ScienceWWebinar Seriesebinar Series slides_Omics webi… · 3. Getting Closer to the Metabolic Whole. Sauer, Heinemann & Zamboni 2007 Science 316: 550 . environment. phenotype •

Sponsored by:

Participating Experts:

Dr. Uwe SauerInstitute of Molecular Systems BiologyZürich, Switzerland

Dr. Albert FornaceGeorgetown UniversityWashington, DC

Dr. Richard GrossWashington University in St. LouisSt. Louis, MO

Brought to you by the Science/AAAS Business Office12 October, 201012 October, 2010

Integrated Biology: What isneeded to bring Omics together?Integrated Biology: What isneeded to bring Omics together?

Webinar SeriesWebinar SeriesScienceScience

2

Uwe Sauer, Professor of Systems Biology, ETH Zurich

ETH Zürich | Institute of Process Engineering | Bioprocess Lab | CH-8092 Zürich

Omics Data Integration in Metabolic Research Not only desirable, but necessary for discovery and mechanistic understanding !

• Steady state (baker’s yeast)• Dynamic nutritional adaptations (B. subtilis)

Institute of Molecular Systems Biology

3

Getting Closer to the Metabolic Whole

Sauer, Heinemann & Zamboni 2007 Science 316: 550

environment phenotype

• development• movement• cell cell comm.• .........• metabolism

4

Integration at Steady State: What is the relationship

between enzymes and metabolites ?????

Bakers‘

yeastmetabolites

DNA

mRNA

Proteins

Environment

Sarah‐Maria Fendt, Jörg Büscher, Florian Rudroff, Paola Picotti, Nicola Zamboni

& Uwe Sauer

5

Reduction of single enzyme expression leads to a strong local increase in the enzymes’ substrate (until the pathway flux changes ……..)

Inverse relationship: Circumstantial observation or common principle ?

Formulate experimentally testable hypotheses from enzyme kinetics

Fendt, Büscher, Rudroff, Picotti, Zamboni & Sauer 2010 Molecular Systems BiologyFendt, Büscher, Rudroff, Picotti, Zamboni & Sauer 2010 Molecular Systems Biology

6

What is Needed for Testing of Broader Principle ?• Quantitative metabolite [c]

• Flux direction

• In vivo enzyme activity approximate by:

– Quant protein changes

– Quant transcript changes

• Perturb abundance of enzyme

Targeted LC-MS/MS30 central metabolites

Computational flux balance analysis based on physiological data

Targeted proteomics50 central enzymes

Aebersold lab, ETH

Deleting a TF that modulates enzyme abundanceLittle flux change!

GCR2 in yeastDecreased expression of glycolysisIncreased expression of TCA cycle

Affi chips


7

Multiple Perturbations through Transcription Factor Deletion (GCR2 in yeast)

Substrate Metabolite fold change

up to 5-fold less enzyme only 40% less flux

Enzyme fold change

No correlations with• Reaction products • Cofactors• Transcripts

(only weak correlation)

Fendt, Büscher, Rudroff, Picotti, Zamboni & Sauer 2010 Molecular Systems Biology Fendt, Büscher, Rudroff, Picotti, Zamboni & Sauer 2010 Molecular Systems Biology

8

Conclusions• Altered enzyme activity triggers local inverse response

of own substrates

– fast “passive” response to maintain close-to-wt homeostasis (eg upon stochastic fluctuations)

– perturbations do NOT propagate (via ‘hub’ metabolites)

• Metabolic network performance is generally robust to mild fluctuations of its constituents; ie. enzymes and metabolites


9

Dynamic Nutritional Shifts What are the Key Regulation Mechanisms ?

Glucose

Glc 6-P

Frc 6-P

Frc 1,6-bP

1,3-BisP-glycerate

Rul 5-P

Rib 5-PXul 5-P

Sedoheptulose

Erythrose-4-P

Glyceraldehyde3-PAcetone-P

Dihydroxy-7-P

NADPHCO2

NADPH

NADH NADPH

PEP

Acetyl-CoA

Citrate

2-Oxoglutarate

Succinate

Malate

Oxaloacetate

Pyruvate

NADHCO2

CO2

CO2 NADHCO2

NADPHCO2

NADHCO2FADH2

NADH

Acetate

PGA Gly

NADH

Shift experiment:Shift experiment:

malate

glucose

The „BIG Experiment“

Bacillus subtilis

2 nutritional shifts to coutilization

3 replicates, 10-20 samples each

Multi-omics

Collaborative effort

50 scientists from 16 labs

Adaptations at all levels: metabolic, protein modification, genetic .....

BaSysBio Team, unpublishedBaSysBio Team, unpublished

10

Jörg Büscher – PhD Defense

11

Jörg Büscher – PhD Defense

12

Some Highlights• Measured data

– Metabolic adaptations precede transcript/protein changes

• Inferred data (through modeling)– Transcription factor activity profiles (network control analysis;

Wolf Liebermeister, Humboldt Univ Berlin)

– Dynamic metabolic fluxes (least square fitting)

– Flux controlling reactions (correlation flux/protein)

– .......

• Although hundreds of transcripts/proteins/ metabolites change, modeling & experimental validation reveals that only very few changes are required for adaptation !

BaSysBio Team, unpublishedBaSysBio Team, unpublished

13

Challenges – Best Practices• Before one can even consider data integration

– Biological variability (in dynamic exps)• all samples from same physical cultures• standardization

– Naming and formatting conventions (one thing - one name)– Combine overlapping dynamic data

• Consensus omics data from different analytical platforms (eg 2D and gel-free proteomics data)

• Replicate time series smoothed/interpolated by Bayesian multicurve regression• Algorithm for metabolomics data smoothing

• Without some type of computational data integration – almost meaningless data piles !

• “Soft” problems– Develop a common language– Leadership at many levels (most effective: small teams with a lead person !)

– Multi-authorship

Sponsored by:








Professor of Medicine, Chemistry and Developmental Biology

Richard W. Gross, M.D., Ph.D.

Integration of the “Omics”in Systems Biology

Disclosure:

Dr. Gross has financial interests in:

LipoSpectrumU.S. Patents 7,306,952; and 7,510,880

PlatomicsU.S. Patent 12/174,493

Chemistry Department

Integration of the “Omics”

Genomics

Proteomics

MetabolomicsLipidomics

Traditional Approaches to Systems Biology

Biological Hierarchy of Metabolic Regulation

Bacteria Yeast Mammals

Metabolic Compartmentation

ReceptorsIon Channels and

Transporters

1. Diverse repertoire of discrete chemical species to modulate transmembrane protein activities.

2. Scaffolding for self-organizing chemical assemblies.3. Storage depot for latent 2nd messengers of signal transduction.4. Bilayer medium for membrane trafficking, membrane fusion,

and hormone release.

Functions of Biologic Membranes

Phospholipases

1. The functional sequelae of SNPs and the consequences of alterations in transcript levels can not be routinely predicted in a comprehensive manner.

2. The amount of protein does not necessarily correlate with enzyme activity or protein function due to multiple posttranslational modifications and compartmentation.

3. Examination of whole cell or tissue extracts are not necessarily indicative of the physical interactions between moieties.

Challenges in Integrating the "Omics" to Identify Mechanisms Underlying

Common Diseases

l1 , l2 , l3 , . . . . ln m1 ,m2 ,m3 ,...mn p1 , p2 , p3, . . .pn x1 , x2 , x3 ,. . .xn

l1 , l2 , l3 , . . . . lnm1 ,m2 ,m3 ,...mnp1 , p2 , p3, . . .pnx1 , x2 , x3 ,. . .xn

Bidirectional Signaling Between Discrete Membrane Compartments

l1 , l2 , l3 , . . . . ln m1 ,m2 ,m3 ,...mn p1 , p2 , p3, . . .pn x1 , x2 , x3 ,. . .xn

l1 , l2 , l3 , . . . . lnm1 ,m2 ,m3 ,...mnp1 , p2 , p3, . . .pnx1 , x2 , x3 ,. . .xn

Compartment A Compartment B

Discriminative Machine Learning Approaches

Gly

Normal Diabetes

40%

Fatty Acid

Glucose

60%

5%

95%

Glucose

Fatty Acid

Diabetic Cardiomyopathy:A Case Study in Integrated “Omics”

Diabetic cardiomyopathy is a metabolic myopathy whose sequelae result from an imbalance in substrate utilization.

Diabetic Cardiomyopathy

PhospholipaseActivation

MembraneDysfunction

MetabolicMyopathy

Fatty Acid (FA) Utilization Glucose Uptake

Shotgun Lipidomics

Shotgun Lipidomics is Comprised of Four Components:

1. Multiplexed Extractions and Chemistries

2. Direct Infusion and Intrasource Separation

3. Multidimensional Mass Spectrometry

4. Array Analysis and Bioinformatics

Multidimensional Mass Spectrometry of Control and Diabetic Myocardium

Biochemistry 44:16684-16694, 2005

Dramatic Alterations in the Myocardial Cardiolipin Profile of Streptozotocin-induced Diabetic Mice

*

*

Dramatic Alterations in the Myocardial Cardiolipin Profile of Ob/Ob Mice at 4 Months of Age

** *

** ****

** ***** *

**

** **** *

*** ** ** * *

*

***************

• Essential for Complex IV function.

• Adapts HII phase promoting mitochondrial fusion and fission.

• Binds to cytochrome C whose release triggers apoptosis.

• Facilitates transduction of proton gradient into electrical potential.

Cardiolipin

CO

O R

CH2

CH

CH2

CO

O R

O

1

2

CH2

CH2

CHOH CO

O RCH

P OO

O-

CH2O P OO

O-

CO

O RCH2 3

4

ll

ll

ll

ll

ll

ll

Barth Syndrome has been localized to Xq28 which encodes tafazzin and associated with a gene responsible for cardiolipin transacylase activity.

1. Cardiomyopathy2. Skeletal Muscle Myopathy3. Neutropenia4. Cardiolipin Depletion

Barth Syndrome

Xp2

2.32

Xp2

2.2

Xp2

2.12

Xp2

1.3

Xp2

1.1

Xp1

1.3

Xp1

1.22

Xq1

2X

q13.

2X

q21.

1X

q21.

31X

q21.

33X

q22.

2X

q23

Xq2

5X

q26.

2X

q27.

1X

q27.

3

Ari CedarsMeng ChenHua ChengMike CreerXiaoling FangDavid FordBeverly GibsonPaul GlaserShao Ping GuanRose Gubitosi-KlugXianlin HanStan HazenChristopher JenkinsHui JiangJohn KelleyMichael Kiebish

Kelly KruszkaDavid MancusoRebecca MillerSung Ho MoonHarold SimsJackie SniderBob StuppyXiong SuGang SunMatt WolfRobert WolfWei YanJingyue YangKui YangYouchun ZengZhangdan ZhaoLori Zupan

Dana AbendscheinNada AbumradPerry BickelIgor EfimovXianlin HanGuenter HaemmerleDan KellyErin KershawSergey KorolevCraig MalloyTony MuslinLinda PikeJeff SaffitzPaul SchlesingerJohn TurkRudolf ZechnerCharles Zorumski

Current and Past MembersDivision of Bioorganic Chemistry: Collaborators:

AcknowledgementsAcknowledgements

Sponsored by:








Integrated Biology: What is needed to bring the Omics together?

Albert J. Fornace Jr. Professor, Dept. of Biochemistry and Molecular &

Cellular Biology Lombardi Comprehensive Cancer Center

Georgetown University

Gene-Protein-Reactions (GPRs) and Data Integration

Hyduke & Palsson

What is Needed to Bring Omics Together?

• Data management– Clear indication of the source and context of the data– Meaningful identifiers (everybody’s proud of their clever system that nobody else uses)

– Accessible data sources

• Models / Methods to interpret the data– An honest assessment of the benefits and limits of various modeling

approaches– A realistic assessment of the near-term capabilities of current

modeling approaches.

• The ability to understand the limits of the data and models– Complexity of mammalian systems– "As the complexity of the variable increases, it becomes more

important to have a solid model of what you think you can predict and to then test it explicitly, rather than less important as the machine learning enthusiasts would have it." Michael Bittner, TGen

Potential integromics approaches in complex systems

• “Holistic” approach– where a comprehensive list of parameters are assembled from a

sufficiently robust dataset– NCI60 example– application to toxicogenomics and subsequent extension to

additional omics levels

• Genetic approach– p53 signaling at multiple omics levels– Analysis of the role of “ATM” through “omics” analysis

• Cheema, Timofeeva, Varghese, Jung, Ressom, & Dritschilo, AACR, 2010

– Stress responses at the metabolomics level

Schematic overview of the NCI-60 databases

Weinstein, Mol Cancer Ther 5, 2601, 2006.

Conceptual schema for molecular profiling of the NCI60 cancer cell lines

Weinstein, Mol Cancer Ther 5, 2601, 2006

Drug-Target Clustered HeatmapIntegrates:

• Gene expressionmRNAmiRNA

• Protein expression• SNPs• Stress responses• Pharmacology

Science 275:43, 1997Comptes Rendus Biol. 326:909,

2003

J.N. Weinstein, MD Anderson

Integromics

Examples from stress signaling studies

• NCI60 lines response to a signal stress signal– radiation signaling

• Multiple stresses– application in toxicogenomics

Heterogeneity of gene expression responses to ionizing radiation in NCI60 cell line panel

transcripts

cell

lines

Amundson et al., Cancer Res 68, 415, 2008

p53 mutant cell lines p53 wild-type

p53p53--dependent Induced Gene Clusterdependent Induced Gene Cluster

Genes

Genes shown in black are known to be p53-dependent

Genes shown in red fall within the same cluster are likelycandidates for p53 regulation.


Cell cyclegene cluster

Down-regulation by IR of some genes is very widespread and maps to common regulatory components

Cell Lines

Gen

es

Cytoscape interaction map

Involvement of the E2F4-RBL2 complex as a down-regulator


Genotoxic stress-response markers

Goodsaid et al, Nat Rev Drug Discov 9, 435, 2010

Acknowledgements

• Georgetown Henghong Li, Daniel Hyduke (UCSD), Tony Dritschilo

• MD Anderson John Weinstein

• Pfizer Jiri Aubrecht

• Columbia Sally Amundson

• TGen Mike Bittner, Jeff Trent

• NCI Frank Gonzalez

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ScienceWWebinar Seriesebinar Series slides_Omics webi… · 3. Getting Closer to the Metabolic Whole. Sauer, Heinemann & Zamboni 2007 Science 316: 550 . environment. phenotype •