Generic and specific constraints shaping adaptive gene

expression profiles in yeast

Ester Vilaprinyó, Rui Alves, Armindo Salvador, Albert Sorribas

Coimbra, 2007 Grup de Bioestadística i BiomatemàticaDep. Ciències Mèdiques BàsiquesUniversitat de Lleida

Introduction Environmental conditions change. Cells living

in those environments need to adapt to those changes in order to survive environmental stresses (heat shock, osmotic...).

Stress

To survive yeast changes its gene expression profile

This allows adaptation of fluxes and concentrations

Introduction

In principle different quantitative and qualitative gene expression profiles (GEP) could produce the same physiological adaptation

However, what has been observed is that GEPs are specific for each type of stress

Constraints to the changes in gene expression

Adaptation is multiobjective. Gene expression profiles (GEPs) must

induce expression of genes whose proteins are needed for the response SPECIFIC CONSTRAINTS

There may be constraints that are common to most stress conditions? GENERAL CONTRAINTS?

Goals

Can we identify general and specific constraints that shape an adaptive gene expression profile (GEP) of yeast under stress conditions?

If so, can we use them to characterize the quantitative changes (design principles) required for a given response?

Outline

Identification of a general type of constraints to GEP design

Identification of specific constraints for heat shock & Quantitative design of GEPs in heat shock response

What is common to all stress responses?

To adapt quickly cells need to synthesize proteins quickly and using as few resources as possible.

Globally, changes in gene expression correlate well with changes in protein levels.

Proteins are the most expensive of macromolecules.

Synthesis of new metabolites is expensive but stress specific.

Therefore a general selective pressure in stress response to adapt quickly and at low cost could shape the regulation of expression for the different genes in the GEP

How to save resources in protein synthesis?

H1: If proteins are abundant in the basal state, the cell is spending energy synthesizing them and keeping them at high level. Because their activity is already abundant, to save energy cells could inhibit expression of abundant proteins.

Basalprotein

abundance

Change ingene

expression after stress

Correlation

Abundant proteins are inhibited

Protein abundance/103

1/changefold

H2: Low abundance proteins have almost no total activity. To achieve larger relative increases in activity, cell could express proteins of low abundance

Basalprotein

abundance

Change ingene

expression after stress

Correlation

How to achieve a fast increase in activity?

Proteins of low abundance are overexpressed

Protein abundance/103

changefold

H3: If in addition to downregulation of abundant proteins, the cell downregulates genes that code for large proteins, it will save more energy.

Are there other ways to design GEP that use resources efficiently?

H4: Upregulation of genes that code for small proteins. This will produce new proteins quicker and at lower cost than if upregulated proteins where larger.

Protein size (MW or length)

Change ingene

expression after stress

Correlation

Are there other ways to design GEP that respond fast and use

resources efficiently?

Size matters in modulation of gene expression?

Protein size (MW)

changefold

Repressed genes

Overexpressed genes

Protein size (MW)

1/changefold

Size matters in modulation of gene expression

H3

H4

Resource usage and quickness of response general constraints

for adaptive GEP?

H1: To save energy cells should inhibit proteins that are abundant

H2: To achieve larger relative increases in activity, cell should express proteins of low abundance

H3: Downregulation of genes that code for large proteins.

H4: Upregulation of genes that code for small proteins.

Resource usage and quickness of response general constraints

for many adaptive GEP

The hypotheses are consistent with these selective pressures in the design of adaptive GEPs

Outline

Identification of a general constraint to GEP

Identification of specific constraints for heat shock & Quantitative design of GEPs in heat shock response

Heat shock response

Well characterized physiologically Previous work (Voit & Radivovevitch) Enough information to identify

contraints Enough information for mathematical

modelling of the relevant reactions

Metabolic network & physiological constraints

Glycogen Trehalose

NADPH

HIGH ENERGY DEMANDC1

STRUCTURAL INTEGRITY-Avoids aggregation of denatured proteins-Membrane -Acts in synergism with chaperonesC2

REDUCING POWERNew synthesis of sphingolipids in order to change the membrane fluidityC3

Curto, Sorribas, Cascante (1995) Math. Biosci. 130, 25-50 Voit, Radivovevitch (2000) Bioinformatics 16: 1023-1037

Glycogen Trehalose

Methodology

×5

×5

×5

5 ×

HXT GLK PFK TDH PYK TPS G6PDH

hip1 5 1 1 1 5 1 5

HXT GLK PFK TDH PYK TPS G6PDH

hip1 5 1 1 1 5 5 5hip2 3 3 3 3 3 3 3

HXT GLK PFK TDH PYK TPS G6PDH

hip1 5 1 1 1 5 5 5hip2 3 3 3 3 3 3 3

hip3 2 1 1 1 2 7 7

×2

×7

×2

7 ×

SIMULATIONS To explain why expression of particular genes is changed, we scanned the gene expression space and translated that procedure into different gene expression profiles (GEP)

Consider a set of possible values for each enzyme.Explore all possible combinations.Total: 4.637.360 hypothetical GEPs

GLK, TPS [ 1, 2.5, 4, ..., 14.5, 16, 17.5, 19]

HXT [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

G6PDH [1, 2, 3, 4, 5, 6, 7, 8]

PFK, TDH, PYK [ 0.25, 0.33, 0.5, 1, 2, 3, 4]

HXT

GLK PFK TDH PYKTPS G6PDH

hip1 5 1 1 1 5 5 5hip2 3 3 3 3 3 3 3hip3 2 1 1 1 2 7 7......

...

.........

...

.........

...

.........

...

...

...

.........

...

.........

...

.........

...

.........

hip4637360

×3

×3

×3

3 ×

×3

×3

×3

NADPH

Implementation of stress responses

Metabolic network

Mathematical model

Power Law form Biochemical System Theory

(Savageau, 1969)

16 25 2712 21

25 27 32 35 38 62 611 72 71521

83 84 85 812

32 35 38 43 45 49 414

43 45 49

.

1 1 2 6 2 1 5 7

.

2 2 1 5 7 3 2 5 8 6 2 11 7 2 15

.8 3 4 5 12

3 3 2 5 8 4 3 5 9 14

.

4 4 3 5 9 14

2

2

f f ff f

f f f f f f f f ff

f f f ff f f f f f f

f f f

X X X X X X

X X X X X X X X X X X

X X X XX X X X X X X X

X X X X X

53 54 55 510414

43 45 49 53 54 55 510 25 27 32 35 38 62 611 95 913414 21

5 3 4 5 10

.

5 4 3 5 9 14 5 3 4 5 10 2 1 5 7 3 2 5 8 6 2 11 9 5 132

f f f ff

f f f f f f f f f f f f f f f ff f

X X X X

X X X X X X X X X X X X X X X X X X X

Each GEP has associated a new steady state→ functional changes → HS index of performance

Reproduce basal conditions (25ºC)

Generalised Mass Action

Gene expression changes

Evaluate HS performance

Criteria of performance

C1- Synthesis of ATP C2- Synthesis of trehalose C3- Synthesis of NADPH

“Well-known” and studied by experimentalist

C1-C3 Production of trehalose, ATP, and NADPH

If we only consider the criteria concerning an increase of fluxes selects a wide set of possible GEPs (27.8 %, 1.290.454)

The enzymes involved directly in the synthesis should be over-expressed.

In many cases, flux increase involve large metabolite accumulation, which is an undesirable situation in terms of appropriate response

■ % of the change-folds before any selection ■ % of the change-folds after selecting by C1-C3

Fold change in gene expression

% o

f to

tal G

EP

s

HXT: Hexose transporters

GLK: Glucokinase

PFK: Phosphofructokinase

TDH: Glyceraldhyde 3P dehydrogenase

PYK: Pyruvate kinase

TPS: Trehalose phosphate syntase

G6PDH: Glucose-6-P dehydrogenase

Criteria of performance

C4- Accumulation of intermediates: High fluxes with high metabolite concentrations are considered a sub-optimal adaptation Reactivity Cell solubility Metabolic waste

C5- Cost of changing gene expression: GEPs that allow adaptation with minimal changes in gene expression are favoured Adaptation should be economic Minimize protein burden

“Well-known” and studied by experimentalist

Well-studied within a system biology perspective

C1- Synthesis of ATP C2- Synthesis of trehalose C3- Synthesis of NADPH

abs ln mRNA change foldCost cost

50 %

No experimental measures are available, so we have chosen as a threshold the value that includes de 50% of all the cases

Criteria of performance

C1- Synthesis of ATP C2- Synthesis of trehalose C3- Synthesis of NADPH

C4- Accumulation of intermediates C5- Cost of changing gene expression

C6- Glycerol production C7- TPS and PFK over-expression C8- F16P levels should be maintained

“Well-known” and studied by experimentalist

Well-studied within a system biology perspective

C6- Glycerol production

Glycerol production helps in producing NADPH from NADH

New synthesis of glycerolipids required

Genes are over-expressed

Glicerol rate

50%Selecting GEPs with the highest glycerol production is synonymous of selecting GEPs with low PYK over-expression

C7- TPS and PFK

TPS is directly related with vtrehalose PFK is inversely related with vtrehalose If PFK is over-expressed, then TPS

should also be over-expressed, which compromises C5 (cost)

Sensitivity analysis shows that the system is highly sensible to change PFK

/ trehaloseTPS PFK v

50%

Glycogen Trehalose

F16P is required for glycerol synthesis

F16P feed-forward effect to the lower part of the glycolysis PYK velocity is increased in vitro by as much as

20 by F16P and hexose phosphates in their physiological concentration ranges

This enzyme modulation facilitates the flow of material and avoids accumulation of intermediates

C8- F16P levels should be maintained

Results based on all previous criteria

Values for Criteria Percentage of GEPs selected

using each criteria

Absolute values Ratio to basal

values Individual Accumulated

C1 VATPa > 180.6 3 45.13e

C2 VTREa > 0.03 25 60.95e

C3 VNADPHa > 3.54 2 85.86e 27.83

C4 GLCb < 0.04 1.2 86.40f G6Pb < 20.22 20 76.04f F16Pb < 22.86 2.5 51.91f PEPb < 0.01 1.2 65.44f ATPb < 6.77 6 89.32f 2.40

C5 Costc < 12.06 12.06 50 0.59 C6 VGlycerol

a > 0.39 0.22 50 0.25 C7 d < 28.10 0.391 50 0.16 C8 F16Pb > 8.64 0.95 61.93 0.06

C1

C2C3 C4

C5

C6C7

C8

Selected profiles

HXT: Hexose transporters

GLK: Glucokinase

PFK: Phosphofructokinase

TDH: Glyceraldhyde 3P dehydrogenase

PYK: Piruvate kinase

TPS: Trehalose phosphate syntase

G6PDH: Glucose-6-P dehydrogenase

■ % of the change-folds before any selection ■ % of the change-folds after selecting by ALL criteria

Fold change in gene expression

% o

f to

tal G

EP

s

Fulfill all criteria of HS performance:• SIMULATION: 0.06% of GEPs (4238 ) • All experimental databases

Eisen et al. at 10 min (BD1 10’) Causton et al. at 15’ (BD2 15’) Gasch et al. at 10’ (DB3 10’) Gasch et al. at 15’ (DB3 15’) Gasch et al. at 20’ (DB3 20’)

Are the eight criteria of performance specific for heat

shock?

We analyzed 294 GEPs from microarray experiments under different environmental conditions

C1 C2 C3 C4 C5 C6 C7 C8

Alkali H202 Diamide

...

HeatShock

Only heat shock conditions are selected

What happens under other conditions? (Principal Component Analysis)

Stationary

HeatS

H2O2

Diamide

Stationary

HeatS

H2O2

Diamide

Sporulation

factor1 factor2 factor3 factor4

factor1

factor2

factor3

factor4

factor2

factor1

factor3

Summary

Identification of general constraints in GEP

Identification of a set of constraints that are specific for heat shock

Identification of the quantitative design of the heat shock GEP

Support by experimental evidence Specificity of the set of constraints

Acknowledgments

FCT

Ramon y Cajal Program MCyT

FUP program MCyT

What next?

Dynamic patterns Define performance criteria based on dynamics Obtain precise measurements of the dynamic

gene expression changes Consider additional metabolic processes Measure in situ levels of metabolites and

fluxes Evaluate the energy and redox status of

the cell Seek for specific constrains that explain

differences and shared behaviors with other stress responses

Interpretation

Vilaprinyo, Alves, Sorribas (2006) BMC Bioinformatics 7(1):184

To generate an appropriate HS response some enzymes seems to have a restricted range of allowable variation. High sensitivity towards these enzymes can explain this result Enzymes (genes) that show no changes may be very important to

understand adaptive responses Fine tuning of fluxes and metabolite levels should be achieved

through coordinated changes in several enzyme levels. The experimental GEPs are situated within the predicted

ranges Our analysis helps identifying the more appropriate GEPs. Also,

we can explain why most of the hypothetical GEPs are inappropriate for HS response.

The considered criteria can be seen as constrains for heat shock performance

Eisen et al. PNAS. 1998 Dec 8;95(25):14863-8. DB1

http://genome-www.stanford.edu/clustering

Causton et al. Mol Biol Cell. 2001 Feb;12(2):323-37 DB2 http://web.wi.mit.edu/young/environment

Gasch et al. Mol Biol Cell. 2000 Dec;11(12):4241-57 DB3 http://WW-genome.stanford.edu/yeast_stress

Conceptual model

Mathematical model

Reproduce basal conditions 25ºC

Calculate new steady states (37º)

SIMULATION OF GEPs

Select which fulfill the criteria of performance

CASE 1CASE 2………

etc

MICROARRAY (3DB)

Conceptual model

Mathematical model

Reproduce basal conditions 25ºC

Calculate new steady states (37º)

SIMULATION OF GEPs

Select which fulfill the criteria of performance

CASE 1CASE 2………

etc

MICROARRAY (3DB)

Define Heat Shock performance

SIMULATIONS

4.637.360 hypothetical gene expression profiles (GEPs)

C4d96.%C4e99.%C5100.%C651.%C77.8%C875.%C12.7%C25.8%C317.%C4a76.%C4b99.%C4c94.%

Selected NonSelected

Heat Heat

Heat

Heat

Diamide

Diamide

AlkaliststYPD

ststYPDspo

Diauxic

DTTDiamide

NaCl

Sorbitol

ststYPD

mutNdepl

CsourceCold Alkali

AAstarv

H2O2

C1 C2

C7 C3

Heat Heat

Heat

Heat

Diamide

Diamide

AlkaliststYPD

ststYPDspo

Diauxic

DTTDiamide

NaCl

Sorbitol

ststYPD

mutNdepl

CsourceCold Alkali

AAstarv

H2O2

C1 C2

C7 C3

Validation of the model prediction by comparison to microarray data

1.540.91-0.04-1.06-1.47160

1.10.61-0.2-0.97-1.3280

1.550.82-0.2-1.43-2.0640

2.180.87-0.1-1.89-2.6420

2.961.23-0.2-1.56-2.5610

1.120.52-0.1-0.56-0.760

0.990.950.50.050.01

QuantilesMinute

DB1

1.540.91-0.04-1.06-1.47160

1.10.61-0.2-0.97-1.3280

1.550.82-0.2-1.43-2.0640

2.180.87-0.1-1.89-2.6420

2.961.23-0.2-1.56-2.5610

1.120.52-0.1-0.56-0.760

0.990.950.50.050.01

QuantilesMinute

DB1

2.481.720.58-0.46-1.05120

2.431.740.55-0.59-1.260

2.411.820.71-0.35-0.9145

2.791.950.7-0.39-0.9830

3.011.850.39-1.02-1.7215

0.650.510-0.81-1.210

0.990.950.50.050.01

QuantilesMinute

DB2

2.481.720.58-0.46-1.05120

2.431.740.55-0.59-1.260

2.411.820.71-0.35-0.9145

2.791.950.7-0.39-0.9830

3.011.850.39-1.02-1.7215

0.650.510-0.81-1.210

0.990.950.50.050.01

QuantilesMinute

DB2

1.680.84-0.04-0.86-1.3680

1.70.83-0.04-0.89-1.3460

2.611.34-0.1-1.6-2.2540

3.611.9-0.1-2.06-3.1830

3.791.99-0.1-2.32-3.8420

4.022.1-0.1-2.4-4.3215

3.431.63-0.3-2.32-3.6910

3.011.2-0.2-1.18-2.065

1.290.960.03-1.18-2.470

0.990.950.50.050.01

QuantilesMinute

DB3

1.680.84-0.04-0.86-1.3680

1.70.83-0.04-0.89-1.3460

2.611.34-0.1-1.6-2.2540

3.611.9-0.1-2.06-3.1830

3.791.99-0.1-2.32-3.8420

4.022.1-0.1-2.4-4.3215

3.431.63-0.3-2.32-3.6910

3.011.2-0.2-1.18-2.065

1.290.960.03-1.18-2.470

0.990.950.50.050.01

QuantilesMinute

DB3

Noise of databases is derived from the values of change expression at basal conditions (minute 0)

Log2 values

A statistical analysis shows that the results are with the allowable error

All microarray gene expression profiles fulfill criteria of performance