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Network-based data integration reveals extensive post-transcriptional regulation of human tissue-specific metabolism PowerPoint PPT Presentation


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Network-based data integration reveals extensive post-transcriptional regulation of human tissue-specific metabolism Tomer Shlomi * , Moran Cabili * , Markus J. Herrgard, Bernhard Q Palsson and Eytan Ruppin * These authors contributed equally to this work. Metabolism.

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Network-based data integration reveals extensive post-transcriptional regulation of human tissue-specific metabolism

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Network-based data integration reveals extensive post-transcriptional regulation of human tissue-specific metabolism

Tomer Shlomi*, Moran Cabili*, Markus J. Herrgard,

Bernhard Q Palsson and Eytan Ruppin

* These authors contributed equally to this work


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Metabolism

Metabolism is the totality of all the chemical reactions that operate in a living organism.

Catabolic reactions

Breakdown and produce energy

Anabolic reactions

Use energy and build up essential cell components


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Why Study Human Metabolism?

  • In born errors of metabolism cause acute symptoms and even death on early age

  • Metabolic diseases (obesity, diabetics) are major sources of morbidity and mortality.

  • Metabolic enzymes and their regulators gradually becoming viable drug targets


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Metabolite

Reaction catalyzed

by an enzyme

Modeling Cellular MetabolismA Short Review

Metabolic flux :

The production or elimination of a quantity of metabolite per mass of organ or organism over a specific time frame

“..it is the concept of metabolic flux that is crucial in the translation of genotype and environmental factors into phenotype or a threshold for disease.”

Brendan Lee Nature 2006


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constant

Constraint Based Modeling

Find a steady-state flux distribution through all

biochemical reactions

  • Under the constraints:

    • Mass balance: metabolite production and consumption rates are equal

    • Thermodynamic: irreversibility of reactions

    • Enzymatic capacity: bounds on enzyme rates

  • Successfully predicts:


Constraint based modeling cbm mathematical representation of constrains l.jpg

Glucokinase

Glucose -1

ATP -1

G-6-P +1

ADP +1

Mass balance

S·v = 0

Subspace of R

Thermodynamic & capacity

10 >vi > 0

Bounded convex cone

Optimization

Maximize Vgrowth

n

growth

Constraint Based Modeling (CBM)Mathematical Representation of Constrains

  • Stoichiometric matrix – network topology with stoichiometry of biochemical reactions

reactions

Glucose + ATP

Glucokinase

Glucose-6-Phosphate + ADP

metabolites

Fell, et al (1986),Varma and Palsson (1993)


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Human Metabolic Models

  • Motivated by the fact that in-vivo studies of tissue-specific

    metabolic functions are limited in scope

  • Individual genes and pathways (KEGG, HumanCyc)

    • Detailed description of the genes, reactions, enzymes

    • No connections between pathways

  • Specific cell-types and organelles

    • Red blood cell Wiback et al. 2002

    • Mitochondria Vo et al. 2004

  • Large-Scale Human Metabolic Networks

    • The first large-scale model of human metabolism ~2000 genes, ~3700 reactions, 7 organelles (Duarte et al. 2007, Ma et al. 2007)


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Can we use constraint-based modeling to systematically predict tissue-specific metabolic behavior?

CBM in HumanModeling human tissue function is problematic

  • Various cell-types activate different pathways (shown in Expression studies)

  • Hard to formulate cellular metabolic objectives – (like biomass maximization for microbial species)

  • Unknown inputs and outputs of each cell-type


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Our Objective :

  • General approach to study tissue specific metabolic models

    2. Tissue specific activity of metabolic genes/reactions

Our Method :

Model Integration with Tissue-Specific Gene and Protein Expression Data

Motivated by the assertion that highly expressed genes in a certain tissue are likely to be active there


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Our Method

1

Gene expression data

Protein measurements data

Gene-to-reaction mapping

Highly and Lowly expressed gene sets

2

Human Metabolic Model

(Duarte et. al)

Highly and Lowly expressed reaction sets

3

New objective function:

Maximize consistency with expression data.

Use Mixed Integer Linear Programming (MILP)

4

Determine activity state and conf. level for each gene/reaction


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Our Method

Determine Highly and Lowly Reaction sets

1. Genes set :Extract set of enzymes whose expression is significantly increased or decreased (GeneNote, HPRD)

2. Reactions set :Employ a detailed gene-to-reaction mapping to identify a tissue-specific expression state for each reaction

R1 = (g1 & g2) | g3 | g4


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Our Method

1

Gene expression data

Protein measurements data

Gene-to-reaction mapping

Highly and Lowly expressed gene sets

2

Human Metabolic Model

(Duarte et. al)

Highly and Lowly expressed reaction sets

3

New objective function:

Maximize consistency with expression data.

Use Mixed Integer Linear Programming (MILP)

4

Determine activity state and conf. level for each gene/reaction


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Our Method

Represent Flux Consistency with Expression State

Highly expressed

Output

M3

M7

E2

E1

H2

Output

M4

M8

H1

E3

M1

M5

Input

L2

E4

L1

M2

M6

H3

M9

E6

E5

E7

Lowly expressed

Looking for real flux vector V

Now add additional Boolean vectors H, L s.t :

Hi=1  Vi != 0 (if the enzyme associated with Vi is Highly expressed)

L i=1  Vi=0 (if the enzyme associated with Vi is Lowly expressed)


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Our Method

Define a New Objective function

Highly expressed

Output

M3

M7

E2

E1

H2

Output

M4

M8

H1

E3

M1

M5

Input

L2

E4

L1

M2

M6

H3

M9

E6

E5

E7

Lowly expressed

4 out of 5 reactions were consistent with the expression state!

Use Mixed Integer Linear Programming. Define a new objective function:

MAX Σ (Hi + Li )

Which practically mean maximize the number of Highly expressed reactions that are active and the number of Lowly expressedreactions that are inactive

Maximize consistency with expression data


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Our Method

1

Gene expression data

Protein measurements data

Gene-to-reaction mapping

Highly and Lowly expressed gene sets

2

Human Metabolic Model

(Duarte et. al)

Highly and Lowly expressed reaction sets

3

New objective function:

Maximize consistency with expression data.

Use Mixed Integer Linear Programming (MILP)

4

Determine activity state and conf. level for each gene/reaction


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Highly expressed

M3

M7

E2

E1

M4

M8

E3

M1

M5

E4

M2

M6

M9

E6

E5

E7

Lowly expressed

Our MethodFlux Activity State

  • Gene’s flux activity states-reflect the absence/existence of non-zero flux through the enzymatic reactions they encode

  • Comparison of the flux activity statesand theexpression state will teach us on post transcription regulation

Up regulated

Down regulated


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Flux Activity State Consider Space of Possible Solutions

  • We predict for each tissue active and inactive gene and reactions sets

  • Since there is a space of possible solutions to the MILP problem we solve a set of MILP problems to determine the gene activity

    • Simulate a state where the gene is inactive

    • Simulate an active gene product

Estimate confidence levels based on the drop in the consistency (with expression) between the 2 different solutions!


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ResultsGene Tissue Specific Activity

  • We employed the method described above on

    • metabolic network model of Duarte et al.

    • gene and protein expression measurements from

      GeneNote and HPRD

  • 10 tissues :

    brain, heart, kidney, liver, lung, pancreas, prostate, spleen, skeletal muscle and thymus.

  • The activity state of 781 out of 1475 model genes was determined in at least one tissue


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Post-transcriptional Regulation of Metabolic Genes

  • Post-transcriptional regulation plays a major role in shaping tissue-specific metabolic behavior: ~20% of the metabolic genes per tissue

  • average of 42 (3.6%) genes post-transcriptionallyup-regulated and 180 (15.4%) post-transcriptionallydown-regulated in each tissue

down-regulated

up-regulated


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Cross Validation Test

We performed a five-fold cross validation test

80% of the genes were used to constrain the model

Gene activity states for a held-out set of 20% of the genes were predicted according to the expression constrains of the remaining other 80%

The overlapbetween the genes predicted as active and the highly expressed genes in the held-out data was significantly high for all tissues


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Large Scale ValidationLarge-Scale Mining of Tissue-Specificity Data

  • Tissue-specificity of genes, reactions, and metabolites is significantly correlated with all data sources

  • Tissue specificity of post-transcriptional up regulated elements is significantly high !!!!

  • Tissue specificity of post-transcriptional down regulated elements is significantly low !!!!


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Tissue-Specific Metabolite Exchange with Biofluids

249 metabolites are known to be secreted or taken up by human tissues

54% of the metabolites are not associated with transporters and cannot be predicted by expression data

Transport direction can not be inferred by the expression data

A transporter might carry several metabolites

Many of the known transporters are post-transcriptionall regulated


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Metabolic Disease-Causing Genes

  • 162 metabolic genes are associated with a mendelian disease

  • Prediction accuracy: precision of 49% and a recall of 22%

  • There is a significant affect of post transcriptional regulation on disease-causing genes

GBE1 causes the glycogen storage disease ispost-transcriptionally up-regulated in liver, heart, skeletal muscle, and brain)


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SummaryMethodological Standpoint

  • First constraint-based modeling analysis of recently published human metabolic networks

  • First to account for post-transcriptional regulation within the computational framework of large-scale metabolic modeling

  • Integrate expression data as part of the optimization instead of imposing it as a constrain during the preprocessing step (Akesson et al. 2004)


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SummaryMain Conclusions

  • Post transcriptional regulation plays a significant rule in shaping tissue specific metabolic behavior

  • The tissue specificity of many metabolic disease-causing genes goes markedly beyond that manifested in their expression level, giving rise to new predictions concerning their involvement in different tissues

  • Metabolites exchange with biofluids displays a large variance across tissues, composing a unique view of tissue-specific uptake and secretion of hundreds of metabolites


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What’s Next?

  • Integrate other tissue-specificity data

  • Modeling of metabolic diseases

    • Using various data sources (known disease-causing genes, drug databases)

    • Predict tissue-wide metabolic symptoms

    • Predict metabolic response to drugs

  • Predict disease biomarkers that can be identified by biofluid metabolomics


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Thank you!


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Mathematical representation of our optimization problem


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