1 Bioinformatics in Metabolomics Shigehiko Kanaya NAra Institute of Science and Technology Graduate School of Information Science; Comparative Genomics Lab. [1] Metabolomics approach for determining gro wth-specific metabolites based on FT-ICR-MS [2] Bio-Database developed by our lab. 2.1 Species-metabolite relation database (KNA pSAcK) 2.2 Easy Gene Classifier to Functional Group
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Bioinformatics in Metabolomics Shigehiko Kanaya NAra Institute of Science and Technology
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. - PowerPoint PPT Presentation
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Bioinformatics in MetabolomicsShigehiko KanayaNAra Institute of Science and TechnologyGraduate 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
2
Data Processing from data acqisition of a time series experiment to description of cellular conditions
0.1
1
10
0 200 400 600 800
Time (min)
OD
600
T1T2
T3T4
T5T6 T7 T8
(a) Time series experiments
MM+1
M/2
Metabolite-derivative group (Isotope ions and multivalent ions)
(e) Assessment of cellular condition by metabolite composition
sM
Mk
Mk
ss
j
j
x
xx
xx
xx
xx
xxx
.............
..................
........
..........
..........
....................
..........
.....
22
11
21
221
11211
m/z
Tim
e p
oin
t
(b) Data preprocessing and constructing data matrix
(d) Annotation of ions as metabolites
(c) Classification of ions into metabolite-derivative group
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.
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DrDMASS+
(i)
(ii)
(iii) (iv)
Sample A1
Sample B3
Sample B2
Sample A2
Sample A3
Sample B1
(ii) Multivariate data processing (i) Peak Correction
NMNk
tMtjtt
sM
Mk
Mk
ss
j
j
xx
x
xxx
xxx
x
xx
xx
xx
xx
xxx
NjNN ........
..................
.............
..................
.....
....................
.....
....................
.............
..................
........
..........
..........
....................
..........
.....
21
21
22
11
21
221
11211
Sample B3
Sample A2
Sample A1
m/z
Sample A3
Sample B2
Sample B1
(iii) Unsupervised learning PCA, BL-SOM
(iv) Supervised learning PLS
KNApSAcK
KNApSAcK search
KNApSAcK search# of metabolites 20752# of species-metabolite pairs41206
Red: E.coli metabolitesBlack: Other bacterial metabolites
MS/MS analyses
MS/MS analyses
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100
0
Ion
inte
nsit
y
253.2181[R2O]-
255.2337[R1O]- 391.2260
[M-C3H6O2 - H - R2OH]-
465.2628[M - H - R2OH]-
483.2735[M - R2]-
719.4868[M -H]-
255.2338[R1O]-
267.2339[R2O]-
391.2268[M-C3H6O2 - H - R2OH]-
465.2639[M - H - R2OH]-
483.2741[M - R2]-
733.5056[M - H]-
255.2345[R1O]-
281.2502[R2O]-
391.2281[M-C3H6O2 - H - R2OH]-
465.2659[M - H - R2OH]-
483.2744[M - R2]-
747.5183[M - H]-
C
O
C15H31R1= R2= C
O
C15H29
C
O
C15H31R1= R2=
C
O
C15H31R1= R2=
C
O
C16H31
C
O
C17H33
MS/MS analyses
100 200 300 400 500 600 700 800 m/z
255.2342[R1O]-
295.2654[R2O]-
391.2271[M-C3H6O2 - H - R2OH]-
465.2651[M - H - R2OH]-
483.2772[M - R2]-
761.5293[M -H]-
C
O
C15H31R1= R2= C
O
C18H35
100
0
Ion
inte
nsit
y
100
0
Ion
inte
nsit
y
100
0
Ion
inte
nsit
y
719.4868 (PG1)
761.5293 (PG4)
747.5183 (PG3)
733.5056 (PG2)
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Summary of phosphatidylglycerols detected in this study
C
O
C15H31
C
O
C15H29PG1
ID Combination of three substructures (X1, X2, X3)
PG2
PG3
PG4
C
O
C16H31
C
O
C17H33
C
O
C18H35
P
O
OOH
CH2 CHOH CH2OH
(b) Relation of mass differences among PG1 to 10
(a) Elucidated structures (PG1 to PG4)
PG530:1(14:0,16:1)
PG132:1(16:0,16:1)
PG334:1(16:0,18:1)
PG631:0(14:0,c17:0)
PG233:0(16:0,c17:0)
PG434:5(16:0,c19:0)
PG734:2(16:1,18:1)
PG936:2(18:1,18:1)
PG835:1(16:1,c19:1)
PG1037:1(18:1,c19:0)
(Cluster 1)
28.0281
14.0170
(Cluster 2)
14.0187 14.0110
14.0181
28.0315
28.0298 28.0237
2.0138
2.0051
28.0330
28.0314
14.0197
CH
CH2
CH2 O
O
O X3
X2
X1
CFA CFA CFA
CFA CFA∆(CH2)2
US
US
∆(CH2)2
∆(CH2)2
∆(CH2)2
∆(CH2)2
∆(CH2)2
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Cyclopropane fatty acid (CFA) formation
O
O C15H31
O
O
OX3
O
O C15H31
O
O
OX3
O
O C15H31
O
O
OX3
O
O C15H31
O
O
OX3
PG1
PG2
PG3
PG4
T1 T2 T3 T4 T5 T6 T7 T84.0
0.0
-8.0
PG2/PG1
PG4/PG3 CFA formation occurs as the cells enter into stationary phase.
Rat
io o
f re
lati
ve io
n in
ten
sity
Constructed model using PLS regression would be useful for extracting of characteristic variables. CFA formation of PGs occurs, as E.coli enters stationary phase.
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[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
27
[1]KNApSAcK
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KNApSAcK link versionhttp://kanaya.naist.jp/knapsack_jsp/top.html
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)Sofia, M., et al., Trends in Anal. Chem., 26, 855-866, (2007)Ohta, D., et al., Anal.Biol. Chem.(2007)Nakamura, Y., et al., Planta, (2007)Suzuki, H., et al., Phytochemistry, (2007)Sakakibara, K., et al., , J .Biol. Chem.,282, 14932-14941, (2007)Saito, K. et al., Trends in Plant Sci., 13, 36-42, (2007)Hummel, J., et al., Topics in Curr. Genet., 18, 75-95, (2007)Gaida, A., and Neumann, S., J. Int. Bioinf., (2007)Kikuchi, K and Kakeya, H., Natuure Chem. Biol., 2, 392-394, (2006)Oikawa, A.,et al., Plant Physiol., 142, 398-413, (2006)Shinbo, Y., et al., Biotchnol. Agric. Forestry, 57, 166-181, (2006)Shinbo, Y., et al., J. Comput. Aided Chem., 7, 94-101, (2006)(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)