1 today’s topics general discussion on systems biology metabolomics approach for determining...

1

Today’s topics

•General discussion on systems biology

•Metabolomics approach for determining growth-specific metabolites based on FT-ICR-MS

•Self organizing mapping(SOM)

What is systems biology?

Each lab/group has its own definition of systems biology.

This is because systems biology requires the understanding and integration of different branches of science and different levels of OMICS information together and individual labs/groups are working on different area.

Theoretical target: Understanding life as a system.Practical Targets: Serving humanity by developing new generation medical tests, drugs, foods, fuel, materials, sensors, logic gates……

Bioinofomatics

a

b c

d e f g

h i k m

j l

5’

5’3’

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A B C D E F G H I J K L MProtein

A B C D EF

G H I JK L MFunctionUnit

Metabolite 1 Metabolite 2 Metabolite 3

Metabolite 4

Metabolite 5

Metabolite 6

B C

D EF

I L

H KMetabolic Pathway

G

Activation (+)A

GRepression (-)

ab c

d e f gh i k m

j l5’

5’3’3’

Genome:

Transcriptome ：

Proteome, Interactome

MetabolomeFT-MS

Integration of omicsto define elements(genome, mRNAs, Proteins, metabolites)

Understanding organism as a system (Systems Biology)

Understanding species-species relations (Survival Strategy)

comprehensive and global analysis of diverse metabolites produced in cells and organisms

Prescription

ProteomeInteractomeTranscriptomeMetabolomicsMedicinal Herb.

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PhysiologicalActivity

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ProteomeInteractomeTranscriptome

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Plant Omics Human Omics

Plant-Human interacted Systems biology

Plant Systems Biology Human Systems Biology

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Traditional & Modern Knowledge of Medicinal Plants

Modelling can be extended to Plant-Human interaction.

Okada, T., Afendi, FM., Amin, M., Takahashi, H., Nakamura, K., Kanaya, S.,Current Computer Aided Drug Design, 179-196, 10, (2010)

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(1) Comprehensively understanding of each layers

Principal component analysisBL-SOMDPClus……….……….


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(2) Relation between layersMathematical modelingPartial Least Square Multi-regression AnalysisDiscriminant analysis

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XfyTherapeutic UsagePhysiological activity etc. Herb composition

metabolites in herbs.


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Plant-Human interaction

(1,2)Multivariate analysis

Partial least Square modelingPrincipal Compornet AnalysisBL-Selforganizing MapDPClus (Network clustering)….….

Metabolomics

Transcriptomcs

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This situation can be exteneded to Plant-Human interaction.

(3) Knowledge Systematization of interaction between human and plantsDatabase

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Plant Systems Biology

Human Systems Biology

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(4) Systems Biology for Plant-Human interaction

[1] Responsibility of synergetic activity[2] reduction of side effects in medication for the complexity of disease derived by mutifactorial causes [3] metabolites in plants interact with multiple targeted proteins in humanregulate gene expression lead to dynamical state change in metabolome and physiological activity in human.

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Metabolomics approach for determining growth-specific metabolites based on FT-ICR-MS

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[1] Metabolomics

Metabolite 1 Metabolite 2 Metabolite 3

Metabolite 4

Metabolite 5

Metabolite 6

B C

D EF

I L

H K

Interpretation of Metabolome

Species

Molecular weight and formula

Fragmentation Pattern

Metabolite information

Species Metabolites

Tissue Samples

Species-Metabolite relation DB

Experimental Information

MS

Data Processing from FT-MS data acquisition of a time series experiment to assessment of cellular conditions

0.1

1

10

0 200 400 600 800

Time (min)

OD

600

T1T2

T3T4

T5T6 T7 T8(a) Metabolite quantities

for time series experiments

Metabolites

MM+1

M/2(e) Assessment of cellular condition by metabolite composition

sM

Mk

Mk

ss

j

j

x

xx

xx

xx

xx

xxx

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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

Detectedm/z

Theoreticalm/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

662.1037 663.1109 C21H27N7O14P2 663.1091 0.0018 NAD Escherichia coli

664.1095 665.1168 C21H29N7O14P2 665.1248 0.0080 NADH Escherichia coli

.....

..........

..........

.....

..... ..........

.......... .....

.....

.....

.....

.....

..........

.....

.....

.....

E. coli

14

time

719.4869

722.505

747.5112

NMNk

tMtjtt

sM

Mk

Mk

ss

j

j

xx

x

xxx

xxx

x

xx

xx

xx

xx

xxx

NjNN ........

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11211time 1

time 8

time 2

metab.1 metab.200(b) Data matrix

Software are provided by T. Nishioka (Kyoto Univ./Keio Univ.)

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1-1

1-2

1-3

1-4,5

1-6

2-1

2-2

2-3

3

45

6

78

9

10

11

PG5

PG7

PG9 PG3

PG1

PG6

PG2

PG4

PG10

PG8

M-1

M-2 M-3

M-4

M-5

M-6

M-7

M-8

M-9M-10

M-11

M-12

M-13

M-14

M-15

M-16

M-17

(c) Classification of ions into metabolite-derivative group (DPClus)

Correlation network for individual ions.

Intensity ratio between Monoisotope (M) and Isotope (M+1) # of Carbons in molecular formula:

16

(d) Annotation of ions as metabolites using KNApSAcK DBDetected

m/za

Theoreticalm/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



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

454.0391 455.0464 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

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

606.0775 607.0848 C17H27N3O17P2 607.0816 0.0032 UDP-N-acetyl-D-mannosamine Escherichia coli

C17H27N3O17P2 607.0816 0.0032 UDP-N-acetyl-D-glucosamine Escherichia coli

618.0897 619.0970 C17H27N5O16P2 619.0928 0.0042 ADP-L-glycero-beta-D-manno-heptopyranose

Escherichia coli

662.1037 663.1109 C21H27N7O14P2 663.1091 0.0018 NAD Escherichia coli

664.1095 665.1168 C21H29N7O14P2 665.1248 0.0080 NADH Escherichia coli

741.4729 742.4801 C32H62N12O8 742.4814 0.0012 Argimicin A Sphingomonas sp.

786.4712 787.4785 C41H65N5O10 787.4731 0.0054 BE 32030B Nocardia sp. A32030

853.3166 854.3239 C41H46N10O9S 854.3170 0.0069 Argyrin G Archangium gephyra Ar 8082

C45H56Cl2N2O10 854.3312 0.0073 Decatromicin B Actinomadura sp. MK73-NF4

C39H50N8O12S 854.3269 0.0030 Napsamycin C Streptomyces sp. HIL Y-82,11372

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PLSY

Responses

X

N=8

M=220K=1

N=8

PLS (Partial Least Square regression model) -- extract important combinations of metabolites. N (biol.condition) << M (metabolites)

(e) Estimation of cell condition based on a function of the composition of metabolites.

Y(Cell density)= a1 x1 +…+ aj xj +….+ aM xM

xj, the quantity for jth metabolites

cell condition cell condition

mea

sure

men

t p

oin

ts

Metabolites0.1

1

10

0 200 400 600 800Time (min)

OD

600

T1T2T3

T4T5

T6T7 T8

0.1

0.0

ajUDP-glucose, UDP-galactose

NAD

Parasperone A

UDP-N-acetyl-D-glucosamineUDP-N-acetyl-D-mannosamine

ADP, Adenosine 3',5'-bisphosphate, dGDP

UDP

omega-Cycloheptyl-alpha-hydroxyundecanoate

Octanoic aciddTMP, dGMP, 3'-AMP

NADH

Argyrin G

dTDP

ATP, dGTP

Lenthionine

omega-CycloheptylnonanoatedTDP-6-deoxy-L-mannoseomega-Cycloheptylundecanoate, cis-11-Octadecanoic acid

ADP-(D,L)-glycero-D-manno-heptose

Glyoxylate

omega-Cycloheptyl-alpha-hydroxyundecanoate

-0.15

Stationary-phase dominantExponential-phase dominant

y(OD600 Cell Density)= a1 x1 +…+ aj xj +….+ aM xM

aj > 0, stationary phase-dominant metabolites

xj , the quantity for jth

aj < 0, exponential phase-dominant metabolites

(e) Assessment of cellular condition by metabolite compositionDetection of stage-specific metabolites

(PLS model of OD600 to metabolite intensities)

Red: E.coli metabolites;Black: Other bacterial metabolites

PG1,3,5,7,9

MS/MS analyses

120 metabolites

80 metabolites

MS/MS analysesPG2,4,6,8,10

10 Phosphatidylglycerols detected by MS/MS spectra

(b) Relation of mass differences among PG1 to 10marker molecules

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

CFA CFA CFA

CFA CFA∆(CH2)2

US

US

∆(CH2)2

∆(CH2)2

∆(CH2)2

∆(CH2)2

∆(CH2)2

O

O C15H31

O

O

OX3

O

O C15H31

O

O

OX3

Cyclopropane Formation of PGs occurs in the transition from exponential to stationary phase.

Exponential phase

Stationary phase

Cyclopropane Formaiton of PGs

unsaturated PGs

cyclopropanated PGs

Self organizing Maps

Time-series Data

0.01

0.1

1

10

12

Tj

Time

Growth curve

DTDjDD

iTijii

Tj

Tj

xxxx

xxxx

xxxx

xxxx

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21

21

222221

111211

…

D

i

Gene

Gene

Gene

Gene

...

...2

1

Expression profiles

…

When we measure time-series microarray, gene expression profile is represented by a matrixSOM makes it possible to examine gene similarity and stage similarity simultaneously.

Stage 1 2 …. j … T

D

i

x

x

x

x

...

...

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T, # of time-series microarray experimentsD, # of genes in a microarray

Time-series Data

0.01

0.1

1

10

12

Tj

Time

Growth curve

DTDjDD

iTijii

Tj

Tj

xxxx

xxxx

xxxx

xxxx

......

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......

21

21

222221

111211

…

D

i

Gene

Gene

Gene

Gene

...

...2

1

Expression profiles

…

When we measure time-series microarray, gene expression profile is represented by a matrixSOM makes it possible to examine gene similarity and stage similarity simultaneously.

Stage 1 2 …. j … T

D

i

x

x

x

x

...

...

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T, # of time-series microarray experimentsD, # of genes in a microarray … …

Stage similarity

Expression similarity

STATES

State-Transition

Multivariate AnalysisSOM : expression similarity of genes and stage similarity simultaneously.

BL-SOM is available at http://kanaya.aist-nara.ac.jp/SOM/

SOM was developed by Prof. Teuvo Kohonen in the early 1980s

Multi-dimensional data/input vectors are mapped onto a two dimensional array of nodes

In original SOM, output depends on input order of the vectors.

To remove this problem Prof. Kanaya developed BL-SOM.

[1] Initial model vectors are determined based on PCA of the data.

[2] The learning process of BL-SOM makes the output independent of the order of the input vectors.

SOM Algorithm

Source: “Clustering Challenges in Biological Networks” edited by S. Butenko et. al.

SOM Algorithm

in Fig. before

Source: “Clustering Challenges in Biological Networks” edited by S. Butenko et. al.

X2

X1

XT

Self-organizing Mapping (Summary)

Gene i (xi1,xi2,..,xiT)

DTDjDD

iTijii

Tj

Tj

xxxx

xxxx

xxxx

xxxx

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D

i

Gene

Gene

Gene

Gene

...

...2

1

D

i

x

x

x

x

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T, different time-series microarray experiments

[1] Detection method for transition points in gene expression and metabolite quantity based on batch-learning Self-organinzing map (BL-SOM)

[2] Diversity of metabolites in species Species-metabolite relation Database

X2

X1

XT

Self-organizing Mapping (Summary)Arrangement of lattice points in multi-dimensional expression spaceLattice points are optimized for reflecting data distribution

Gene ClassificationGenes are classified into the nearest lattice points

Gene i (xi1,xi2,..,xiT)

X2

X1

XT

Self-organizing Mapping (Summary)

Non-linear projection of multi-dimensional expression profiles of genes.Original dimension is conserved in individual lattice points.Several types of information is stored in SOM

Arrangement of lattice points in multi-dimensional expression spaceLattice points are optimized for reflecting data distribution

Gene ClassificationGenes with similar expression profiles are clusterized to identical or near lattice points

Feature Mapping In the i-th condition, lattice points containing only highly (low) expressed genes are colored by red (blue).

Xk> Th.(k)

Xk< -Th.(k)

X1 (Time 1)

X2 (Time 2)

X3 (Time 3)

XT (Time T)

Visually comparing among each stage of time-series data

(ex.)

…..…..…..

k=1,2,…,T

SOM for time-series expression profile

Estimation of transition points; Bacillus subtilis (LB medium) (Data: Kazuo Kobayashi, Naotake Ogasawara (NAIST))

Stage 1 2 3 4 5 6 7 8

(min)

Cell Density (OD600 )

0.001

0.01

0.1

1

10

-1000

0

-2000

1

2

34

8765

LB

log(Prob. Density)

0 200 400 600 800 1000

State transition point is observed between stages 3 and 4

Low prob.

High prob.

Integerated analysis of gene expression profile and metabolite quantity data of Arabidopsis thaliana (sulfur def./cont.; Data are provided by K.Saito, M. Hirai group (PSC) )

Nakamura et al (2004)

ppm(error rate)

Accurate molecular weights Candidate metabolites corresponding to accurate molecular weights

3. Species-metabolite relation Database

Lattice points with highly difference between 12 and 24 h.Blue: DecreasedRed: increased

Gene

Metabolites(m/z)

Feature Maps

State transition

Root Root

LeafLeaf

Download sites of BL-SOMRiken ：　 http://prime.psc.riken.jp/NAIST: http://kanaya.naist.jp/SOM/

Application of BL-SOM to “-omics”

Genome

Kanaya et al., Gene, 276, 89-99 (2001)Abe et al., Genome Res., 13, 693-702, (2003)Abe et al., J.Earth Simulator, 6, 17-23, (2003)Abe et al., DNA Res., 12, 281-290. (2005) Transcriptome Haesgawa et al., Plant Methods, 2:5:1-18 (2006)

MetabolomeKim et al., J. Exp.Botany, 58, 415-424, (2007)Fukusaki et al., J.Biosci.Bioeng., 100, 347-354, (2005)

Transcriptome and MetabolomeHirai, M. Y., M. Klein, et al. J.Biol. Chem., 280, 25590-5 (2005)Hirai, M. Y., M. Yano, et al. Proc Natl Acad Sci U S A 101, 10205-10 (2004)Morioka, R, et al., BMC Bioinformatics, 8, 343, (2007)Yano et al., J.Comput. Aided Chem.,7,125-136 (2007)……

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Some other popular clustering/classification algorithms:

K-mean clustering

Support vector machines

Summary of Bioinformatics Tool developed in our laboratory http://kanaya.naist.jp/~skanaya/Web/JTop.html

Metabolomics-- MS data processing

Transcriptome and Metabolomics Profiling-- estimation of transition points

Species-metabolite DB

Transcriptomics-- Statistics, Profiling, …

Network analysis: PPI

All softwares and DB are freely accessable via Web.

www.geneontology.org www.genome.ad.jp/kegg www.ncbi.nlm.nih.gov www.ebi.ac.uk/databases http://www.ebi.ac.uk/uniprot/ http://www.yeastgenome.org/ http://mips.helmholtz-muenchen.de/proj/ppi/ http://www.ebi.ac.uk/trembl http://dip.doe-mbi.ucla.edu/dip/Main.cgi www.ensembl.org

Some websites

Some websites where we can find different types of data and links to other databases

1 today’s topics general discussion on systems biology metabolomics approach for determining...

Documents

definition of systems

therapeutic usageconnect

herb compositionmetabolites

different area

growthspecific metabolites

multivariate analysispartial

new generation medical

individual labsgroups