スライド 1 - University of Minnesota
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Transcript スライド 1 - University of Minnesota
Bioinformatics in Metabolomics
Shigehiko Kanaya
NAra Institute of Science and Technology
Graduate School of Information Science; Comparative Genomics Lab.
[1] Metabolomics approach for determining growth-specific
metabolites based on FT-ICR-MS
[2] Bio-Database developed by our lab.
2.1 Species-metabolite relation database (KNApSAcK)
2.2 Easy Gene Classifier to Functional Group
1
Data Processing from data acqisition of a time series
experiment to description of cellular conditions
10
T8
T6 T7
T5
(c) Classification of ions into
metabolite-derivative group
T3
T2
T1
1
0.1
0
DrDMASS
200
400
600
800
Time (min)
Time point
(b) Data preprocessing and
constructing data matrix
OD600
T4
(a) Time series experiments
x11
x21
.....
x s1
x12 ..... x1 j ..... x1k ..... x1M
..... ..... x2 j ... x2 k ..... x2 M
..... ..... ..... ... ..... ..... .....
xs 2 ..... ..... ... ..... ..... xsM
m/z
M
M/2
Metabolite-derivative group
M+1 (Isotope ions and multivalent ions)
DPClus
(d) Annotation of ions as
metabolites
Detected Theoretical
m/z
m/z
(e) Assessment of cellular condition
by metabolite composition
Molecular
formula
Exact mass Error
Candidate
Species
72.9878
73.9951
C2H2O3
74.0004
0.0053 Glyoxylic acid Escherichia coli
143.1080
144.1153
C8H16O2
144.1150
0.0003 Octanoic acid Escherichia coli
662.1037
663.1109
C21H27N7O14P2
663.1091
0.0018
NAD
664.1095
665.1168
C21H29N7O14P2
665.1248
0.0080
NADH
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
.....
Escherichia coli
KNApSAcK
DB
.....
Escherichia coli
2
FT-ICR/MS
(Fourier transform ion cyclotron resonance MS)
Metabolomics research has been performed by
GC-MS, LC-MS, CE-MS, NMR.
FT-ICR/MS can
offer extremely high levels of resolution and sensitivity.
High accurate mass
Assign to molecular formula
i.e.) Experimetal m/z = 662.1037
NAD+ theoretical m/z = 662.1019 (Δ= 0.0018, 2.7ppm)
3
Predicted number of molecular
formula by high accurate mass
600
597
# of candidates
C10H10O6
MW:226.0477380528
251
Chorismic acid
Isochorismic acid
32
3
0
±1
±0.1
±0.01
±0.001
Error
4
Data set (E.coli time-series)
10
Conditions
1
E.coli W3110
T4
OD600
T8
T6 T7
8 time points
T1
LB medium 2000ml
Agitation 300rpm
T3
T2
T5
0.1
Air 2l/h
Temperature 37℃
Start pH 7.4
･Collect cells by membrane filter
･Extract metabolites by methanol
0.01
0.001
0
200
400
Time (min)
600
800
5
Bioinformatics
DrDMASS+
KNApSAcK
(i)
(ii)
(iii)
(i) Peak Correction
(ii) Multivariate data processing
(iv)
(iii) Unsupervised learning
PCA, BL-SOM
KNApSAcK search
# of metabolites
20752
# of species-metabolite
pairs
41206
(iv) Supervised learning
PLS
Sample A1
x11
Sample A2 x21
.....
Sample A3
xs1
.....
Sample B1
xt1
Sample B2 .....
Sample B3 xN 1
Sample A1
Sample A2
Sample A3
Sample B1
Sample B2
Sample B3
x12 ..... x1 j ..... x1k ..... x1M
..... ..... x2 j ... x2 k ..... x2 M
..... ..... ..... ... ..... ..... .....
xs 2 ..... ..... ... ..... ..... xsM
..... ..... ........ ..... ..... .....
xt 2 ..... xtj ... ..... ..... xtM
..... ..... ........ ..... ..... .....
xN 2 ..... xNj ... xNk ..... xNM
m/z
6
(i) Peak correction
(ii) Multivariate data processing
DrDMASS+
KNApSAcK
(i)
(ii)
(iii)
(i) Peak Correction
(ii) Multivariate data processing
(iv)
(iii) Unsupervised learning
PCA, BL-SOM
KNApSAcK search
# of metabolites
20752
# of species-metabolite
pairs
41206
(iv) Supervised learning
PLS
Sample A1
x11
Sample A2 x21
.....
Sample A3
xs1
.....
Sample B1
xt1
Sample B2 .....
Sample B3 xN 1
Sample A1
Sample A2
Sample A3
Sample B1
Sample B2
Sample B3
x12 ..... x1 j ..... x1k ..... x1M
..... ..... x2 j ... x2 k ..... x2 M
..... ..... ..... ... ..... ..... .....
xs 2 ..... ..... ... ..... ..... xsM
..... ..... ........ ..... ..... .....
xt 2 ..... xtj ... ..... ..... xtM
..... ..... ........ ..... ..... .....
xN 2 ..... xNj ... xNk ..... xNM
m/z
7
Validation of data processing
(i) Peak correction
(ii) Multivariate data processing
(b) Data preprocessing and
constructing data matrix
Metabolite
IMC peak
peak
3.5×10-6
Coefficient of variation (CV)
Before correcting
2.5×10-6
After correcting
Scan 3
Scan 2
2.0×10-6
Scan 1
1.5×10-6
(i) m/z correction among each scan
(ii) Peak matching among 10scans
(multivariate data processing)
1.0×10-6
0.5×10-7
Scan 3
0
Peak1
Peak2
Peak3
IMC
Peak4
Scan 2
Scan 1
Oikawa et al, (2006)
8
9
M-12
5
M-8
M-11
4
3
M-9
M-10
6
M-5
M-14
M-4
9
M-7
2-3
8
10
M-15
7
M-6
M-13
2-2
M-16
11
M-17
PG9
PG3
PG10
1-3
M-3
M-2
PG4
1-4,5
1-1
M-1
PG7
PG6
PG1
2-1
PG2
PG8
PG5
1-6
1-2
(c) Classification of ions into
metabolite-derivative group (DPClus)
10
(d) Annotation of ions as metabolites
using KNApSAcK DB
Detected
m/za
Theoretical
m/z
Molecular
formula
Exact mass
Error
Candidate
Species
72.9878
73.9951
C2H2O3
74.0004
0.0053 Glyoxylic acid
Escherichia coli
143.1080
144.1153
C8H16O2
144.1150
0.0003 Octanoic acid
Escherichia coli
253.2137
254.2210
C16H30O2
254.2246
0.0036 omega-Cycloheptanenonanoic acid
Alicyclobacillus acidocaldarius
253.2185
254.2258
C16H30O2
254.2246
0.0012 omega-Cycloheptanenonanoic acid
Alicyclobacillus acidocaldarius
281.2444
282.2516
C18H34O2
282.2559
0.0042 Oleic acid
Escherichia coli
C18H34O2
282.2559
0.0042 cis-11-Octadecanoic acid
Lactobacillus plantarum
C18H34O2
282.2559
0.0042 omega-Cycloheptylundecanoic acid
Alicyclobacillus acidocaldarius
297.2410
298.2482
C18H34O3
298.2508
0.0026 alpha-Cycloheptaneundecanoic acid
Alicyclobacillus acidocaldarius
297.2467
298.2540
C18H34O3
298.2508
0.0032 alpha-Cycloheptaneundecanoic acid
Alicyclobacillus acidocaldarius
297.2516
298.2589
C18H34O3
298.2508
0.0081 alpha-Cycloheptaneundecanoic acid
Alicyclobacillus acidocaldarius
321.0506
322.0579
C10H15N2O8P
322.0566
0.0013 dTMP
Escherichia coli K12
346.0570
347.0643
C10H14N5O7P
347.0631
0.0012 AMP
Escherichia coli
C10H14N5O7P
347.0631
0.0012 3'-AMP
Escherichia coli
C10H14N5O7P
347.0631
0.0012 dGMP
Escherichia coli
401.0168
402.0241
C10H16N2O11P2
402.0229
0.0012 dTDP
Escherichia coli
402.9962
404.0035
C9H14N2O12P2
404.0022
0.0013 UDP
Escherichia coli
426.0237
427.0310
C10H15N5O10P2
427.0294
0.0016 Adenosine 3',5'-bisphosphate
Escherichia coli
C10H15N5O10P2
427.0294
0.0016 ADP
Escherichia coli
C10H15N5O10P2
427.0294
0.0016 dGDP
Escherichia coli
C20H19Cl2NO7
455.0539
0.0075 Antibiotic MI 178-34F18A2
Actinomadura spiralis MI178-34F18
C20H19Cl2NO7
455.0539
0.0075 Antibiotic MI 178-34F18C2
Actinomadura spiralis MI178-34F18
454.0391
455.0464
458.1112
459.1185
C15H22N7O8P
459.1267
0.0083 Phosmidosine B
Streptomyces sp. strain RK-16
495.1039
496.1112
C24H20N2O10
496.1118
0.0006 Kinamycin A
Streptomyces murayamaensis sp. nov.
C24H20N2O10
496.1118
0.0006 Kinamycin C
Streptomyces murayamaensis sp. nov.
505.9908
506.9981
C10H16N5O13P3
506.9957
0.0023 ATP,dGTP
Escherichia coli
547.0756
548.0829
C16H26N2O15P2
548.0808
0.0020 dTDP-L-rhamnose
Escherichia coli
565.0503
566.0576
C15H24N2O17P2
566.0550
0.0025 UDP-D-glucose
Escherichia coli
C15H24N2O17P2
566.0550
0.0025 UDP-D-galactose
Escherichia coli
C17H27N3O17P2
607.0816
0.0032 UDP-N-acetyl-D-mannosamine
Escherichia coli
606.0775
607.0848
11
(iii) Unsupervised learning (PCA)
DrDMASS+
KNApSAcK
(i)
(ii)
(iii)
(i) Peak Correction
(ii) Multivariate data processing
(iv)
(iii) Unsupervised learning
PCA, BL-SOM
KNApSAcK search
# of metabolites
20752
# of species-metabolite
pairs
41206
(iv) Supervised learning
PLS
Sample A1
x11
Sample A2 x21
.....
Sample A3
xs1
.....
Sample B1
xt1
Sample B2 .....
Sample B3 xN 1
Sample A1
Sample A2
Sample A3
Sample B1
Sample B2
Sample B3
x12 ..... x1 j ..... x1k ..... x1M
..... ..... x2 j ... x2 k ..... x2 M
..... ..... ..... ... ..... ..... .....
xs 2 ..... ..... ... ..... ..... xsM
..... ..... ........ ..... ..... .....
xt 2 ..... xtj ... ..... ..... xtM
..... ..... ........ ..... ..... .....
xN 2 ..... xNj ... xNk ..... xNM
m/z
12
PCA analysis
(iii) Unsupervised learning (PCA)
10
T3
T2
1
T4
T6 T7
T5
T8
220 dims. → 2 dims.
OD600
T1
Metabolic profiling could
distinguish between
exponential and stationary
phases.
0.1
0.01
(220 independent ions)
0
200
600
400
800
Time (min)
2.0
T7
PC2 (2.4%)
T2
T3
0.0
T1
T8
T4
T5
T6
Exponential-phase
-4.0
-8.0
The first two principal components,
which can explain 96.7% of total
variance, are enough to examine the
differences in 8 time points.
（SUM=1）
Stationary-phase
0.0
PC1 (94.3%)
12.0
Which metabolite is representative at each stage?
13
(iv) Supervised learning
DrDMASS+
KNApSAcK
(i)
(ii)
(iii)
(i) Peak Correction
(ii) Multivariate data processing
(iv)
(iii) Unsupervised learning
PCA, BL-SOM
KNApSAcK search
# of metabolites
20752
# of species-metabolite
pairs
41206
(iv) Supervised learning
PLS
Sample A1
x11
Sample A2 x21
.....
Sample A3
xs1
.....
Sample B1
xt1
Sample B2 .....
Sample B3 xN 1
Sample A1
Sample A2
Sample A3
Sample B1
Sample B2
Sample B3
x12 ..... x1 j ..... x1k ..... x1M
..... ..... x2 j ... x2 k ..... x2 M
..... ..... ..... ... ..... ..... .....
xs 2 ..... ..... ... ..... ..... xsM
..... ..... ........ ..... ..... .....
xt 2 ..... xtj ... ..... ..... xtM
..... ..... ........ ..... ..... .....
xN 2 ..... xNj ... xNk ..... xNM
m/z
14
PLS(Partial Least Squares)
(iv) Supervised learning
PLS
- Is supervised regression method.
- Can extract important combinations of variables.
- Can work with many responses.
factors/predictors
responses
K=1
N=8
X
PLS
Observations
Y
M=220
N=8
We tried to estimate the cell condition based on a function of
the composition of metabolites.
OD600 = a1 x1 +…+ aj xj +….+ aM xM
xj, the quantity for jth
15
-------- Time ------
Partial Least Square Modeling
y1
y
2
...
y2
...
y N
x11
x
21
...
xi1
...
x N 1
OD600
----- Metabolite quantity data -----
x12
... x1 j
x22
...
... x2 j
... ...
xi 2
...
xij
... ... ...
x N 2 ... x Nj
... x1M
... x2 M
... ...
... xiM
... ...
... x NM
y = a0 x0 ＋a1 x1 +…+ aj xj +….+ aM xM
G (w, )
wk
wk
M N
M 2
wk yi xik w1 1 0
k 1 i 1
j 1
Optimization of wk for correration between xｋ and y
N
wk
y x
i 1
i
ik
N
y i xik
k 1 i 1
M
2
16
Partial Least Square Modeling
N
wk
y x
i 1
i
ik
N
y
x
i ik
k 1 i 1
M
t 1 Xw
2
Minimization of
square of error.
Minimization of
square of error.
y = a0 x0 ＋a1 x1 +…+ aj xj +….+ aM xM
a W(Pt W)1 q
17
Advantages of PLS
y = a0 x0 ＋a1 x1 +…+ aj xj +….+ aM xM
PLS
MRA(重回帰）
G (w, )
wk
wk
G (a, )
ak
M N
M 2
wk yi xik w1 1 0
k 1 i 1
j 1
# of samples << # of variables
N
2
(
y
a
x
a
x
....
a
x
)
1 i1
2 i2
1 iM
0
ak i 1
# of samples > # of variables
x Correlation of variables
y1
y
2
...
y2
...
y N
x11
x
21
...
xi1
...
x N 1
x12
... x1 j
x22
...
... x2 j
... ...
xi 2
...
xij
... ... ...
x N 2 ... x Nj
... x1M
... x2 M
... ...
... xiM
... ...
... x NM
18
PLS regression modeling
5.0
r = 0.97
T8
T7
Predicted OD600 value
T6
OD600 = a1 x1 +…+ aj xj +….+ aM xM
xj , the quantity for jth
aj > 0, stationary-phase dominant metabolites
T5
aj < 0, exponential-phase dominant metabolites
T4
T1
T2
T3
0.0
0.0
5.0
Observed OD600 value
Our constructed model
- Could work well because of r = 0.97.
- Is informative to clarify the relation between a growth stage and
metabolic profile.
19
Coefficients in the constructed model
OD600 = a1 x1 +…+ aj xj +….+ aM xM
xj , the quantity for jth
aj > 0, stationary phase-dominant metabolites
aj < 0, exponential phase-dominant metabolites
The ions with the negative and positive coefficients contribute to the constructed
model, negatively and positively, and are dominant in exponential and stationary
phase, respectively.
0.1
aj
0.0
-0.15
Exponential-phase dominant
Stationary-phase dominant
20
KNApSAcK search
DrDMASS+
KNApSAcK
(i)
(ii)
(iii)
(i) Peak Correction
(ii) Multivariate data processing
(iv)
(iii) Unsupervised learning
PCA, BL-SOM
KNApSAcK search
# of metabolites
20752
# of species-metabolite
pairs
41206
(iv) Supervised learning
PLS
Sample A1
x11
Sample A2 x21
.....
Sample A3
xs1
.....
Sample B1
xt1
Sample B2 .....
Sample B3 xN 1
Sample A1
Sample A2
Sample A3
Sample B1
Sample B2
Sample B3
x12 ..... x1 j ..... x1k ..... x1M
..... ..... x2 j ... x2 k ..... x2 M
..... ..... ..... ... ..... ..... .....
xs 2 ..... ..... ... ..... ..... xsM
..... ..... ........ ..... ..... .....
xt 2 ..... xtj ... ..... ..... xtM
..... ..... ........ ..... ..... .....
xN 2 ..... xNj ... xNk ..... xNM
m/z
21
Coefficients in the constructed model
OD600 = a1 x1 +…+ aj xj +….+ aM xM
xj , the quantity for jth
aj > 0, stationary phase-dominant metabolites
aj < 0, exponential phase-dominant metabolites
Red: E.coli metabolites
Black: Other bacterial metabolites
0.1
aj
dTDP-6-deoxy-L-mannose
Parasperone A
UDP-glucose, UDP-galactose
UDP-N-acetyl-D-glucosamine
UDP-N-acetyl-D-mannosamine
Lenthionine
omega-Cycloheptylnonanoate
omega-Cycloheptylundecanoate, cis-11-Octadecanoic acid
UDP
Octanoic acid
dTMP, dGMP, 3'-AMP
NADH
MS/MS analyses
733.5056 (PG2)
761.5293 (PG4)
0.0
MS/MS analyses
747.5183 (PG3)
Argyrin G
omega-Cycloheptyl-alpha-hydroxyundecanoate
ATP, dGTP
omega-Cycloheptyl-alpha-hydroxyundecanoate
dTDP
Glyoxylate
719.4868 (PG1)
-0.15
Exponential-phase dominant
ADP, Adenosine 3',5'-bisphosphate, dGDP
ADP-(D,L)-glycero-D-manno-heptose
NAD
Stationary-phase dominant
22
MS/MS analyses
100
Ion intensity
719.4868 (PG1)
253.2181
[R2O]-
255.2337
391.2260
[R1O][M-C3H6O2 - H - R2OH]-
R1=
465.2628
[M - H - R2OH]-
C C15H31
R2=
C C15H29
O
O
719.4868
[M -H]-
483.2735
[M - R2]0
100
Ion intensity
733.5056 (PG2)
255.2338
[R1O]-
267.2339
[R2O]391.2268
[M-C3H6O2 - H - R2OH]465.2639
[M - H - R2OH]-
R1=
C C15H31
R2=
O
C C16H31
O
733.5056
[M - H]-
483.2741
[M - R2]-
747.5183 (PG3)
Ion intensity
0
100
255.2345
[R1O]-
281.2502
[R2O]-
R1=
C C15H31
R2=
O
391.2281
[M-C3H6O2 - H - R2OH]-
C
C17H33
O
465.2659
[M - H - R2OH]483.2744
[M - R2]-
747.5183
[M - H]-
761.5293 (PG4)
Ion intensity
0
100
255.2342
[R1O]-
295.2654
[R2O]465.2651
[M - H - R2OH]391.2271
[M-C3H6O2 - H - R2OH]-
R1=
C C15H31
R2=
O
C
C18H35
O
761.5293
[M -H]-
483.2772
[M - R2]-
0
100
200
300
400
500
600
700
800 m/z
23
Summary of phosphatidylglycerols detected in this study
(a) Elucidated structures (PG1 to PG4)
ID
Combination of three substructures (X1, X2, X3)
C C15H29
PG1
O
C C16H31
PG2
O
C
PG3
C17H33
C C15H31
OH
P O CH2 CHOH CH2OH
O
O
CH2 O
X1
CH
O
X2
CH2 O
X3
O
C
PG4
C18H35
O
(b) Relation of mass differences among PG1 to 10
(Cluster 1)
∆(CH2)2
PG5
30:1(14:0,16:1) 28.0281
∆(CH2)2
PG1
32:1(16:0,16:1) 28.0315
PG3
34:1(16:0,18:1)
US
CFA 14.0170
CFA 14.0187
CFA 14.0110
∆(CH2)2
∆(CH2)2
PG6
PG2
31:0(14:0,c17:0) 28.0298 33:0(16:0,c17:0) 28.0237
∆(CH2)2
PG7
34:2(16:1,18:1) 28.0330
PG9
36:2(18:1,18:1)
PG4
CFA 14.0181
34:5(16:0,c19:0)
US
(Cluster 2)
2.0138
CFA 14.0197
2.0051
∆(CH2)2
PG8
PG10
35:1(16:1,c19:1) 28.0314 37:1(18:1,c19:0)
24
Cyclopropane fatty acid (CFA) formation
4.0
O
Ratio of relative ion intensity
T1
T2
T3
T4
T5
T6
T7
T8
O
C15H31
PG1
O
O
X3
O
0.0
O
O
C15H31
O
PG2
O
X3
O
PG2/PG1
PG4/PG3
CFA formation occurs as the cells enter into
stationary
phase.
O
-8.0
O
C15H31
PG3
O
O
X3
O
O
O
C15H31
O
PG4
O
X3
O
Constructed model using PLS regression would be useful for extracting of
characteristic variables.
CFA formation of PGs occurs, as E.coli enters stationary phase.
25
[1] Metabolomics approach for determining growth-specific
metabolites based on FT-ICR-MS
[2] Bio-Database developed by our lab.
2.1 Species-metabolite relation database (KNApSAcK)
2.2 Easy Gene Classifier to Functional Group
26
[1]KNApSAcK
27
KNApSAcK link version
http://kanaya.naist.jp/knapsack_jsp/top.html
28
KNApSAcK（http:/kanaya.naist.jp/KNApSAcK ）
(Since 2004)
Authors who utilize KNApSAcK DB ( Thanks!)
Farder, A. et al., J. Nutrition, 138, 1282-1287, (2008) (Red, in Japan)
Takahashi, H., Anal. Bioanal Chem. (in press) (2008)
Mintz-Oron, S., et al., Plant Physiol.,147,823-825, (2008)
Iijima, Y., et al., Plant J., 54, 949-962, (2008)
Overy, D.P., et al., Nature Protocols, 3, 471-485, (2008)
Dunn, W.B., Physical Biol., 1-24, 5, (2008)
Want, E.J. et al., J. Proteome Res., 6, 459-468, (2007)
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(WikiBook)
http://en.wikibooks.org/wiki/Metabolomics/Databases
(UC Davis）
http://fiehnlab.ucdavis.edu/staff/kind/Metabolomics/Structure_Elucidation/
(KEGG)
http://fire3.scl.genome.ad.jp/dbget-bin/www_bfind?knapsack
（LECO社マニュアル）
29
http://en.wikibooks.org/wiki/Metabolomics/Databases
30
Linked by KEGG DB
http://fire3.scl.genome.ad.jp/dbget-bin/www_bfind?knapsack
31
KNApSAcK – Lupin Alkaloids
http://kanaya.naist.jp/knapsack_jsp/lupin/top.html
32
[2] Other DB developed in our group
Function annotation DB for Arabidopsis thaliana
http://kanaya.naist.jp/arabidopsis/top.jsp
Functional annotations
Cellular Localization inf.
14502 genes
2242 genes
33
Categorization of genes into functional classes
34
Categorization of gene pairs into pairs of
functional classes
35
[3] DB for Edible Organisms
http://kanaya.naist.jp/LunchBox/top.jsp
36
Allium cepa
Link to KNApSAcK DB
37
Time series change of total number of detected ions
(a)
10
T6
T7
T8
T5
T4
T3
OD600
1
(b)
T2
T1
0.1
120
Number of detected ions
(c)
0
1.0
0.0
1.0
Relative ion intensity
0.0
1.0
0.0
1.0
0.0
1.0
Cluster 5
Cluster 3
Cluster 1
Cluster 2
Cluster 4
0.0
0
800
Time (min)
38