≈ 4,000
Difficult
Easy
Laborious
Metabolomics
(metabolite)
Not reflect Difficult to reflect Easy to reflect
Serum Metabolomics by GC/MS
Training set
N
Male
Female
Age
Average
Median
Range
BMI
Stage
•
•
•
0
1
2
3
4
Colorectal
cancer patients
60
39
21
Healthy
volunteers
60
39
21
67.7
70
36-88
21.9
64.5
68
39-88
22.1
12
12
12
12
12
P value
N.S.
N.S.
(N.S., Not significant)
The cancer staging was determined base on the International Union Against Center (UICC) TNM classification
Diagnosis of colorectal cancer patients were performed at Kobe University Hospital or Hyogo Cancer Center.
Healthy volunteers were selected based upon the results of consultations at Kobe University Hospital or those
of health examination at another institutions.
Training set
A total of 131 metabolites was identified in 50 L of serum.
First Screening
Confirmation of the metabolites
•
•
•
•
not-derived from serum
stability through the analysis
intra and inter- day variations
Increased or decreased in CRC patients
27 candidates
(Nishiumi S et al. PLos One 2012)
GC/MS血清メタボロミクス
Metabolites selected by first screening
Lactitol (an artificial sweetener)
meso-Erythritol (an artificial sweetener)
Kynurenine
2-Hydroxy-butyrate
Glutamic acid
p-Hydroxybenzoic acid
Arabinose
Aspartic acid
Exclusion of metabolites from foods
Cysteine+Cystine
Cysteamine+Cystamine
Selection of Top 10 metabolites
Pyruvate+Oxalacetic acid
Isoleucine
Xylitol
Pyroglutamic acid
-Alanine
Palmitoleate(C16:1)
Second Screening
Ornithine
Stepwise selection
Inositol
Phosphate
Asparagine
Glucuronate_1
Citrulline
Glucosamine_2
Construction of Logistic Regression Model
O-Phosphoethanolamine
Creatinine
Ribulose
Nonanoic acid(C9)
Stepwise-Multivariate Logistic Regression (MLR) Model
Stepwise selection
Methods that select metabolites objectively from candidates.
Multivariate Logistic Regression (MLR) model
Multivariate linear regression model；prediction of Y using variables.
Y = aX1+ bX2 + cX3 + dX4…..+ intercept
How can we predict “diagnosis” using variables?
Set dummy; healthy = 0, diseased = 1
“Output” of the prediction model needs to be converged within 0 and 1.
P
1
1  e ( aX 1 + bX 2 + cX 3 + dX 4 .... + intercept )
Multivariate Logistic Regression (MLR) Model
Appropriate P value (cut off value) is determined by ROC analysis.
Coefficient
（a, b…）
Prediction model
1  e (Intercept + ax1 + bx 2 + cx 3 + dx 4 ................ )
286.59
33.87
1634.96
78.78
Intercept
-8.32
ROC analysis
AUC= 0.9097 (95% CI: 0.8438-0.9495)
Cut-off value=0.4945
True positive
Sensitivity
P
1
2-Hydroxy-butyrate
Aspartic acid
Kynurenine
Cystamine
Sensitivity: 85.0%
Specificity: 85.0%
Accuracy: 85.0%
Specificity
False positive
Validation
N
Male
Female
Age
Average
Median
Range
BMI
Stage
0
1
2
3
4
Colorectal
cancer patients
59
30
29
Healthy
volunteers
63
32
31
64.8
66
31-84
22.5
62.8
63
47-73
22.2
P value
N.S.
N.S.
15
11
3
11
19
(N.S., Not significant)
Comparison with Tumor Markers Training set
CEA
CA19-9
stage stage stage stage stage stage
0-4
0-2
3-4
0-4
0-2
3-4
Predictive model
stage stage stage
0-4
0-2
3-4
Sensitivity 35.0% 30.6% 37.5% 16.7% 5.6% 29.2%
100%
Specificity 96.7%
58.3%
Accuracy 65.8%
85.0% 83.3% 87.5%
85.0%
85.0%
Validation set
CEA
CA19-9
stage stage stage stage stage stage
0-4
0-2
3-4
0-4
0-2
3-4
Predictive model
stage stage stage
0-4
0-2
3-4
Sensitivity 33.9% 6.9% 60.0% 13.6%
100%
Specificity 96.8%
58.2%
Accuracy 66.4%
0%
26.7%
83.1% 82.8% 83.3%
81.0%
82.0%
Summary
• Construction of stepwise MLR model based on the results of training
set between healthy and CRC patients
• The calculated prediction model with training set had good performance
(sensitivity, 85.0%: specificity, 85.0% and accuracy, 85.0%).
• When applied to the validation set, the predictive ability was maintained
(sensitivity, 83.1%: specificity, 81.0% and specificity, 79.6%).
Metabolites selected in the prediction model
2-Hydroxy-butyrate（2-HB）
Aspartic acid（Asp）
Kynurenine（Kyn）
Cystamine（Cyst）
p=
1
1 + e-{-8.32+286.59(2-HB)+33.87(Asp)+1634.96(Kyn)+78.78(Cyst)}
Serum Metabolomics for Early Detection of Pancreatic Cancer
Metabolites for Formula
Xylitol (Xly)
1,5-Anhydro-D-glucitol（1,5AD）
Histidine（His）
Inositol（Ino）
p=
1
1 + e-{5.48+167.57(Xly)-15.21(1,5AD)-282.34(His)+60.99(Ino)}
Kobayashi et al. Cancer Epidemiol Biomarkers Prev. 2013
Development for Clinical Medicine
Pretreatment
GCMS analysis
Identification and Quantification
P
Extraction and Derivatization
blood
automation
Meaduament by Conventional Methods
Diagnosis of Multiple Diseases
Diagnosis
P
1
Diagnosis Kits for Specific Disease
1
Background for Inflammatory Bowel Disease
Inflammatory bowel disease…
is characterized by chronic and
relapsing inflammation of the
gastrointestinal tract
Aim
Inflammatory bowel disease
Genetic
Factors
Environmental
Factors
?
Immune
Abnormalities
Intestinal
Inflammation
HIbi T, et al. J Gastroenterol. 2006
Utilized metabolomics to examine the pathogenesis of IBD
DSS-induced Colitis Model
Oral administration of dextran sulphate sodium (DSS) causes similar
clinical features to human UC. (Okayasu et al., 1990; Cooper et al., 1993)
Sacrifice
0 day
5 day
3.0% DSS
C57BL/6J
Water
7 day
10 day
Water
DSS: dextran sulphate sodium
DSS (day 7) DSS (day 10)
Day 7: The degree of colitis
was severe
(x40)
Day 10: The degree of colitis
was almost improved
(x200)
Shiomi et al., Inflamm Bowel Dis. 20111
Methods in Metabolomics
Serum (Start volume: 50 l) / Tissue (20 mg)
Extraction
(CH3OH:CHCl3:H2O=2.5:1:1)
Soluble Fraction
Lyophilization
Lyophilized Product
Oximation & Derivatization
Liquid Solution
Measurement by GCMS
Metabolite Data
Gas Chromatograph Mass
Spectrometer (GC/MS)
Results
・ In serum, 77 metabolites were detected.
23 Amino acids
42 Organic acids
6 Fatty acids
6 Others
・ In colon tissue, 92 metabolites were detected.
24 Amino acids
56 Organic acids
6 Fatty acids
6 Others
Look for the decreased metabolite at day 7
Results
~PLS-DA scores plots～
Partial Least Square Discriminant Analysis (PLS-DA)
: one of Multiple Classification Analysis
3D of the first three principal components
control
DSS
(day10)
PLS-DA scores plots showed
distinct clustering and clear
separation of the groups according
to the degree of colitis.
DSS
(day7)
2D-PLS-DA scores plots
10
DSS (day7)
5 DSS (day10)
control
control
5
DSS (day10)
0
-5
-10
-10
-5
0
t[1]
0
-5
DSS (day10)
5
10
t[3]
control
t[3]
t[2]
5
DSS (day7)
-10
-5
0
t[1]
0
-5
5
10
-10
DSS (day7)
-5
0
t[2]
5
10
Results
DSS (day7)
5
DSS (day10)
control
-5
DSS (day10)
-10
-5
0
5
-10
10
-5
0
-0.10
-0.20
0.20
T48
T22
T67
T82
T45
T58
T16
T20 T73
T33
T17
T66
T5
T34
T19 T30
T31
T52 T77
T21 T40
T62
T46
T53
T35T89
T81T90
T9
T54T56 T39 T88
T2
T10
T49
T32
T12
T72
T68
T1
T4
T65 T28
T44
T41
T60
T38
T18
T6
T37
T59
T84
T42
T23T27
T43
T3
T70
T61
T47
T51
T25
T83
T87
T50
T29
T86
T26
T7T24 T80 T91
T92
T71 T55
T63
T69
T13
T78
T14
T79
T15T85
T57T36 T74
T75
T64
T76
-0.20
-0.10
-0.00
w*c[1]
0.10
T41
T71
T57
-0.00
-0.10
-0.20
-0.30
T25
-0.00
w*c[1]
0
5
10
T71 T25
T57
T64
T41
T36
T69 T50
T89
0.10
T46
T83 T65T9
T38 T77
T76T75
T35
T55
T43T32T21T17
T33 T8
T63
T26T61
T91
T84
T42
-0.00
T27
T59
T28
T60
T70
T88 T34
T74T7
T24
T86
T29 T72
T54T52
T6T56
T23
T14
T12
T13 T79
T5
T80T51
T78
T90T31
T37
T66
-0.10 T85 T92 T47
T16T45
T3 T68T53
T22
T40
T81
T4
T67T48
T1
T10
T49
T2 T73
T15
T62
T82
T18
T19
-0.20
T39 T30
T20
T87 T44
0.20
T64
-0.10
-5
t[2]
T36
T50
T38
T77
T75
T76
T35
T55
T43
T33 T26
T63 T91
T21T32
T17
T8
T84
T61
T7
T34T42
T59T70
T28 T27
T60
T88
T24
T74
T72 T86
T29
T54T56
T6 T52
T23
T79
T12
T5
T51
T80
T90
T78
T37
T66
T47
T31 T53
T16
T3T14
T22
T13
T45
T68
T85T92
T40 T67
T81
T4
T1 T49
T10 T15
T48
T2
T73
T62
T82
T18
T19
T39
T20 T30
T87
T44
T11
T58
-0.20
DSS (day7)
-10
10
T69
0.10 T46T65T89
T9T83
T8
w*c[3]
w*c[2]
-0.00
5
t[1]
T11
0.20
-5
DSS (day7)
t[1]
0.10
0
control
-5
-10
0
DSS (day10)
control
t[3]
t[3]
t[2]
5
0
5
0.10
w*c[3]
10
~PLS-DA scores plots and loadings plots～
-0.30
T11
T58
-0.20 -0.10 -0.00 0.10 0.20
w*c[2]
The dereased or increased meatbolits will be found easily.
Results
Decreased Metabolites at day 7 in colon tissue
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Succinic acid
Cont.
DSS DSS
7day 10day
L-Glutamic acid
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Cont.
1.4
1.2
1
0.8
0.6
0.4
0.2
0
DSS DSS
7day 10day
L-Glutamine
Cont.
DSS DSS
7day 10day
Indol-3-acetic acid
1.2
1
0.8
0.6
0.4
0.2
0
Cont.
DSS DSS
7day 10day
(Avg±SE, n=6)
Supplementation of Glutamine in DSS-induced Colitis
Sacrifice
C57BL/6J DSS
7 day
5 day
3.0% DSS
Gln or Water
Gln: Glutamine DSS
DSS
+ 2.0 g/dl Gln + 4.0 g/dl Gln
(x40)
Histological score
0 day
*
10
*
5
0
DSS
(x200)
Administration of glutamine could attenuate
DSS-induced colitis in mice.
DSS
DSS
+
+
2.0 g/dl 4.0 g/dl
Gln
Gln
(Mean±SE, n=5)
Supplementation of Glutamine in DSS-induced Colitis
Glutamine:
◆ The primary source of amino acids in the intestinal mucosa
◆ The main respiratory substrate for enterocytes
Serum
The glutamine level
The glutamine level
1.2
1
0.8
0.6
0.4
0.2
0
Colon tissue
1.2
1
0.8
0.6
0.4
0.2
W
W W
+
+
2G 4G
D
D D
+
+
2G 4G
0
W
W W
+
+
2G 4G
D
D D
+
+
2G 4G
D: DSS
G: glutamine
(Avg±SE, n=5)
Inflamm Bowel Dis, 2011
DSS-induced colitis animal model
• The pathogenesis of colitis led to the alterations of
some metabolites in the colon tissue.
• Supplementation of the metabolite in the body; i.e.,
glutamine, recover rapidly.
Patient Information
Ulcerative colitis (UC) patients
N (male/female)
Age (median/range)
Years with disease (median/range)
Inflammation (Proctitis/Left Side/Pan Colitis)
Rachmilewitz index (CAI) (remission/active)
Sampling location (normal/lesion)
Matt's classification (median/range)
Daily medication
5-aminosalicylates
Prednisolone
6-mercaptopurine
Azathioprine
Tacrolimus
22 (12/10)
43.9/14-85
8.4/1-30
3/7/12
16/6
16/22
3/1-5
21 (2250-4000 mg/day)
2 (5-10 mg/day)
0
0
2 (4-8 mg/day)
Tissue Metabolomics
Colon tissue of UC patient
Non-inflamed
site
colon
cecum
anus
rectum
Inflamed site
Liquid-liquid extraction
from each tissue site
GC/MS measurement
Target: Amino acids and
TCA-cycle related metabolites
GCMS-QP2010plus
Result: Comparison of Detected Metabolites
Amino acids (19)
Fold induction
(lesion/normal)
0.66
Alanine
0.58
Aspartic acid
0.94
Asparagine
0.47
Glutamic acid
0.73
Glutamine
0.25
Glycine
0.73
Isoleucine
0.67
Leucine
0.74
Lysine
0.59
Methionine
0.70
5-Oxoproline
0.89
Phenylalanine
0.70
Proline
0.59
Serine
0.67
Threonine
0.70
Tryptophan
0.75
Tyrosine
0.70
Valine
0.70
TCA related metabolites (6)
Fold induction
P value
0.0028a
<0.0001a
0.39
<0.0001a
0.044a
<0.0001a
0.0021a
0.00067a
0.0050a
0.031a
0.0016a
0.30
0.0016a
<0.0001a
0.00049a
0.0030a
0.051
0.0011a
0.0023a
Citric acid
Fumaric acid
Isocitric acid
Malic acid
Pyruvic acid
Succinic acid
(lesion/normal)
0.61
0.56
0.58
0.50
1.03
0.63
P value
0.011a
0.00031a
0.0031a
0.00060a
0.41
<0.0001a
(Red color: Significantly decreased
metabolites)
The levels of 16 amino acids and
5 TCA-clcle related metabolites
were significantly decreased in
the lesional site compared with
the normal tissue.
(Ooi et al., Inflamm Res, 2011)
Serum Metabolomics Method
Serum metabolomics
Blood collection
Liquid-liquid extraction
from blood
GC/MS measurement
Target: Amino acids and
• UC patients
• Healthy volunteers
TCA-cycle related metabolites
GCMS-QP2010plus
Patient Information
Ulcerative colitis (UC) patients
N (male/female)
13 (7/6)
Age (median/range)
39/26-57
Years with disease (median/range) 5.8/1.5-12
(All patients were followed up, and their pathology of UC showed clinical remission.)
Healthy volunteers
N (male/female)
Age (median/range)
17 (12/5)
38.9/25-67
Result: Comparison of Detected Metabolites
Amino acids (20)
Fold induction
UC/H
Alanine
0.99
Aspartic acid
1.46
0.80
Asparagine
Glutamic acid
0.73
0.51
Glutamine
Glycine
1.74
0.38
Histidine
4-Hydroxyproline
1.30
Isoleucine
1.12
Leucine
0.97
Lysine
1.14
Methionine
1.08
5-Oxoproline
1.01
Phenylalanine
0.99
Proline
0.96
Serine
1.08
Threonine
1.10
0.63
Tryptophan
Tyrosine
0.94
Valine
0.99
TCA related metabolites (6)
P value
UC vs H
0.983
0.025a
0.0032a
0.075
<0.0001a
<0.0001a
<0.0001a
0.305
0.174
0.983
0.187
0.754
0.818
0.691
0.601
0.464
0.950
0.00010a
0.161
0.884
Aconitic acid
Citric acid
Fumaric acid
Isocitric acid
Malic acid
Pyruvic acid
Succinic acid
Fold induction P value
UC/H
UC vs H
1.20
0.069
0.98
0.544
1.33
0.013a
0.96
0.490
1.18
0.117
1.03
0.851
0.99
0.722
UC, Ulcerative colitis patients
H, Healthy volunteers
(Ooi et al., Inflamm Res, 2011)
The levels of 4 metabolites
including asparagine, glutamine,
histidine, and tryptophan were
significantly decreased in both
the lesional tissue and the UC
patients serum (P < 0.05).
Summary
Human inflammatory bowel disease
•
•
The levels of many metabolites were significantly decreased in the
inflamed site.
The serum levels of some of amino acids were also significantly
downregulated in the UC patients.
✓ The potential of nutritional therapy
The potential of Personalized Medicine
Metabolic profiling
～Metabolomics～
Identification of the specific
changing metabolites in
individual patient
Personalized medicine
Therapy with in vivo targeted metabolite
• Supplementation of the insufficient
metabolites in the body
• Normalization of the metabolites which
present excessively in vivo
Improvement in
pathological
conditions
Conclusion
Metabolomics is capable of providing the greatly
useful information in the medical field.
✓The discovery of disease biomarkers
✓The finding of novel therapeutic agents
✓Examination of pathogenetic mechanisms behind various
diseases

Yoshida_Shimadzu_Metab2013_Analysis for Medical Research

Transcription

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