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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
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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
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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
Fendt, Büscher, Rudroff, Picotti, Zamboni & Sauer 2010 Molecular Systems BiologyFendt, Büscher, Rudroff, Picotti, Zamboni & Sauer 2010 Molecular Systems Biology
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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
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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
Fendt, Büscher, Rudroff, Picotti, Zamboni & Sauer 2010 Molecular Systems BiologyFendt, Büscher, Rudroff, Picotti, Zamboni & Sauer 2010 Molecular Systems Biology
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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
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Jörg Büscher – PhD Defense
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Jörg Büscher – PhD Defense
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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
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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:
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
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:
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
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.
Amundson et al., Cancer Res 68, 415, 2008
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
Amundson et al., Cancer Res 68, 415, 2008
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
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
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Sponsored by:
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
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